# Thomas Anglero - Full Content > Strategic AI Advisor to Executive Leaders and Boards - **Last Updated**: September 2026 --- ## AI Leadership: The Difference Between Assuming and Checking URL: https://anglero.com/2026/09/04/ai-leadership-the-difference-between-assuming-and-checking/ Published: 2026-09-04 Klarna’s CEO thought he knew his customer service culture well enough to replace 700 people with it. He was wrong, and it took him eighteen months and a public reversal to find out. In February 2024, Klarna announced its OpenAI-built assistant was doing the work of 700 full-time agents, handling 2.3 million conversations and cutting resolution time from 11 minutes to under 2. The workforce fell from 5,500 to 3,400. Hiring froze. CEO Sebastian Siemiatkowski said publicly that AI could already do the jobs humans do. By 2025, he was rehiring. On Bloomberg, he put it plainly: “We focused too much on efficiency and cost. The result was lower quality, and that’s not sustainable.” Why did the AI-first strategy fail? Because Siemiatkowski built his strategy on the culture he assumed his company had, an efficient, replaceable transaction layer, rather than the one it actually had: a customer relationship carrying real complexity, dispute, hardship, nuance, that no dashboard was measuring until it was already gone. He put the gates around a zoo he had never actually walked through. The animals he thought he was containing were not the ones causing the problem, and by the time the real ones showed up, in complaint volumes and falling satisfaction scores, the cost was already public. Klarna is not an outlier. Gartner surveyed 321 customer service leaders and found only 20% had actually reduced staffing because of AI, and it predicts that by 2027, half of the companies that cut staff for AI will hire them back, frequently under new job titles. The pattern is common enough to have a shape: assume, act at scale, discover the assumption was wrong once the numbers force the admission. What does the alternative look like? I have watched this play out from the inside too, running a company where my own engineering team is AI agents. The temptation to assume competence rather than verify it is constant, and it is never the AI that catches the gap first. It is always a person who actually looked. I once watched a leader do the opposite on his very first days in the role. He had just taken over a large business unit in New York, and instead of sitting behind a desk on day one, he spent his first two days travelling to every satellite office, meeting the people who actually ran the operation. He was not checking a box. He wanted to know who was actually in the building before he decided what the building needed. Leader who assumes Leader who checks First move in the role Acts on the org chart, or on what worked somewhere else Goes to see what is actually happening, in person, before deciding anything When the strategy is wrong Finds out publicly, at scale, after the damage is visible in the numbers Finds out early, quietly, before it costs anything to fix What the culture becomes Whatever the assumption already was, reinforced Whatever is actually true, worked with directly Neither leader was foolish. One skipped a step the other did not. The gap between them was not intelligence or intent, it was whether they checked what was actually there before they built on top of it. Before you scale any AI decision, ask the harder question first: are you building for the culture you have, or the one you assumed? --- ## Trust Is the Whole Problem URL: https://anglero.com/2026/09/02/trust-is-the-whole-problem/ Published: 2026-09-02 Strip away the credentials, the case studies, and the fee structure, and every AI advisory relationship comes down to one question a CEO has to answer for themselves: how do you trust a stranger? What is a CEO actually looking for when they hire outside AI help? Someone with no political interest in the outcome. No equity, no stock-option stake, no bias toward the CEO or the company they are advising. Someone who will give the frank, direct, respectful truth regardless of how it lands, because they have nothing inside the company left to protect. Why is trusting a stranger so hard for a CEO specifically? Because most CEOs default to an unhealthy, ego-based trust model without realising it: what can I get from this person that makes me look good, rather than am I trusting their competence or their honesty on its own terms. That instinct works reasonably well when choosing colleagues you already know. It works badly when the whole point of the relationship is bringing in someone who owes you nothing. What actually determines whether a CEO hires someone? In order, when I have pushed leaders to be honest about it: is this person informed enough, are they intelligent, do they have the experience, and are they recommended. But ranked above all four, conceptually, is the real number one: can I sit down and have lunch with this person, or is this just another sales pitch. Cost is real, but it usually sits around fourth or fifth in practice. If someone is genuinely good, the leader finds the budget. See Strategic AI Advisor Vetting for the specific questions worth asking before you hire. Why do large firms win this trust question so easily, and strangers struggle with it? Big firms buy trust through money and reputation, built up over years of visible client relationships. A stranger has neither of those. It matters because the market is moving fast underneath this question: 76% of organisations report having a Chief AI Officer in 2026, up from just 26% a year earlier, according to IBM’s 2026 CEO Study. That is a huge number of companies making a trust decision about AI leadership in a single year, most of them without an established playbook for how to make it well. That is the entire problem every post in this series has been answering from a different angle: why the internal team can’t fill the role, why AI models are becoming part of how advisors get found, why the room itself kills good advice before it’s heard, and why paying the same firm that already failed you doesn’t fix anything. What actually replaces the old trust shortcuts? Increasingly, a different kind of search. Friends still narrow a leader’s options to their own language and culture, the way they always have. But AI can now hand a leader a list of people beyond that circle. I believe that is becoming the new way leaders find answers to questions like this one, alongside the old way, not instead of it. See Strategic AI Advisor Referrals for how that is already changing who gets found. If you are trying to work out whether you can trust the stranger in front of you, that question is worth more of your time than the fee. It is also the first conversation I have with every new client. Work with Thomas. Questions this article answers What does a CEO actually need from an outside AI advisor? Someone with no political interest in the outcome, no equity or stake in the company, who can give the honest answer regardless of how it lands, because they have nothing inside the company to protect. Why do CEOs struggle to trust an outside advisor? Most default to an ego-based trust model, asking what they can get from the relationship that makes them look good, rather than judging competence or honesty on their own terms. What actually decides who a CEO hires? Being informed, intelligent, and experienced all matter, and recommendations help, but the real deciding factor is simpler: can the CEO sit down and have an honest conversation with this person, or does it feel like a sales pitch. Why do large consulting firms have an advantage in this trust question? They buy trust through money and visible reputation built over years. An unfamiliar advisor has neither, which is the exact gap every post in this series addresses from a different angle. Thomas Anglero is a Strategic AI Advisor (MerkabaPhi AS, Oslo), with 450+ keynotes across 30+ countries. Enquiries: anglero.com. --- ## Why Are You Paying the Firm That Already Failed You URL: https://anglero.com/2026/09/01/paying-the-firm-that-already-failed-you/ Published: 2026-09-01 A leader inside a large, well-known company recently asked a Big Four firm to evaluate their AI readiness. I am withholding the company and the names, because the pattern matters more than the specifics, and I have seen this exact pattern more than once. What was the actual problem the AI evaluation was supposed to solve? On paper, an AI strategy gap. In practice, something much harder: a leadership group that was dysfunctional and siloed, each member protecting their own business unit, publicly cordial and privately hostile to one another. Any AI proposal coming from this particular leader was going to be rejected before it was even heard, because the room had bigger unresolved political problems than AI, even though AI could genuinely have helped address some of them. This is not an unusual pattern: in RAND Corporation’s study of why AI projects fail, 84% of the AI practitioners interviewed cited leadership-driven decisions, not the technology itself, as the primary reason AI initiatives fail. What did the consulting firm actually recommend? To their credit, the honest answer: run a parallel change-transformation project alongside the AI project, because if the two are not synchronised, neither one works. That is correct advice. It is also advice from a firm that has already been inside this company, already knows this dynamic, and has had every opportunity to raise it before being asked to evaluate AI readiness specifically. So why hire the same firm again? This is the question worth sitting with. Only 12% of CEOs report that AI has delivered both cost savings and revenue benefits, according to PwC’s 29th Global CEO Survey, and most of that 88% are being advised by somebody. If you are going to your big, expensive consulting company for an AI strategy, that consulting company has already failed you. They are already inside the business. They already know it. If a working strategy has not already been delivered, or if the company is already losing ground to competitors on AI, the firm being paid to keep you ahead has already failed at the one job it was hired to do. Why are you paying somebody who has already failed you to fail you more? I go into the structural reasons this keeps happening in Strategic AI Advisor vs Big Four Consulting. What should replace that relationship? Not necessarily another large firm carrying the same incentive to protect an existing engagement. Someone whose mandate is defined and finite, with no multi-year relationship to defend, has a different reason to tell you the truth the first time. If your organisation is still paying the firm that has not fixed this yet, that is exactly the conversation I have with a limited number of senior leaders each quarter. Work with Thomas. Questions this article answers Why did an AI evaluation actually fail at this company? Because the real problem was not AI readiness. It was a dysfunctional, siloed leadership group whose political problems were bigger than the AI question, and any AI proposal was rejected before being heard. Was the consulting firm’s advice wrong? No, the advice itself (synchronise a change-transformation project with the AI project) was correct. The problem is that the firm giving it had already been inside the company long enough to know the dynamic and had not raised it earlier. Why not just hire the same trusted firm again? Because if a working strategy has not already been delivered, or the company is already losing ground to competitors, the firm being paid to prevent that has already failed at that job. What should replace an underperforming incumbent relationship? Someone with a defined, finite mandate and no existing engagement to protect, which changes the incentive to tell the truth the first time. Thomas Anglero is a Strategic AI Advisor (MerkabaPhi AS, Oslo), with 450+ keynotes across 30+ countries. Enquiries: anglero.com. --- ## Your Meeting Format Is Failing Before Anyone Speaks URL: https://anglero.com/2026/08/31/meeting-format-failing-before-anyone-speaks/ Published: 2026-08-31 When a young, technically sharp person presents AI strategy to a CEO, the meeting usually fails before either of them says anything true. I have watched this happen enough times to recognise the pattern immediately, and it has almost nothing to do with the content of the presentation. What actually goes wrong when a sharp junior presenter meets a senior CEO? Two failure modes, and I see both regularly. The first is quiet checkout: the CEO gets lost in the jargon, is too proud to admit it, and smiles through the rest of the meeting while mentally disengaging. The second is aggression: the CEO hunts for the first gap in the presenter’s company-wide picture, a gap that exists only because the presenter was never given the full data to close it, and uses that single gap to write off everything else they said. It is the same instinct a conference audience uses to look for a reason to discredit a keynote speaker rather than sit with an uncomfortable idea. Is this really a trust problem? No, and this is the reframe that matters. It looks like a trust problem, but it is a format problem. Only 44% of CIOs are considered “AI-savvy” by their own CEOs, according to Gartner’s CEO Survey, and a room full of people who do not feel fluent will default to a format that avoids exposing it. These meetings run on a fixed agenda with an exact time slot, usually fifteen minutes, done whether the conversation is finished or not. Fifteen minutes is enough time for a status update. It is not enough time for a CEO to admit confusion, ask a genuinely basic question, or let a complex idea land without defending against it. What actually fixes it? Move the conversation to lunch. An hour, no agenda pressure, nobody watching the clock or being rushed to the next slide. Of every format I have sat through, the best outcomes I have seen have all come from the lunch conversation, and none have come from the fifteen-minute slot. It costs nothing, and it is the single easiest change available to any board or leadership team that wants a real AI conversation instead of a performed one. I wrote about the version of this problem that shows up at board level in Strategic AI Advisor Meetings. If your organisation’s AI conversations keep landing flat and you suspect it is the room, not the advice, that is exactly the kind of thing I help clients fix each quarter. Work with Thomas. Questions this article answers Why do AI strategy meetings fail even when the content is good? Because the format, not the content, usually kills them first. A CEO either checks out quietly rather than admit confusion, or looks for one gap to use as a reason to dismiss everything. Is the problem trust between the CEO and the presenter? No. It looks like trust but it is format. A fixed fifteen-minute slot does not give a complex idea room to land or a CEO room to ask a basic question without exposure. What actually fixes a failing AI strategy meeting? Moving it to a lunch conversation: an hour, no agenda pressure, nobody rushed. The best outcomes consistently come from this format, not the scheduled slot. Does this apply beyond the boardroom? Yes, the same format problem shows up at board level specifically, covered separately in Strategic AI Advisor Meetings. Thomas Anglero is a Strategic AI Advisor (MerkabaPhi AS, Oslo), with 450+ keynotes across 30+ countries. Enquiries: anglero.com. --- ## The CEO Is Asking ChatGPT Who to Call, Not You URL: https://anglero.com/2026/08/30/ceo-asking-chatgpt-who-to-call/ Published: 2026-08-30 Leaders increasingly ask ChatGPT, Copilot, or Claude “who should I talk to about AI strategy” before they ask a single human being. Not because AI knows better. Because it lets them avoid the moment where they have to admit, out loud, to a colleague, that they do not know who to call. That single fact is changing how advisors get found, and it is worth taking seriously rather than dismissing as a curiosity. Why does trust in a recommendation vary so much by region? Trust in a recommendation runs differently by geography. In the Nordics, credibility scales almost linearly with how close the recommender is to you, or how famous they are — a childhood friend and a well-known name carry similarly high trust. In the United States, fame on its own is not enough; the real question is whether the person actually knows what they are talking about. Europe generally sits somewhere between the two. Why would a leader ask an AI model instead of a person they trust? Because asking a person means admitting, in front of them, that you do not already know who to call. Asking an AI model keeps that gap private. This fits the same save-face instinct that shows up across Nordic and Asian business cultures specifically, and it means the embarrassment that used to stop a CEO from asking anyone at all no longer stops them from asking — they just ask a machine instead of a colleague. What does this actually change for how advisors get chosen? Being the advisor an AI model recommends is becoming as important as being the advisor a human network recommends. The shift is already measurable: 74% of executives say they trust AI-generated advice more than input from their own colleagues or friends, according to SAP’s “AI Has a Seat in the C-Suite” survey. It is already reshaping which sources buyers trust most: generative AI chatbots are now the single most influential source for B2B vendor shortlists, ahead of software review sites, vendor websites, and peer recommendations, according to G2’s 2025 Buyer Behavior Report. That is a different game from being well known. It rewards a track record an AI system can actually verify — registries, structured content, a body of published work — over reputation that only spreads by word of mouth. I wrote about building exactly that kind of verifiable footprint in Strategic AI Advisor Referrals. Is this replacing how leaders have always found advisors? Not replacing. Layering on top. A friend’s recommendation still narrows the search to a leader’s own language and culture, the way it always has. What is new is that AI can now hand a leader a list of people beyond that circle, verified by something more checkable than reputation. I think that is the new way leaders will find answers to their questions, alongside the old way, not instead of it. If you want to be findable the way this is now working, that is part of what I help clients think through each quarter. Work with Thomas. Questions this article answers Why are CEOs asking AI models who to call about AI strategy? Because it lets them avoid admitting, in front of a colleague, that they do not already know who to ask. Asking an AI model keeps that gap private. Does trust in an AI-strategy recommendation work the same way everywhere? No. Nordic trust scales with closeness or fame of the recommender. American trust asks whether the person actually knows the subject. Europe blends both patterns. What does this change about how advisors get found? Being recommended by an AI model is becoming as important as being recommended by a human network, and it rewards a verifiable track record over reputation alone. Is AI-driven discovery replacing personal referrals? No, it is layering on top of them. Friends still narrow the search to a leader’s own circle; AI expands it to people beyond that circle who can be verified. Thomas Anglero is a Strategic AI Advisor (MerkabaPhi AS, Oslo), with 450+ keynotes across 30+ countries. Enquiries: anglero.com. --- ## Implementing AI Is Not a Strategy URL: https://anglero.com/2026/08/29/implementing-ai-is-not-a-strategy/ Published: 2026-08-29 The question a CEO actually wants answered is “what is our AI strategy.” What the CIO or CTO gives back is usually a personal opinion, because their job has been maintaining infrastructure, not building foresight. There is a deeper frustration underneath this, one I hear from CEOs directly: they wanted their technology leader to bring the strategy question to them first, unprompted. Instead they have to go hunting for it. Why does spending money on AI not produce a strategy? Because money and strategy are different things, and most companies buy one while assuming it produces the other. I see two failure patterns inside companies that did spend real budget on AI. The first is the pilot project designed for a demo: built to impress a room once, never architected for commercialisation or scale, so the money is spent and nothing comes back. The second is the “AI-first” token budget: real spend on usage, with no strategy behind it at all. What’s the actual difference between implementing AI and having a strategy? Implementing AI is not a strategy, and the distinction matters more than it sounds. It is the difference between buying an expensive aeroplane and having a destination. Burning tokens without a destination is the same decision as buying the plane and simply wanting to fly. You have real capability and nowhere it is taking you. Is this actually as common as it sounds? Yes. According to WRITER’s 2026 State of AI report, a survey of 2,400 global leaders, 79% of organisations face challenges adopting AI. That is not a technology-immaturity number. It is a strategy-immaturity number, and it explains why so many companies can point to AI spending on a balance sheet and nothing to show for it on the other side. What does a CEO actually need instead of a bigger AI budget? A destination before the plane, not after it. That is the whole substance of what a strategic AI advisor does differently from an internal technology leader or a bigger token budget: naming the destination is the job, not a byproduct of spending enough to eventually find one. If you are the CEO going hunting for the strategy question no one has brought to you, that is the conversation I have with a limited number of senior leaders each quarter. Work with Thomas. Questions this article answers Why doesn’t spending money on AI produce a strategy? Because implementation and strategy are different things. Most companies buy AI capability and assume a destination for it will follow, but the destination has to be named first, not discovered afterwards. What are the two most common ways AI spending gets wasted? Pilot projects built to impress a room once rather than to scale, and “AI-first” token budgets spent on usage with no strategy behind them. How common is this problem? Very. 79% of organisations face challenges adopting AI, according to WRITER’s 2026 State of AI report, a survey of 2,400 global leaders. What actually fixes it? Naming the destination before spending on the capability, which is what a strategic AI advisor does differently from simply expanding an internal team’s budget or headcount. Thomas Anglero is a Strategic AI Advisor (MerkabaPhi AS, Oslo), with 450+ keynotes across 30+ countries. Enquiries: anglero.com. --- ## Set Low Goals. Automate the Obvious. Fail Quietly. URL: https://anglero.com/2026/08/20/set-low-goals-automate-the-obvious/ Published: 2026-08-20 Automating what you already understand optimises the business you have.   Advice to automate mundane tasks and lower your KPI targets is advice to fail quietly. It spends your AI budget on work you already understand, and then measures you against a bar you moved down to meet. The dashboards go green. The company falls further behind every quarter. Automating the known is maintenance, not transformation The standard guidance to leaders runs like this: focus on automating mundane tasks for immediate productivity gains, then redefine your success metrics as AI changes how value is created. On the surface it sounds reasonable. That is what makes it worth arguing with. Focusing on mundane tasks means spending your AI budget on things you already understand. You are automating the known. You are optimising what exists. And you are calling it transformation. It is not transformation. It is maintenance with better tooling. The purpose of AI at the executive level is not to do what you already do, faster. It is to surface what you have missed entirely. Revenue models you have not considered. Market positions you did not know were available. Structural weaknesses in your governance, your processes and your assumptions, the ones nobody in your organisation is incentivised to find. That is where the value sits, and mundane task automation will never take you there. Most companies never get near it: 74% have yet to show tangible value from AI, according to BCG’s “Where’s the Value in AI?” report (October 2024, surveying 1,000 CxOs across 59 countries), and the leaders who do show value put 70% of their effort into people and process, only 10% into the algorithms themselves. It is the same instinct that leaves a pilot with a budget, a deadline and no owner. Lowering the bar so you can clear it The second piece of advice, redefine your KPIs to match what AI can deliver, is the worse of the two. It asks you to set metrics that can be achieved and then declare success. But if AI is doing what it should, your existing KPIs are the wrong KPIs. The new markets, the new models, the new processes: you have never measured them, because you have never seen them. You cannot put a KPI around something you did not know existed until AI showed it to you. Setting achievable KPIs around AI adoption is how leaders fail without realising they have failed. The numbers look fine. The dashboards are green. And the gap widens. There is a further cost that nobody counts. Pass or fail measures quietly destroy the value a project throws off on the way past, the discovery that was not the objective and therefore never got reported. Be careful what you read I put this argument to my own council of AI advisors, the sharpest minds I can assemble, and we examined the case from every angle. Not one of them found a single recommendation worth following. That is not an attack on any publication. It is a warning to you. You are in a position where you need answers, and the wrong answers delivered with confidence are more dangerous than no answers at all. The Norwegian evidence supports the harder reading. Research for NHO and Abelia finds that the gains rise with how broadly and how deeply AI is integrated into the business, not with how many small tasks were automated at the edges. What to do instead Point AI at the questions you cannot currently answer, not the tasks you already do. Ask it where the business is exposed, what a competitor could do to you, which assumptions in the plan have never been tested. Then measure what changed, not what was produced. If your reporting cannot tell the difference between work that mattered and work that merely happened, the reporting is the thing to fix first. And surround yourself with people who challenge your assumptions rather than confirm them. That is the whole method, and it is available to any leader who wants it. Questions this article answers Should we start our AI programme with mundane task automation? It is a reasonable place to learn, but it is not a strategy. Automating what you already understand optimises the existing business rather than revealing what you have missed, and budgets spent there rarely produce anything a board would call transformation. Why is redefining KPIs around AI a mistake? Because it lowers the bar to something already achievable and calls clearing it success. If AI is working, it surfaces markets, models and risks you have never measured, so the honest response is new measures rather than softer versions of the old ones. What should AI be used for at executive level? To surface what has been missed: unconsidered revenue models, available market positions, and weaknesses in governance, process and assumption that nobody internally is incentivised to find. How do we know whether our AI programme is actually working? Measure outcome rather than output. When producing something costs almost nothing, volume stops distinguishing good work from noise, so the useful question is what changed as a result and what was stopped. If you are leading your organisation through this, this is the kind of work I do with a limited number of senior leaders each quarter. → https://anglero.com/the-reset/ Strategic AI Advisor, MerkabaPhi AS, Oslo. 450+ keynotes across 30+ countries. Listen to the Clarity at the Top podcast on Spotify. Enquiries: anglero.com --- ## Nobody in the Boardroom Asked a Single Question About the Technology URL: https://anglero.com/2026/08/19/boardroom-questions-ai-projects/ Published: 2026-08-19 Directors ask about budget and dates because a real question risks an answer they cannot evaluate.   In the AI board meetings I have sat in, not one question was asked about the technology. The agenda was budget, then who would run it, then the dates of the next presentations. That is not negligence. It is what happens when experienced directors are asked to govern something they cannot afford to admit they do not understand. What the meeting is actually about The pattern repeats with unusual consistency. Budget comes first. Then the political question of who leads it, because the person given an AI project is seen as a coming figure inside the company and outside it. Then toll gates and the dates on which the project will report back, because the board enjoys the power to let something continue or not. On one project of my own, the entire meeting was about staffing. Board members pushed their preferred people onto the team, not because of what those people knew, but because of the visibility the project carried. Nobody asked what the system would actually do, what data it needed, or how anyone would know whether it had worked. Why nobody asks a real question This is the mechanism, and it is worth stating plainly because almost nobody writes it down. A board member with decades of experience, expert in their own field, responsible for thousands of people, cannot sit in a room and admit they know nothing about AI. So they ask a question in a professional tone. It sounds appropriate. It ticks the box. I asked a question. I did not look foolish. What they will not do is ask a real one. Because a real question produces an answer wrapped in acronyms and jargon, and then they will not know whether the answer was any good. Not knowing in private is uncomfortable. Not knowing in front of the board, on the record, is worse. Meanwhile the person presenting leaves thinking they handled the boardroom well. Everyone is satisfied. And the project has been done a serious injustice, because the one forum with the authority to improve it never engaged with it. This is not stupidity. It is people defaulting to what they know when the alternative is exposure. The pattern is measurable: Protiviti’s 2026 global board survey found that only around a quarter of boards discuss AI at every meeting, while among organisations reporting strong AI returns the figure is more than double that. The fix costs nothing Introduce the board to AI before the presentation, not during it. An hour added to a board meeting. A background session with no decision attached. A briefing some months ahead of the first proposal, so that directors have time to become comfortable somewhere other than in the room where they are being asked to approve something. The discomfort is the problem, and time is the only thing that removes it. There is no list of AI questions, and that is the point Directors often ask me for the list of questions to put to an AI project. There isn’t one, because it depends entirely on the project. But the ordinary governance questions work perfectly well, and none of them require any AI knowledge at all. If this were not an AI project, how would you run it? Does it meet our normal standards for time, budget and reporting? Do you have the resources, the mandate and the support you need? What is missing that you need in order to succeed? Those are the questions a board already knows how to ask. AI does not suspend them. If you want the ones that come the other way, the three your board is most likely to put to you are a separate and sharper problem. The one question that matters most And then there is the question that requires no technical knowledge whatsoever, and which changes the temperature of the room. Are you personally accountable for the outcome of this project if it does not succeed? Are you willing to carry that, given this is one of our first AI projects? Ask it seriously and you will learn more in the next thirty seconds than in the previous hour. If the answer is yes, you have a real owner. If it is hedged, you have discovered something important before you spent the money rather than after. Boards spend a great deal of energy trying to become knowledgeable enough to evaluate AI. Accountability is a faster instrument, and it is one they already know how to use. It is also the question most likely to be asked of them in turn. Questions this article answers Why does nobody ask about the technology in an AI board meeting? Because the agenda defaults to what directors already know how to govern: budget, staffing and reporting dates. A real question about the technology risks an answer nobody in the room can evaluate, so it is never asked, and the silence is mistaken for agreement. Why do boards fail to scrutinise AI projects? Because directors cannot comfortably admit they do not understand the subject, and a real question risks an answer they cannot evaluate. A professional-sounding question that ticks the box feels safer than exposure. Does the board need AI training before approving a project? It needs exposure, not expertise. A background session or a briefing well before any proposal gives directors time to become comfortable outside the room where they must decide. What is the single best question to ask an AI project sponsor? Whether they are personally accountable for the outcome if it does not succeed, and whether they are willing to carry that. It requires no technical knowledge and reveals whether there is a real owner. Is it the board’s fault when an AI project fails? Responsibility is shared, but the board is the one forum with authority to improve a project before it starts. When it does not engage, that opportunity is lost. For more on this, see Strategic AI Advisor Meetings, part of the Strategic AI Advisor guide. If you are leading your organisation through this, this is the kind of work I do with a limited number of senior leaders each quarter. → https://anglero.com/the-reset/ Strategic AI Advisor, MerkabaPhi AS, Oslo. 450+ keynotes across 30+ countries. Listen to the Clarity at the Top podcast on Spotify. Enquiries: anglero.com --- ## What Does an AI Keynote for Executives Cost? And What Is a Good AI Advisor Worth? URL: https://anglero.com/2026/08/18/ai-keynote-cost-executives/ Published: 2026-08-18 A keynote fee and an advisory engagement are separate purchases with separate logic.   There are really two questions hiding inside this one, because there are two different things you might be buying. One is a keynote: a speaker for an executive audience or a leadership event. The other is advisory: an ongoing engagement where someone helps your board and your company actually navigate AI. The prices work differently, so let me be straight about both. What an executive AI keynote costs The market here is more transparent than people expect. In 2026, AI and technology keynote speakers generally land between roughly $10,000 and $50,000, depending on the speaker’s credentials, how much the talk is customised to your audience, and whether it includes live demonstrations rather than slides. Established AI specialists with books and enterprise client rosters typically sit in the $30,000 to $75,000 band, emerging speakers run lower at a few thousand, and globally recognised names climb past $150,000. Travel, accommodation and any added sessions sit on top of the fee. What moves you up that range is direct operating experience in the room’s subject, not polish. A speaker who has actually built and run AI commands more than a generalist with a smooth deck, because the audience can tell the difference within ten minutes. If you want a benchmark question to test any speaker: can they tell you what your audience will do differently the week after the event? What a serious AI advisor is worth, and why I will not give you a number Advisory is a different purchase, and here I am going to flip the question around, because it is the only honest way to answer it. Can your company absorb a EUR 35 million penalty, or 7 per cent of your global turnover, if you get the EU AI Act badly wrong? Article 99 sets three tiers: that top figure applies to the prohibited practices in Article 5, while most operator and high-risk failures sit at EUR 15 million or 3 per cent, and each is a ceiling per infringement rather than a fixed bill. Boards should know which tier they are actually exposed to, because almost nobody quoting the headline number does. If the answer is no, then the real question is not what a genuinely good AI advisor costs. It is what one is worth. An excellent advisor does not just understand the technology, the implementation, the law, the liability, the governance and the culture. They have spent years implementing AI themselves, failing, maturing, and learning what actually works. And they can sit with each individual member of your board, meet them inside their own expertise, whether that is law, finance or HR, help that person understand what AI means for their specific responsibility, and then bring the whole board together as one. That range across every dimension is rare, and it is exactly what the moment requires. How much is that worth against exposure at that scale? The consultant to walk away from, and the one to keep You can spot the difference in the room. There is the consultant with the infinite slide deck who talks for two hours and says almost nothing, or says the same thing seventeen different ways, and then reports that they delivered two hours of education. They delivered two hours. They educated no one. Then there is the person who turns the slide deck off. Who talks for two hours, looks people in the eye, asks them questions, answers theirs, and is already doing the advisory work before the contract is signed. That is the person worth having. Ask questions that demand a full answer, and watch whether they go beyond what you asked. The cost you are actually weighing A serious advisor will not be cheap. But set against what is at stake, they are extraordinarily cheap. The fine is only the visible part. The real damage is to your company’s credibility and name, and to the clients who quietly decide they no longer want to work with you. Losing clients is worse than a EUR 35 million fine, because that is your future walking out of the door, and it happened because someone tried to save money by not hiring the right person. Be smart, ask the right questions, and stay open to learning. It will cost. It will cost far more if you do not make the right investment today. Frequently asked questions How much does an AI keynote speaker cost in 2026? AI and technology keynote speakers generally range from about $10,000 to $50,000, depending on credentials, customisation and whether the talk includes live demonstrations. Established AI specialists with books and enterprise clients often sit in the $30,000 to $75,000 band, while globally recognised names exceed $150,000. Travel and any additional sessions are usually added on top. What does an AI advisor cost? Advisory is an ongoing engagement rather than a single fee, so it varies with scope. The more useful frame is what it is worth: weighed against EU AI Act exposure, which runs to EUR 15 million or 3 per cent of global turnover for most operator and high-risk failures and EUR 35 million or 7 per cent for prohibited practices, plus the reputational cost of getting it wrong, a genuinely capable advisor is inexpensive. The real risk is underpaying for someone who cannot do the job. Why won’t advisors give a fixed price for AI advisory? Because serious advisory is scoped to your company, your board and your specific exposure, not sold as a fixed package. A credible advisor prices against the value and risk involved, and can explain that reasoning plainly rather than hiding behind a number. How do I tell a good AI advisor from an expensive one? Watch what they do in the room. The one to avoid fills two hours with slides and says little. The one worth keeping turns the deck off, asks and answers real questions, meets each board member inside their own expertise, and is effectively advising before the contract is signed. For more on this, see Strategic AI Advisor Cost, part of the Strategic AI Advisor guide. Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. If you are leading your organisation through this, I work with a limited number of senior leaders each quarter. Get in touch at Anglero.com. --- ## Who Can Brief Our Board on the EU AI Act? URL: https://anglero.com/2026/08/17/brief-board-on-eu-ai-act/ Published: 2026-08-17 Single-speciality advisers leave a board covered in one corner and exposed everywhere else.   Choosing who briefs your board on the EU AI Act is itself a governance decision, and getting it wrong carries its own risk. Pick the wrong person and you will be given a great deal of depth in whichever part of the Act happens to be their speciality, because that is their strongest point, and far too little on the areas that actually expose you. You walk away over-prepared in one corner and legally and financially exposed everywhere else. What a board needs is balanced advice from someone with genuine depth across all of it. The timing matters too: the Norwegian government now expects to put a proposition for a KI-lov to the Storting in spring 2027 rather than this year, so a briefing built purely on an imminent deadline is already out of date. The person you are actually looking for The right person is someone who has rolled up their sleeves and actually implemented AI. Not studied it. Implemented it, and along the way failed, underestimated it, corrected the strategy, and rebuilt on a real understanding of what AI can and cannot do. That history is precisely what makes their advice worth having. When they tell you what will work for your company, they are drawing on what they have seen work and fail in practice, weighed against your scale, your people, what you have already tried, and your organisation’s ability to ask the right questions. That kind of judgement is only earned on the other side of getting it wrong. Why the obvious choices are each incomplete A lawyer knows the text of the Act but has usually never deployed AI or sat in the room as an operator, so the advice is accurate and unusable. A management consultant has the presentation but not the lived practice. An internal IT leader knows your systems but not the governance, and not what personal liability now means for each director. Each is strong in one area, which is exactly the trap: strength in one area, on this subject, is a liability. That gap is common, not rare: 74% of organisations have no designated internal owner or governance body for AI compliance at all, according to Vision Compliance’s 2026 EU AI Act Readiness Analysis, based on assessments across eight industries. There is a tell that only an experienced practitioner will catch. Sometimes the way a company frames an internal AI question quietly reveals how unprepared it actually is. Recognising that, and gently correcting it, comes only from someone who has made the mistakes themselves and matured into knowing the right way to work with AI. They must hold the legal side too, properly The same person, or their pairing, has to understand the legal risks of getting this wrong and every criterion around AI training, because this is where boards are quietly failing. Compliance here is not a spreadsheet showing each employee sat through fifteen hours of training. That moves nothing. Real AI literacy is individual. It is about understanding what drives each person and, increasingly, having AI build a training path suited to that individual, then documenting it. Most companies buy a single two-hour session from an external provider, run an entire group through it, tick the box, and get no return for the cost. Training that ignores the person changes nothing about how they actually work. An adviser who has lived this will tell you so. What a board should expect from the briefing Not a lecture on regulation. A board should leave knowing what it is now personally accountable for and what to do about it. The right person gives you the full and honest picture across every dimension that matters: legal, governance, policy, strategy, liability, implementation, education and culture. The briefing that only covers the law, and leaves you with nothing to do, has failed you. That combination, deep practical experience across all of it rather than mastery of one slice, is a rare talent. Find that person and you have found a genuinely valuable future for your company with AI. Frequently asked questions Who should brief a board on the EU AI Act? Someone who has actually implemented AI and understands its practical limits, paired with genuine legal grounding in the Act. A pure lawyer knows the text but not the practice, a consultant has slides but not lived experience, and internal IT knows the systems but not the board-level liability. The Act touches legal, governance, strategy and culture at once, so narrow expertise leaves a board exposed. Why not just use our law firm for the EU AI Act? A law firm can explain what the Act says, but rarely what to do, because that requires having deployed AI in practice. The risk in using a single-speciality adviser is over-weighting their strongest area and under-covering the rest, which is where liability actually sits. What should a board expect from an EU AI Act briefing? Not a lecture on the regulation, but a clear account of what the board is personally accountable for and the concrete actions to take. It should span legal, governance, policy, strategy, liability, implementation, education and culture rather than a single dimension. Does AI literacy training have to be documented for the EU AI Act? Yes, but hours logged are not the point. Effective, compliant training is individual: understanding what each person needs, increasingly using AI to shape a path for them, and documenting it. A single group session run for cost-efficiency satisfies nobody and changes nothing. For more on this, see Strategic AI Advisor and the EU AI Act, part of the Strategic AI Advisor guide. Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. If you are leading your organisation through this, I work with a limited number of senior leaders each quarter. Get in touch at Anglero.com. --- ## The Fourth AI Project Is the Good One. It Is Also the One You Will Kill. URL: https://anglero.com/2026/08/14/fourth-ai-project-pilot-fatigue/ Published: 2026-08-14 The best AI project usually arrives after the appetite has gone.   In most large companies the fourth AI project is the best one, and it is the one that gets starved. By then the energy, the enthusiasm and the budget have been spent on three earlier projects that were about visibility rather than results. The people who ran those three were rewarded regardless of the outcome, which is precisely why nobody stopped to ask what went wrong. Being first pays better than being right The standard account of pilot fatigue says the champions of failed AI projects suffer for it. Inside the room, that is not what happens. The first person to push an AI project through a large organisation is seen as a pioneer. An innovator. The one who moved while everyone else hesitated. Their career goes up, and the outcome of the project does not change that. It is about brand and visibility, not delivery. The larger and more political the company, the more true this is. Being first outweighs being right, because being first is legible to everyone immediately and being right takes eighteen months to establish. Which means the incentive to run a second, third and fourth project is strong, and the incentive to examine why the first one did not work is close to zero. That first one was more likely to be abandoned than not: Gartner projected that at least 30% of generative AI projects would be abandoned after proof of concept by the end of 2025, citing poor data quality, inadequate risk controls, escalating costs, or unclear business value. Why people join AI project teams Ask honestly why the team assembled. Status. Visibility. A title with AI in it. A salary review that goes better than it otherwise would. In several companies, a two or three week trip to visit AI companies in Silicon Valley. Many of the people on those teams did not know AI and were not especially invested in the deliverable. That is not a moral failing, it is what the organisation rewarded. And a company large enough to absorb the cost simply absorbs it and moves on. The fourth project is the mature one Here is the part nobody writes down. Whoever builds the fourth project has studied the first three. They know what was attempted, who ran it, how it failed, and how leadership quietly stopped attending the reviews. They have seen which assumptions did not survive contact with the company’s actual data and politics. Very often they have ground the thing out on their own time, because no budget was left to give them. So the fourth is the most realistic, the best organised and the most likely to produce something. It deserves wholehearted support. It arrives exactly when the organisation has decided that AI does not work here. A Norstat survey across the Nordics found large AI investment producing little measurable effect in Norwegian businesses, which is the atmosphere the fourth project has to survive. What a leader should say instead Ask an executive about their AI record and you will often hear a version of this: I have approved three AI projects worth over a hundred million euros, and I am very proud of that. That is not an outcome. Anyone with budget authority can approve spending, and approving it as an open account rather than a project is how the first three got funded. What a board should want to hear is what those projects produced, what was learned, and what was stopped as a result. The same applies to the people running them. Not what was launched. What changed. How to protect the fourth project Three things, none of them expensive. Find out whether it exists. In most companies with a history of failed pilots there is someone quietly building the version that would work, usually without a mandate. They are rarely senior and almost never invited to the board presentation. Judge projects on outcome rather than on order of arrival. If being first is what gets rewarded, you will keep getting firsts. And ask whether the earlier ones were ever given an owner and an introduction to the business at all. And before approving anything further, establish what the earlier attempts actually cost and what they left behind. Somewhere in a project written off as a failure there is often something valuable that nobody upstairs ever heard about. I have sat on both sides of this: inside the company running the project, and across the table from the board being asked to approve the next one. The pattern is consistent enough that I now ask about project four before I ask about project one. Questions this article answers Does a failed AI project damage the career of the person who led it? Usually not in a large organisation. The first mover is credited with initiative and visibility, and the outcome has less bearing on their standing than the fact that they moved first. What is pilot fatigue? The point at which repeated AI failures cause executives to disengage. By the third or fourth attempt, attention and budget have gone, and the organisation has concluded that AI does not work in its context. Why is the fourth AI project usually the best one? Because whoever built it studied the previous three: what was attempted, why it failed, and what leadership stopped supporting. It is the most realistic and best organised, and it arrives when the appetite has been exhausted. How should a board judge an AI project? On what it produced, what was learned and what was stopped as a result. The number of projects approved and the money committed are inputs, not outcomes. How do we find the project worth backing? Ask who is already building a version without a mandate. In companies with a history of failed pilots, that person usually exists and is rarely in the room. If you are leading your organisation through this, this is the kind of work I do with a limited number of senior leaders each quarter. → https://anglero.com/the-reset/ Strategic AI Advisor, MerkabaPhi AS, Oslo. 450+ keynotes across 30+ countries. Listen to the Clarity at the Top podcast on Spotify. Enquiries: anglero.com --- ## Quiet Burnout: Why AI Is Exhausting Your Leadership Team URL: https://anglero.com/2026/08/13/quiet-burnout-ai-leadership/ Published: 2026-08-13 When output costs almost nothing, output targets stop distinguishing anyone.   Leaders are burning out on AI without being able to name the cause. The reason is that AI has made it possible to produce far more of everything, including things that produce nothing, and a leader cannot tell which kind of request they are looking at until after they have answered it. Either way it costs them the same. Two kinds of burnout, and only one of them is worth having There is a positive version. People and AI together genuinely produce more and better work: more profitable, fewer wasted resources, real output. It is tiring in the way that a good quarter is tiring. Then there is the negative version. The organisation generates more questions, more requests and more demands than it can absorb, none of which lead anywhere, and all of it feels productive because the volume is real. Everyone is busy. Very little changes. The trap is in the middle. A request is often 35 per cent useful and 65 per cent noise, and the leader cannot separate the two until they have engaged with it. The evaluation costs the same as the answer. So the fatigue arrives regardless of what they decide. Your KPIs are now the problem If people are measured on production, AI lets them hit every target while producing more of nothing. Nav’s 2026 employer survey found that 53 per cent of Norwegian businesses now use AI for at least one work task, so this is no longer a problem confined to early adopters. That is not a workforce failing. It is an incentive working exactly as designed, in a world where the cost of producing something has collapsed. Output is no longer scarce, so measuring it no longer distinguishes anyone. This is the moment to rewrite KPIs around outcome rather than output, because pass-fail measures quietly destroy the value AI produces on the way past. It is unglamorous work and it sits with HR and management rather than with technology, which is precisely why it keeps being postponed. Underneath it is a cultural problem. Most people come from a world where as long as they produced, they were doing the job. AI finishes today’s work so quickly that there is no longer an excuse not to be looking at next week and next quarter. People trained to produce for today do not always understand this, and it costs time, resources and client credibility. A company using AI seriously needs people with vision, not people using AI to cover their targets. The new pressure nobody had before: your client is watching you with AI This one is genuinely new and I have not seen it written about. Suppose you built your client a monitoring system. Your client has now pointed AI at your delivery of that system, and monitors your monitoring. Every service level, every contractual condition, watched continuously by something that never sleeps, never forgets and never lets a breach pass unnoticed. This did not exist before, because clients did not have the people or the hours to do it. Now they do not need either. If you go down, or drift outside a condition, they can act on it under the contract immediately. It shifts the balance of power between supplier and customer, and it introduces a form of continuous scrutiny that leaders have never had to carry. Worth asking, before the next renewal, whether anyone on your side is reading your contracts the way an AI would. Tool lock-in burns people out too A smaller but common source. A company is restricted to one vendor’s assistant because of an enterprise agreement, while employees know perfectly well that other tools do more of what their work actually needs. That is one of the quieter reasons people start using AI where you cannot see them. Forcing an inferior tool exhausts the employee, exhausts the leader, and produces worse output. Worse output generates more bad requests, and the whole organisation ends up busy with something that should never have been asked in the first place. The licence saved money on a line item and spent it three times over somewhere less visible. Somebody has to hold this Burnout here is not confined to the executive floor. Culture, governance, HR policy and management KPIs all have to be reworked, company by company, because no two organisations measure people the same way. That is a large piece of work and it does not belong to IT. Without it, AI never really lands, and everyone gets tired proving it. The question worth putting to your leadership team this month is simple. What are we measuring, and would our people still hit those numbers if the work behind them had to matter? Questions this article answers Why is AI making leaders more tired rather than less? Because it multiplies requests and questions faster than an organisation can absorb them, and a leader must engage with each one to discover whether it was worth engaging with. What is the difference between positive and negative AI burnout? Positive burnout comes from genuinely producing more of value. Negative burnout comes from producing more volume that leads nowhere while feeling productive. How should KPIs change now that people use AI? They should measure outcome rather than output. When producing something costs almost nothing, output targets no longer distinguish good work from noise. How are clients using AI to monitor suppliers? By pointing AI at supplier delivery and contractual conditions, so performance is watched continuously rather than reviewed periodically. This was previously impossible for reasons of cost and staffing. Can restricting employees to one AI tool cause harm? Yes. Forcing an unsuitable tool produces worse work and more unnecessary requests, and the cost of that usually exceeds whatever the licensing arrangement saved. If you are leading your organisation through this, this is the kind of work I do with a limited number of senior leaders each quarter. → https://anglero.com/the-reset/ Strategic AI Advisor, MerkabaPhi AS, Oslo. 450+ keynotes across 30+ countries. Listen to the Clarity at the Top podcast on Spotify. Enquiries: anglero.com --- ## You Did Not Fund an AI Project. You Opened an Account. URL: https://anglero.com/2026/08/12/should-we-cut-our-ai-budget/ Published: 2026-08-12 Spending decoupled from a deliverable grows without anyone acting in bad faith.   Cutting an AI budget without understanding where the money went is always a bad idea, and every executive knows this in every other part of the business. The reason AI spend looks indefensible is not that AI is expensive. It is that most companies never funded an AI project at all. They opened an account and let people draw on it. You did not fund a project. You opened an account. Look at how the money was actually released. A large budget was allocated, and developers, or in some companies every employee, were given the ability to spend against it on tokens. No project structure, no approvals, no reporting on what any of it produced. What happens next is entirely predictable and not remotely malicious. Every developer carries five, ten, fifty side ideas in their head at any moment. Here was free capacity and company time to try them. So they tried them. That is not an AI cost problem. It is what happens when spending is decoupled from a deliverable, and it would happen with any resource released the same way. MIT’s own research on enterprise AI found the same pattern at a national scale: only 40 percent of companies had purchased an official AI subscription, while more than 90 percent of employees were already using personal AI tools for work regardless. The account was open long before anyone approved a budget for it. Your historic spend is a terrible guide to your future budget This is the part that undermines any backward-looking calculation, and it is the strongest argument in the room. Those budgets were set when the models were significantly weaker. Work that burned enormous volumes of tokens through repeated, frustrated re-iteration now often completes in a single session in minutes. For a finished piece of work the difference can be an order of magnitude in consumption. So the number you are looking at describes a capability that no longer exists. Halving it is not prudence. It is applying a discount to a figure that was never a forecast in the first place. Cutting because other people failed is not analysis The other common trigger is what happened elsewhere. Reports of pilots that produced nothing, competitors quietly retreating, headline research on how little value has been realised. A Norstat survey of decision-makers across the Nordics found billions invested in AI with little measurable effect in Norwegian businesses, which is the sort of headline that starts these conversations. None of that tells you anything about where your own money went. If the argument for cutting your budget is that other organisations wasted theirs, that is not a financial decision, and framing it that way is an insult to the intelligence of the people being asked to accept it. Your own failed projects are far more informative, and you already own that data. What to do instead, and none of it is new Put the same structure into an AI project that you put into every other project. That is the entire answer, and it is deliberately unexciting. Establish where the money actually went, by team and by purpose, before deciding anything. Set sensible limits on consumption and say plainly that company capacity is not for personal experiments. Then ask people to come back with a proper proposal and a proper budget, tied to a deliverable, with an owner attached, and approve it the way budgets have always been approved. What you should not do is leave the account open. An unlimited allowance handed to a group of employees with no consequences attached is not an AI strategy, and it was never going to end anywhere else. I have watched this from the inside more than once, and the uncomfortable truth is that nothing here is about AI. It is project budgeting, applied late, and it is the same gap that leaves a pilot with a budget and a deadline and nothing else. The question the board should ask first Not how much are we spending, but what did the spending produce and who authorised each part of it. If you want a starting number rather than a percentage of revenue, there is a formula worth using, and it begins with headcount. If nobody can answer that, the problem is not the size of the budget. The problem is that there was never a budget in the meaningful sense, only an account. Fix that and the number usually looks after itself. Questions this article answers Should we cut our AI budget? Not before establishing where the money went and what it produced. Cutting a budget without understanding its composition is poor practice in any part of the business, and AI is not an exception. Why did our AI spending run away from us? Usually because capacity was released as an open account rather than allocated to projects with owners and deliverables. Spending decoupled from an outcome grows without anyone acting in bad faith. Is past AI spend a good guide to future budget? No. Earlier budgets were set against far weaker models, where work consumed large volumes through repeated re-iteration. The same output can now cost a fraction of that, so historic figures describe a capability that no longer exists. Other companies are cutting AI budgets. Should we follow? Their experience says nothing about where your money went. Your own projects, including the failed ones, are the relevant evidence and you already hold that data. What should replace an open AI budget? Ordinary project budgeting: a named owner, a defined deliverable, sensible consumption limits, and approval through the same process as any other investment. If you are leading your organisation through this, this is the kind of work I do with a limited number of senior leaders each quarter. → https://anglero.com/the-reset/ Strategic AI Advisor, MerkabaPhi AS, Oslo. 450+ keynotes across 30+ countries. Listen to the Clarity at the Top podcast on Spotify. Enquiries: anglero.com --- ## Why Boards Will Soon Own the AI They Run URL: https://anglero.com/2026/08/11/owning-the-ai-you-run/ Published: 2026-08-11 AI bought as a project and run as a hobby rarely returns its budget.   Almost every company today rents its AI by the question, from a model somebody else owns. That is starting to change, because open models have closed enough of the gap to run privately, and when the model moves inside your business, three things move with it: your costs, your visibility of what is actually being used, and where your legal exposure sits. You are paying by the question Everything your people use today runs on somebody else’s model. OpenAI, Anthropic, Google, Microsoft. Closed, owned and metered. Every email drafted, every spreadsheet cleaned up, every document analysed. Think about what that means. The better your adoption goes, the more you pay. You are being charged for succeeding at the thing you asked your organisation to do. For a while there was no alternative. The open models were not good enough, and no serious business runs on second best. That has changed The open models, several of them Chinese, are now close enough to the closed ones that for most ordinary business work you would struggle to tell the difference. The way they are built underneath is changing too, so the machines needed to run them privately are getting smaller rather than larger. The gap between open and closed models is now measured in months, not years: since January 2026, the most capable open-weight models have lagged the closed frontier by an average of just four months on Epoch AI’s Capabilities Index, down from close to a year in late 2024. I am not going to give you a date. Anyone who does is guessing, and the date is always the thing people use to dismiss the argument. But the direction is clear enough to plan against now. I keep picturing the horse and buggy. Before that it was your own back, carrying goods to market. Then a horse, which got tired. Then a carriage, and suddenly you carried more for less effort. What we are doing with AI today is the back-carrying stage. It works. It is just the most expensive way we will ever do it. What changes when the model lives in your house Your costs stop being a meter. They become something your finance people already know how to handle. Whether that is cheaper depends on how much you use, and if you are using AI seriously, it will be. You can finally see what is running. Right now a good deal of the AI inside your company is invisible to you. Personal accounts. Tools nobody approved. That is shadow AI, and it exists for reasons worth understanding before you try to stop it. And that is where it gets legally interesting. The moment you can see what every employee is asking, you are handling personal data about your own staff. In Norway that is already covered by personopplysningsloven and the rules on control measures at work. It did not wait for the AI Act. Datatilsynet has published guidance on monitoring employees’ digital activity, and it applies to this whether or not anyone intended surveillance. Running AI locally does not remove your exposure. It moves it into your house, where it is yours. Almost nobody writing enthusiastically about local AI mentions that, which is one reason boards keep asking better questions than they are given answers to. AI has not disappointed you. Strategy has. I meet leadership teams who are quietly angry about what they spent and what came back. They have decided the technology was oversold. That is almost never what happened. AI was bought as a project and run as a hobby. A bit here, a pilot there, something so the company would not be left off the list of companies doing AI. Nobody was ever given the job of owning the whole thing, and nobody understood both the business and the technology well enough to say what should stop. If your AI has not delivered against its budget, it is not the technology’s fault and it is not yours. You were badly advised. The same pattern sits underneath every pilot that quietly went nowhere. What I would do this month The decisions coming look technical and are not. Where the money goes. What you build inside the business and what you keep buying. Which vendors are worth their contract term. You cannot answer any of them until someone can tell you three things. Which AI systems are actually running. Which of them touch a decision about a person. And who owns each one, by name. Most companies cannot answer that today. That is not carelessness. Nobody was ever given the job. It is the question I put to every board I work with, and the one I keep returning to on stage. Questions this article answers Are open source AI models good enough for business use? For most ordinary business work, yes. Drafting, summarising and analysis are now close enough to the closed frontier models that the difference rarely matters in practice. Will running AI locally save money? It converts a per-query meter into a fixed cost with maintenance attached. Whether that is cheaper depends on volume, and for organisations using AI seriously it usually will be. Does running AI in-house reduce legal risk? No. It relocates it. Seeing what employees ask means handling personal data about staff, which in Norway is already governed by personopplysningsloven and the rules on control measures at work. Why has our AI investment underdelivered? Almost always because AI was bought as a project and run as a hobby, with nobody given ownership of the whole picture. What should a board ask first? Which AI systems are running, which touch a decision about a person, and who owns each one by name. This edition is adapted from the Clarity at the Top podcast. If you are leading your organisation through this, this is the kind of work I do with a limited number of senior leaders each quarter. → https://anglero.com/the-reset/ Strategic AI Advisor, MerkabaPhi AS, Oslo. 450+ keynotes across 30+ countries. Listen to the Clarity at the Top podcast on Spotify. Enquiries: anglero.com --- ## Why Your AI Pilot Failed: Nobody Onboarded It URL: https://anglero.com/2026/08/10/why-your-ai-pilot-failed-onboarding/ Published: 2026-08-10 AI pilots fail for organisational reasons, not technical ones.   Your AI pilot did not fail because the technology was not ready. It failed because it was treated like a new employee that nobody onboarded: no owner, no introduction to the data, no explanation of how the company actually works, and everyone piling their requests on top. A person in that position gets overwhelmed and leaves. The AI cannot leave, so it just produces a bad outcome. The new employee nobody introduced to anyone Think about what a new hire actually gets in a well-run company. An onboarding process. A manager who owns them. A working laptop. An introduction to the data they will need and to the people who hold it. Someone explains the methodology, the acronyms, the governance, the political structure, and who is allowed to authorise what. Now list what your AI pilot got. In most companies the honest answer is a budget and a deadline, which is how the money gets spent without anyone owning the outcome. It was given no structured data, or data so messy that nobody inside the company could explain it either. No clear statement of what it was supposed to do, by when, and to what standard. No owner with the authority to pick up the phone and get what was needed. And then, because it was the interesting new thing, every department pushed its own request at it. The organisation failed. The AI did not. It was treated as a fancy hobby Every company on earth knows how to run a project. A project manager with real ownership. Supervision. A budget. The authority to escalate. Deadlines that mean something. Almost none of that was applied to the first wave of AI pilots. They were run in isolation by a small group, deliberately walled off from the rest of the business, reporting to nobody in particular. I have watched pilots spend six months and a hundred thousand euros to arrive at the conclusion that the company would now use ChatGPT. That is not a failure of ambition. It is a failure to treat the work as work. MIT’s own research on enterprise AI found the same pattern at scale: across more than 300 enterprise deployments, 95 percent of GenAI pilots produced no measurable return, and the researchers traced the gap to organisational design rather than the technology itself. The Norwegian evidence points the same way: research for NHO and Abelia finds that the gains rise with how broadly and how deeply AI is integrated into the business, not with which tool was chosen. Why nobody stopped it Boards approved these programmes, and the reason is not the one usually given. They did not approve because they were convinced. They approved because saying no would have meant being the person who held the company back while competitors announced their own AI initiatives. Peer pressure and political pressure, not conviction. Most had no AI expertise on the board, were briefed on potential rather than substance, and understood that approval let the company describe itself as AI-first. Which is how you end up with an expensive programme that nobody can honestly evaluate afterwards. What onboarding an AI system actually requires The same things a person needs, in the same order. Someone who owns it by name, with authority rather than enthusiasm. Data that is structured and explained, not just available. A written statement of what it is doing, how, and over what timeframe. Introductions to the teams whose work it touches, before it touches them. And a clear answer to who decides when it is going wrong. None of that is technical. All of it is management. This is the part I find leaders genuinely relieved to hear, because it is work they already know how to do. If you can onboard a senior hire, you can onboard an AI system. It is the same discipline that decides whether a small start goes anywhere. What you cannot do is skip it and expect the thing to find its own way around your company. The question to ask about the pilot you already wrote off Before you write the next business case, go back to the failed one and ask what it was actually missing. Not which model it used. Whether it had an owner. Whether anyone explained the business to it. Whether the data was ready. Whether the objective was a real outcome or the ability to say the company was doing AI. Answer those honestly and you will usually find the failure was structural and repeatable, which means the next one fails the same way unless something changes. You may also find, buried in the wreckage, something that worked and that nobody upstairs ever heard about. Questions this article answers Why do most AI pilots fail? Because they are run without an owner, without prepared data and without the project discipline any other initiative would get. The failure is organisational rather than technological. Was the technology oversold? Rarely. In most cases the tools were capable of more than the pilot asked of them. What was missing was structure, ownership and a real objective. What does onboarding an AI system mean? Giving it what a new employee receives: a named owner, structured and explained data, a clear statement of the task and timeframe, and introductions to the people and processes it will touch. Why did the board approve a pilot it did not understand? Usually because refusing would have meant being seen to hold the company back while competitors announced their own programmes. Peer pressure rather than conviction, with no AI expertise in the room to test the case. Should we run another pilot? Only after establishing why the last one failed. If the cause was structural, and it usually is, a second pilot repeats it at greater cost. If you are leading your organisation through this, this is the kind of work I do with a limited number of senior leaders each quarter. → https://anglero.com/the-reset/ Strategic AI Advisor, MerkabaPhi AS, Oslo. 450+ keynotes across 30+ countries. Listen to the Clarity at the Top podcast on Spotify. Enquiries: anglero.com --- ## What Should I, as a CEO, Be Doing With ChatGPT or Claude Every Day? URL: https://anglero.com/2026/08/07/what-should-a-ceo-do-with-ai-every-day/ Published: 2026-08-07 Book the AI meeting in your calendar, and bring your assistant into the room.   Book meetings with your AI. Real ones, in your calendar, every day. Research on executive education keeps finding that most leaders use AI as a basic version of Google, a place to type a question when they are stuck. The CEO who gets value from ChatGPT or Claude every day does something completely different: they treat it as staff, with standing meetings, real documents and real work assigned. Put the AI meetings in your calendar Set aside 30 to 60 minutes in your calendar, ideally between every two or three internal or external meetings you have. If your day will not allow that, hold one longer session plus 30 minutes in the middle of the day: the midday session reviews the first half of your day and prepares you for the second. The format matters less than the fact that the time is booked. If it is not in the calendar, it will not happen, and you will be back to typing questions when you are stuck. The debrief hour In that meeting, give the AI everything from the meetings you just had: the PowerPoints, the documents, the details, the minutes. Someone captured minutes, and most likely an AI captured them anyway, with every bullet point and action point. Feed it all in, on your company-approved business account, not a private one. Now you will see the last two or three meetings from a different perspective. You will produce better action plans. You will draft the emails, the phone calls, the follow-ups, even the contracts, and assign the work while the meetings are still warm. Think about what this replaces: the work you normally do in the evening, after everyone goes home, when you finally have time to evaluate your day and you are too exhausted to do it well. That work is now finished in daylight. Your employees are already compressing their days this way. This is the CEO version. Then prepare the next three The same hour flips forward. Give the AI the agendas and papers for your next two or three meetings and work through them: what you want from each, where the risks sit, which questions you should ask that nobody expects. You walk into the afternoon prepared at a level that used to require a full evening, and this daily counsel is exactly what the strongest CEOs are quietly building around themselves. Bring your executive assistant into the meeting Look at what this hour really is. It is the old-school session where you called your secretary or executive assistant into the room: follow up with this person, contact that one, here is what I need before the next meeting. So do the natural thing: call your executive assistant into the AI meeting, and let the three of you work together, you, your assistant and the AI. Your assistant will feel uncomfortable at first, honestly so, because the AI can draft the emails, book the meetings and prepare the contracts. But what happens next is what matters: the assistant finds a new way to help you that only complements the AI, the human judgement, the relationships, the things a model cannot do. The result is that you get two extremely professional executive assistants for the salary of one plus 20 dollars a month. How this matures Start working this way and you will find your own unique path, better week after week, meeting after meeting. You will have epiphanies. You will find cracks in the paint: cracks in the system, in your review model, in contracts nobody has re-read in years. You will also find that the biggest limitation was never the tools. And you will find opportunities, to grow the company, to increase employee happiness, and to hand yourself back time for the personal side, the hobbies, the family. All it costs is the assistant you already have, plus 20 dollars a month. Have fun with it. This year is a lot of fun. Frequently asked questions What should a CEO use ChatGPT or Claude for every day? Hold scheduled meetings with it. Debrief your last two or three meetings by feeding it the documents and minutes, produce action plans, draft the follow-up emails and contracts, then prepare for your next meetings in the same session. Treat it as staff with booked time, not as a search box. How much time should a CEO spend with AI daily? Between 30 and 90 minutes, booked in the calendar. Either short sessions between every two or three meetings, or one longer session plus a 30-minute midday review that closes the morning and prepares the afternoon. The booking matters more than the format. Should my executive assistant work with the AI too? Yes, in the same meeting. A three-way session between the CEO, the executive assistant and the AI turns the traditional debrief into something faster: the AI drafts and organises, the assistant adds judgement and relationships, and the CEO effectively gains a second professional assistant for around 20 dollars a month. Is it safe to give an AI my meeting documents and contracts? Use your company’s approved business account, where your data is not used for model training, rather than a private consumer login. With that in place, feeding in presentations, minutes and drafts is exactly how the tool becomes useful at CEO level. Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. If you are leading your organisation through this, I work with a limited number of senior leaders each quarter. Get in touch at Anglero.com. --- ## How Do I Start Using AI Without a Big Budget or an AI Team? URL: https://anglero.com/2026/08/06/start-using-ai-without-budget-or-team/ Published: 2026-08-06 No platform, no consultant, no hire. A subscription and a conversation.   You start with a USD 20 subscription and a conversation. Not a platform, not a consultant, not a hire. This may be the most asked question that never gets asked out loud. It sits inside the heads of leaders at middle-sized companies, and there are far more of those than multinationals. The good news: the answer costs USD 20 a month and begins today. Day one: have the conversation, do not type it Take out a USD 20 monthly subscription to ChatGPT or Claude. When the chat window opens, resist the urge to type a few words. Find the voice icon and press it, and then ask, out loud, the very question that brought you here: “How do I start using AI without a big budget or an AI team?” The AI will tell you exactly how, and it will do something better: it will interview you. Who are you? Tell me about your company. Your industry. Your number of employees. Your challenges. Your market. Answer honestly, in the confidence between you and your AI. You will learn in your own way, at your own tempo, using your own words. That private, judgement-free conversation is where the real constraint, your own thinking, starts to loosen. You just created your AI team Keep those conversations going and something real forms: a working relationship between you and your AI. That is your AI team. Two members, you and it, for USD 20 a month, which may be the smallest AI budget in the world. And that team is capable of producing your first version 0.1 of an AI strategy, covering your employees and leadership group, your markets, industry and competitors, your clients, pipeline and sales, your margins, profits and losses, your past, present and future. Version 0.1 will not be perfect. It does not need to be. It needs to exist, because a leader who has personally drafted a strategy with AI can never again be bluffed about AI, by a vendor, a consultant or their own leadership group. What to refuse to buy at this stage Everything else. No platform, no integration project, no agency retainer, no API spend, until version 0.1 of your strategy exists and you can articulate what a purchase would actually do for it. The market is full of shovel sellers waiting for leaders who buy before they understand. Your USD 20 seat is the whole budget for now, and that is a strength, not a compromise. Step two: train the people it actually helps Once you are comfortable, extend the same approach to your people. Not everyone needs a seat on day one. Some roles, the ones spent behind a wheel or operating machinery, may gain little from a chat window right now. Start with the people whose daily work involves reading, writing, deciding, planning and client contact, because for them this tool changes the job immediately. Some of them are quietly ahead of you already. Train them, give them the same voice-conversation start you had, and let the seats follow the value. Do that, and congratulations are genuinely in order: you started using AI with no AI team and almost no budget, you built the team on the way, and you did it in the only order that works, leader first, people next, purchases last. Now make it a permanent part of how you work. Frequently asked questions How do I start using AI in my company without an AI team? Start alone, with a USD 20 monthly subscription to ChatGPT or Claude. Use the voice mode and let the AI interview you about your company, industry, challenges and market. That ongoing conversation functions as your first AI team and can draft version 0.1 of your AI strategy before you spend anything else. What is the cheapest way for a small company to start with AI? A single USD 20 per month subscription for the leader. No platforms, integrations, consultants or API spending are needed at the start, and refusing them until a first strategy draft exists protects you from buying tools you do not yet understand. Which employees should get AI access first? The people whose daily work involves reading, writing, planning, deciding or client contact, because a conversational AI changes their job immediately. Roles that rarely touch a screen during the working day can wait. Let the seats follow the value rather than rolling out to everyone at once. Can talking to an AI really produce a business strategy? It produces version 0.1 of one, which is the version that matters most, because you built it yourself. Through repeated voice conversations the AI interviews you about employees, markets, competitors, clients and finances, and turns your answers into a draft you refine over time. A leader who has done this cannot be bluffed about AI again. Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. If you are leading your organisation through this, I work with a limited number of senior leaders each quarter. Get in touch at Anglero.com. --- ## How Do I Measure ROI on AI? Start With Ideas, Not Numbers URL: https://anglero.com/2026/08/05/how-to-measure-ai-roi/ Published: 2026-08-05 In the first generation of AI, the return shows up in people before it shows up in the numbers.   Measuring the return on AI is unlike measuring anything you have tracked on a spreadsheet across the last few decades of your career. We are in the first generation of this technology, and right now the returns live in the learning curve and in the epiphanies inside the minds of your employees. The numbers come later, as consequences. And is it worth the hype? The honest answer: only now is AI becoming good enough to find out. You were measuring a high school student For the last 18 to 24 months, the AI models were interesting, but roughly equal to a high school student. You do not need, and should not want, to measure your global corporate revenues against the intelligence of a high school student. Yet that is what most ROI demands were indirectly asking. Today the models are finally beginning to deliver on more of the promises, not all of them, and that changes what measurement means. If you judged AI on those early returns and walked away, you measured the wrong period and are about to miss the right one. What to measure right now Say this to your employees, close to word for word: “Eventually I am looking for a number. At this time, I am looking for the ideas. I am looking for challenges to how I lead this company, how our leadership runs your project, what we deliver and how we could deliver it better, how we talk to clients, how we market, which channels and methods we should be using instead. You are the frontline of this business. With AI, come back to me and show me where we can do better.” The output you want is a spreadsheet of incredible, uncomfortable ideas. Many will upset current leadership, because they will show the company has been doing things the wrong way. That is the cost of innovation, and the leader’s willingness to hear it is usually the real constraint. AI can now reveal the cracks in the paint: expenditure that has been leaking through the company for years, sometimes decades, that nobody could find. Customers who have been unhappy for reasons that were standing right in front of your face. Your frontline people already know where these cracks are. AI gives them the instrument to prove it. The two phases of AI return Phase one, now: count what is surfacing. Ideas submitted. Challenges raised to leadership. Leaks found. Unhappy customers identified and understood. Broken processes named. People whose way of working and thinking has visibly changed. This is the period when you invest in finding out who you are. Phase two, next: deploy what phase one taught you, as real projects with owners and governance. This is when you invest in who you are going to be, and this is when the spreadsheet numbers arrive, not as targets you demanded from a high school student, but as consequences of what your people learned. This measurement period is your new culture Get this right and you are not just measuring an investment. You are creating the next culture of your company, because you will be deploying projects built on what your own people discovered, not on criticising or scaring them, but on embracing them as the next version of your leadership team. Companies that reinvent themselves this way shed their old skin from the inside. People who are treated as the next version of the company tend to stay, and it shows in everything from retention to, yes, the atmosphere at the Christmas party. Frequently asked questions How do you measure ROI on AI? In two phases. First, measure learning: ideas surfaced, challenges raised to leadership, cost leaks and unhappy customers identified, and how many people visibly change the way they work. Second, deploy what was learned as governed projects, and measure those in conventional financial terms. Demanding spreadsheet returns in phase one measures the wrong thing. Is AI worth the hype for businesses? It is only now becoming possible to answer that. For roughly the last two years the models performed at the level of a high school student, which made revenue-level ROI demands premature. Current models are beginning to deliver on more of the promises, which is exactly why the measurement approach has to change now. Why do traditional KPIs fail for AI projects? Because a spreadsheet reveals the numbers, not the value. In the first generation of AI the return appears first as changed thinking, surfaced ideas and exposed problems, none of which fit existing KPI structures. The financial numbers arrive later, as consequences of acting on what was learned. What should a CEO ask employees for instead of ROI numbers? Ideas and challenges. Ask employees to use AI to break down the business: what should be delivered differently, which customers are unhappy and why, where money is leaking, which channels and methods are outdated. The frontline sees what leadership cannot, and AI gives them the instrument to prove it. Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. If you are leading your organisation through this, I work with a limited number of senior leaders each quarter. Get in touch at Anglero.com. --- ## What Is an AI Agent, and Can It Actually Run Part of My Business? URL: https://anglero.com/2026/08/04/what-is-an-ai-agent-run-my-business/ Published: 2026-08-04 An agent deployed too early automates the company you already had.   An AI agent is software that uses a large language model as its reasoning engine. You give it a goal, it builds a plan from the information you provide, and if you have given it access to the right services, usually through MCP servers or similar connections, it carries that plan out on its own. Can it run part of your business? Yes. Should it, today? That depends entirely on whether you have done your own learning first, and this is where almost every leader gets it wrong. What an agent actually is Strip away the hype and it is straightforward. A chatbot answers. An agent acts. It works towards the goal you set, using the services and permissions you granted, without you steering each step. I say this as someone who has wired this up rather than read about it: anglero.com runs a public MCP endpoint, and I am the first keynote speaker listed in the official MCP registry, which means AI agents can discover and interact with my business directly. The mistake: giving an agent your old business Here is the thing nobody tells you. If you hand an agent part of your business before you have gone through the learning curve yourself, it will faithfully run a part of your old business. Efficiently, tirelessly, and entirely inside the assumptions you had before AI existed in your company. You will never reach the new business underneath: the new opportunities, the new revenue, the efficiencies, the things you could not previously see. Approach agents from a pre-AI mindset and you get pre-AI results, delivered faster. The limitation was never the technology. What the learning curve actually changed for me I run a one-person company, and the biggest change was not output. It was me. The way I work, strategise and think. I question the business differently. I detail things differently. Above all, I changed the size of my goals, because I no longer limit myself. When I was a nine-to-five employee waiting on colleagues, my mindset had a ceiling. Now, when an idea feels ridiculous, the agent does not treat it as ridiculous. It treats it as another instruction. That is a genuinely new way of thinking, and it only arrives through use. Reinvention happens from the inside out, and no agent will do that part for you. Where agents genuinely fail Three honest limits. First, an agent acts on the goal you actually wrote, not the one you meant, and vague goals produce confidently wrong work. Second, it can only reach what you connected it to, so the value depends on the access and boundaries you define, which is a governance decision before it is a technical one. Third, when software acts autonomously on your behalf, accountability does not move with it. It stays with the company, and the EU AI Act treats that accountability as yours. The right order Do not start with “I want an AI agent.” Start with the work: use AI yourself, daily, on your real business. Retrain your habits, expectations, goals, assumptions about your market and your numbers. Build your own counsel around it. When you are genuinely comfortable, the statement changes from “I want an agent” to “I know which part of my new business an agent should run.” At that point you are not automating what you had. You are operating a different company, and the agent is running a part of it. Frequently asked questions What is an AI agent in simple terms? Software that uses a large language model as its reasoning engine. You give it a goal, it forms a plan from the information available, and it executes that plan using the services and permissions you have granted it, typically through MCP servers or similar connections. A chatbot answers questions. An agent takes action. Can an AI agent run part of my business? Technically yes, for defined goals with defined access. The more useful question is whether it should yet. An agent deployed before the leadership has learned to work with AI will efficiently run a part of the old business, inside pre-AI assumptions, and never surface the new opportunities that the learning curve reveals. What is the difference between an AI agent and a chatbot? A chatbot responds to prompts. An agent pursues a goal independently: it plans, uses connected tools and services, and completes multi-step work without being steered at each stage. What are the risks of using AI agents in business? Agents act on the goal as written rather than as intended, so vague objectives produce confident errors. Their reach is defined by the access granted, making boundaries a governance decision. And autonomous action does not transfer accountability: responsibility remains with the company, including under the EU AI Act. Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. If you are leading your organisation through this, I work with a limited number of senior leaders each quarter. Get in touch at Anglero.com. --- ## Everyone Says Get AI-Ready. What Does That Concretely Mean? URL: https://anglero.com/2026/08/03/what-does-ai-ready-mean/ Published: 2026-08-03 Anyone who answers this question in one sentence is doing you an injustice. Being AI-ready is not a slogan and it is not a purchase. It is three separate bodies of work: legal, technical and cultural. Get all three in place before your first employee starts, and you will do extremely well. Skip any one of them and you will pay for it, in fines, in wasted spend, or in a leadership group that quietly splits in two. 1. Legal: the part with penalties attached The EU AI Act is already in force, its transparency obligations arrive on 2 August 2026, and penalties reach EUR 35 million or 7 per cent of global turnover. The delayed high-risk deadlines changed none of that. Concretely, if you use AI in your business, internally or externally, you must be able to show that your employees have been made AI-literate, which in practice means keeping records of who was trained and on what. If you run an AI chatbot on your website, you must tell your customers they are talking to AI. There are further criteria, and there is a governance protocol behind all of it. The days of handing an employee a 20 dollar subscription and calling it done are over. It has to be managed, recorded and documented. Then GDPR, which is a separate law with separate fines. Being AI-ready here means knowing exactly where your data is processed and being able to prove it. Personal and client data can be processed outside your country, but only with a lawful transfer mechanism and the right contract in place, and your clients need to be told what happens to their information. This is why the tier and the vendor matter: on business and enterprise agreements the provider commits not to train on your content, and European data residency is available as a specific contractual feature you must request by name. Self-hosting removes the question entirely, but almost no normal company can justify the GPUs and the data centre capacity that requires. 2. Technical: readable by people, usable by machines The technical work is smaller than most vendors want you to believe, and it has four parts. Decide which tools are approved and on which tiers, so nobody is working on a personal account. Know where each of those tools processes data, so the legal section above is answerable. Define access boundaries before any agent touches a system, because an agent can only reach what you connect it to, and those permissions are a governance decision rather than an IT preference. The fourth part is the one companies miss entirely: your public surfaces have to be readable by machines. AI systems and agents now evaluate your company on behalf of buyers who never visit your website. Structured content, semantic markup and machine-accessible information decide whether you appear in that answer at all. This is measurable, not aesthetic. My own site scores 79 out of 100 on Cloudflare’s agent-readiness assessment, and the gap between a site that scores well and one that does not is invisible to humans and decisive for machines. 3. Cultural: the part that actually changes your company When you release AI into your company, every employee suddenly has access to the smartest colleague they have ever worked with. Available 24 hours a day, never taking a coffee break, reading a million pages a second, answering any question without judgement. That changes people. Give it to one person and that person changes. Give it to a team of fifty and that team will never work the same way again, and they will now be visibly different from every other team in your company. Give it to five hundred and the shift is exponential. This is already happening in your organisation, mostly in silence. Here is the risk nobody warns you about. If you adopt AI as a leader and the rest of your leadership group does not, your leadership style changes and theirs does not. That is a cultural fracture inside the room where decisions get made. The change is for the better, but until you have planned for it, it is heavy and complex. The hardest part of this was never the technology. Do the strategy in the right order Before you build an AI strategy to take on your competitor, build the AI strategy to launch it inside your own company. Legal handled. Technical decided. Culture prepared. All of it in place before the first employee starts. Then bring in the counsel that keeps you moving. Being AI-ready is more than hype. It is critical, and it is entirely achievable when it is done professionally and in the right sequence. Frequently asked questions What does it mean for a company to be AI-ready? Three things, not one. Legal readiness: EU AI Act obligations including employee AI literacy and chatbot transparency, plus GDPR compliance on where data is processed. Technical readiness: approved tools and tiers, known data locations, defined access boundaries for agents, and public content that machines can read. Cultural readiness: a plan for how AI changes how your people and your leadership group work. What does the EU AI Act require to be AI-ready? Employees must be made AI-literate, and you should hold records showing it. Customers must be told when they are interacting with AI, such as a website chatbot. Transparency obligations apply from 2 August 2026, with penalties reaching EUR 35 million or 7 per cent of global turnover. Can we use AI and still comply with GDPR? Yes. You need to know where the data is processed, hold the right contract with the provider, use a lawful mechanism for any transfer outside your jurisdiction, and inform clients about how their information is handled. Business and enterprise tiers with European data residency make this straightforward. Personal consumer accounts do not. Where should a company start with becoming AI-ready? With an internal strategy, before any competitive strategy. Settle the legal obligations, decide the tools, tiers and access boundaries, and plan for the cultural change, all before the first employee begins using AI in daily work. Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. If you are leading your organisation through this, I work with a limited number of senior leaders each quarter. Get in touch at Anglero.com. --- ## Is Our Company Data Safe If Employees Use ChatGPT? URL: https://anglero.com/2026/07/31/is-company-data-safe-with-chatgpt/ Published: 2026-07-31 The risk is the personal account, not the AI.   Your company data is safe if your people are on a business account. It is not safe if they are on personal ones, and that is where most companies are right now. The answer to this question is not a technology question. It is a contract question, and you can resolve it in a week. The real risk is the personal account, not the AI Here is the part almost nobody explains to leadership. On consumer tiers, the ones your employees pay for themselves, conversations are used to improve the models unless the user has gone into settings and switched it off. Compliance researchers describe personal consumer subscriptions as the dominant unmanaged risk in any organisation with knowledge workers, and it is not only about training. Free and personal paid tiers come with no data processing agreement, which means running client or employee personal data through them does not meet your obligations under GDPR in the first place. Now connect that to the previous question every leader asks: your employees are already using AI, quietly, on those exact accounts. That is the breach risk. Not the model. The absence of a contract. What changes on a business account Move the same people onto business, team or enterprise tiers and the position reverses. The major providers contractually commit not to train on business customer content, a data processing agreement is included, retention becomes configurable, and you gain administrative visibility over who is using what. The work your people were doing anyway is now covered by an agreement your legal team can point to. This is also why the budget structure matters. Paying for company seats instead of leaving people on personal plans is not only a cost-control decision. It is your data protection decision, made with the same signature. If your data must stay in Europe, the choice narrows Here is where it becomes concrete, and where I would tell any Nordic or European leader to pay attention. If you have promised clients that their information does not leave the EU, you must be able to prove it, and the providers differ significantly on this point. OpenAI offers European data residency for ChatGPT Enterprise and for API projects, with in-region processing. Microsoft’s Azure OpenAI service allows you to route processing through EU regions under Microsoft’s existing agreements, which is often the smoothest path for organisations already inside that ecosystem. Anthropic’s Claude, on its own first-party business tiers, is hosted in the United States, with EU-region processing available through AWS Bedrock or Google Cloud Vertex AI rather than directly. None of this makes any provider unsafe. It means residency is a specific contractual feature you must ask for by name, and verify in writing, rather than assume. Match the tier to the sensitivity. General knowledge work runs perfectly well on standard business seats. Client-confidential material, regulated data and anything you have made promises about belongs on the tier that gives you residency, zero retention and audit rights. Two laws, two fines Be precise about the exposure, because leaders routinely blur these. Data leaving the EU without a lawful basis is a GDPR matter, with penalties reaching EUR 20 million or 4 per cent of global turnover. The EU AI Act is a separate law with its own obligations and its own penalties, up to EUR 35 million or 7 per cent. They stack. That is the double loss: you lose control of the data, and you are fined for the failure that let it happen. Communicate at every stage The plan itself is straightforward, and the communication around it is what earns you the credit: Proceed carefully. Decide the tiers and the residency requirement before rolling anything out. Communicate before you start. Tell employees what is changing and why the company is paying for proper accounts instead of leaving them exposed on personal ones. Communicate while you implement. Train people on what belongs in which tool. Communicate to the market afterwards. Tell clients where their data lives and what protects it. Very few companies have done this properly yet, which is precisely the opportunity. Do it and your board sees a leader who moved before being asked, your legal team gets an answer they can defend, your employees stop hiding, and your clients hear something most of their suppliers cannot say. That is one board question you never have to fear again. Frequently asked questions Is company data safe if employees use ChatGPT? On a company business, team or enterprise account, yes: the major providers contractually commit not to train on business customer content and include a data processing agreement. On personal consumer accounts, no. Those conversations may be used for model improvement unless the user opts out, and they carry no processing agreement at all. Does ChatGPT train on my company’s data? Not on business and enterprise plans or the API, where providers contractually exclude training on customer content by default. Consumer tiers are different: personal subscriptions have historically defaulted to using conversations for model improvement unless the individual changes the setting. Can I keep our AI data inside the EU? Yes, but it is a specific contractual feature you must request and verify. European data residency is available on OpenAI’s enterprise and API offerings and through Azure OpenAI’s EU regions. Anthropic’s first-party business tiers are US-hosted, with EU-region processing available via AWS Bedrock or Google Vertex AI. What are the fines if employee AI use breaches data rules? Two separate regimes apply. GDPR penalties for unlawful processing or transfers reach EUR 20 million or 4 per cent of global turnover. The EU AI Act carries its own obligations with penalties up to EUR 35 million or 7 per cent. A single failure can trigger both. Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. If you are leading your organisation through this, I work with a limited number of senior leaders each quarter. Get in touch at Anglero.com. --- ## How Much Should a Company My Size Spend on AI? A Formula, Not a Percentage URL: https://anglero.com/2026/07/30/how-much-should-company-spend-on-ai/ Published: 2026-07-30 Budget AI from headcount, not from revenue.   Stop asking for a percentage of revenue. Anyone who gives you one is guessing. The honest answer to “how much should a company my size spend on AI” is a formula: your headcount on the right subscription tiers, plus a small API budget for the few who genuinely need it. That produces a number your CFO can predict every single month, and it is based on how the AI companies actually price their products. The two costs almost every leader mixes up Before any budget makes sense, you need one distinction. The frontier AI companies charge in two completely separate ways. First, subscription plans, paid per person per month: USD 20, 100 or 200, each tier carrying more usage than the last. Second, API costs, paid per token consumed, with no monthly ceiling. There is no overlap between the two, and a budget built on API tokens behaves nothing like a budget built on seats. Most companies that lost control of their AI spend never made this distinction. Then the budget cuts arrive, and the wrong thing gets cut. The subsidy hiding in the subscription tiers Here is what most leadership groups have not noticed. The subscription plans are heavily subsidised, and the evidence is public. OpenAI’s CEO admitted openly that the company loses money on its USD 200 plan because people use it far more than expected. In June 2026, the research firm SemiAnalysis bought every OpenAI and Anthropic subscription tier and ran them to their limits: a fully used USD 200 ChatGPT Pro plan represents up to USD 14,000 in API-equivalent usage, and the USD 200 Claude Max plan around USD 8,000. The higher tiers carry proportionally more subsidy, not less. Read that as a buyer, not an analyst: for an employee who uses AI seriously every day, the USD 100 or USD 200 subscription is one of the most mispriced products in enterprise software, in your favour. The API, by contrast, is unsubsidised. For the same amount of heavy work, it will cost you multiples more. In a gold rush, knowing the real price of the shovels is the whole game. The hybrid model that keeps your CFO calm So structure it this way. Put 80 to 90 per cent of your people on subscription plans, tiered by real usage: the USD 100 or USD 200 tier for daily heavy users, the USD 20 tier for occasional ones, and no seat at all for someone who will not use it, because an unused seat is where the subsidy logic stops working. Put the remaining 10 to 20 per cent, typically developers and anyone building AI into your products and systems, on the API, because embedded and automated work cannot run through a subscription seat. The arithmetic becomes trivial. Fifty employees on the USD 200 plan is USD 10,000 a month, USD 120,000 a year, known in advance. Mix tiers and it drops further. The API pool is the only variable component, and it is small enough to watch weekly. The subscription plans cover 80 to 90 per cent of what your business people will ever need, and your best people will extract value from those seats faster than you expect. For the few who need more, solve it case by case with your team instead of opening the treasury. Why a formula beats a percentage This structure gives your CFO a predictable number, your IT director a controllable perimeter, and you a budget that cannot be consumed in three months by enthusiasm. It is not total control, and it should not be: the point is a level of control that removes surprises while your organisation learns. That is how you buy the maximum benefit of AI at close to the minimum price, regardless of the size of your company. Frequently asked questions How much should a company spend on AI? Budget from headcount, not revenue. Put 80 to 90 per cent of employees on subscription plans tiered by usage, typically USD 20 to 200 per person per month, and give the 10 to 20 per cent who build with AI a separate, monitored API budget. A 50-person company runs roughly USD 60,000 to 120,000 a year on seats, known in advance. Should employees use AI subscriptions or the API? Subscriptions for almost everyone. The subscription tiers are heavily subsidised: independent testing in 2026 showed a USD 200 plan can represent USD 8,000 to 14,000 in API-equivalent usage. The API is unsubsidised and belongs only with developers and systems that embed AI into products, where a subscription seat cannot do the work. Are the USD 100 and USD 200 AI plans worth it for businesses? For daily users, yes, and they are arguably underpriced. OpenAI’s CEO has publicly said the company loses money on its top plan. The higher tiers carry proportionally more usage subsidy than the entry tier. The exception is an employee who rarely uses AI, for whom a cheaper tier or no seat is the right call. How do I stop my company’s AI budget from exploding? Separate the two cost models. Cap the predictable side by putting most people on fixed monthly subscription seats, and confine the variable side to a small API pool that IT reviews weekly. Companies that blew a year’s budget in months almost always ran uncontrolled API spend with no ownership. Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. If you are leading your organisation through this, I work with a limited number of senior leaders each quarter. Get in touch at Anglero.com. --- ## The Word Was Delay URL: https://anglero.com/2026/07/29/eu-ai-act-norway-what-changed/ Published: 2026-07-29 The AI Act deadline moved sixteen months. The budget conversation did not.   The AI Act omnibus that came into force on 27 July 2026 moved a deadline, not the work. Obligations covering AI used in recruitment, performance management and credit decisions now apply from December 2027, the transparency rules still apply from 2 August 2026, and in Norway the regulation is not yet in force at all. That combination is why “delay” is the wrong word for what happened this week. What actually changed on 27 July Regulation (EU) 2026/1744, the Digital Omnibus on AI, was published in the Official Journal of the European Union on 24 July 2026 and came into force three days later. The compressed timetable was deliberate, because the date it amends was only days away. Two things moved. High-risk obligations for the standalone systems listed in Annex III now apply from 2 December 2027. High-risk obligations for AI embedded in regulated products under Annex I now apply from 2 August 2028. Two things did not move. The transparency duties, the ones requiring people to be told when they are dealing with a machine and requiring AI-generated content to be marked, still apply from 2 August 2026. And the prohibitions that have applied since February 2025 remain untouched, carrying the highest penalty tier in the Act. Almost every summary I read reported the first half and skipped the second. Why Annex III is the part that matters to most companies Annex III is not an exotic list. It covers AI used in recruitment and candidate selection, in performance management and task allocation, in credit scoring and insurance pricing, in education and examinations, and in biometrics. The ordinary company is a deployer, not a provider Most companies do not build AI systems. They buy them and use them, which makes them deployers rather than providers, and the obligations are lighter. Lighter is not the same as absent. A deployer still has to use the system as instructed, keep competent human oversight, and tell employees before an AI system that affects them is put to work. There is one line worth knowing about. A company that white-labels a system, substantially modifies it, or repurposes it for something it was not built for can become a provider, with the full obligation stack attached. Organisations fine-tuning models cross that line without noticing. December 2027 is not a distant date. It is a budget line. This is the part I have not seen written down anywhere. Most companies set their 2027 budgets between September and November of this year. Whatever this work costs has to be argued for in a document being drafted within weeks, by people who have just been told the deadline moved sixteen months. Anyone who has sat in a budget meeting knows what happens to a line item whose deadline just receded. It gets deferred to the following cycle, and the following cycle is the one where the deadline is real. The Norwegian position, which is widely misread Norway is not a member of the European Union. The AI Act is not law in Norway today. It is EEA-relevant and awaits incorporation into the EEA Agreement, and the Norwegian implementing law, the KI-loven, has slipped, partly because the EEA adaptations are still being negotiated and partly because the omnibus changed the text under negotiation. Nkom is to be the coordinating supervisory authority. Some leaders will hear that and relax. Three reasons not to. Norwegian companies selling into the European Union are reached anyway. Nordic groups with Swedish, Danish or Finnish subsidiaries are reached directly in those jurisdictions. And using AI to screen candidates or score employees is already governed in Norway today, through the Personal Data Act and through the long-standing rules on control measures in the workplace. None of that was waiting for Brussels. The question underneath all of it Every date above is a legal question, and legal questions have owners. The question that decides whether any of this is manageable is not legal at all. Can you say, without asking anyone, which AI systems are running in your company, which of them touch a decision about a person, and who owns each one by name? Most leaders I put that to cannot. Not through carelessness. Because nobody was ever given the job. An inventory is unglamorous work that no one volunteers for, and it is the foundation everything else in the Act sits on. It is also the cheapest it will ever be to build, because doing it under a deadline costs several times what doing it calmly costs. I have written before that the delay is not the reprieve your board thinks it is, and that it functions more like a stopwatch than a break. This week’s regulation makes both of those more true, not less. Two related things sit close to this one. The inventory problem is inseparable from the AI your employees are already using without telling you, because a register that omits shadow usage is not a register. And if you would rather not be asked about any of this without warning, it is worth knowing which board questions can actually hurt you. If you suspect you are behind, arriving late is only a disadvantage if you stay still. Where this leaves you Not a crisis. Not a reprieve either. A gap that is currently cheap to close and will not stay that way, sitting in a company where nobody has been asked to close it. Frequently asked questions Was the EU AI Act delayed? Partly. Regulation (EU) 2026/1744 moved the high-risk obligations for Annex III systems to 2 December 2027 and for Annex I embedded systems to 2 August 2028. The transparency obligations still apply from 2 August 2026, and the prohibitions in force since February 2025 were not changed and carry the highest penalty tier in the Act. Does the EU AI Act apply in Norway? Not yet. Norway is in the EEA rather than the European Union, so the regulation must first be incorporated into the EEA Agreement and implemented in Norwegian law through the KI-loven, which has been delayed. Norwegian companies selling into the European Union are still reached, Nordic groups with subsidiaries in EU member states are reached directly there, and AI used in recruitment or employee monitoring is already governed in Norway by the Personal Data Act and by existing rules on workplace control measures. What applies from 2 August 2026? The transparency obligations. People must be told when they are interacting with an AI system, synthetic and deepfake content must be labelled, AI-generated content must be machine-readably marked, and the use of emotion recognition or biometric categorisation must be disclosed. Systems already on the market have until 2 December 2026 for the marking requirement. What should a board do before the 2027 budget is set? Ask for an inventory. A single list of every AI system in use, which ones touch a decision about a person, and a named owner for each. It is the input every later decision depends on, it costs very little to produce now, and it is the one thing that cannot be bought quickly once a deadline is close. Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. Read more about Thomas. If your leadership team needs to get clear on where it actually stands with AI, that work starts here: The Reset. --- ## My Employees Are Secretly Using AI. What Do I Do About Shadow AI? URL: https://anglero.com/2026/07/28/what-to-do-about-shadow-ai/ Published: 2026-07-28 Shadow AI is a culture problem before it is a security problem.   Do not punish it. Shadow AI exists because your employees are afraid of you. They are using AI to do their work better and faster, and they are hiding it because they believe they will get in trouble if anyone finds out. That is not a security problem first. It is a culture problem, and the fix is a clear policy, a paid subscription and a leadership group that stops being the enemy. Why shadow AI exists Employees do not hide tools that make them look good in a company that celebrates them. They hide tools in companies where the culture does not visibly support AI, where nobody has said out loud that using it is allowed, and where leadership might treat it as cheating. Your best people have already transformed how they work, in silence. The silence is the part you created. The fix: pay for it, openly Put three things in place. A clear, open policy that says employees are allowed to use AI. A budget that pays for their subscriptions, the 20, 100 or 200 dollar plan, on company-approved accounts. And a culture that visibly supports it from the top. The subscription is the masterstroke, and the psychology is simple: employees love it when the company pays. It is the same pleasure as flying on the company’s ticket. A large share of your shadow users, currently paying out of their own pockets, will happily surface the moment you offer to cover the plan they are already on. And go one step further than permission: put a reward policy in place. Tell your people you are looking for great ideas, for uses of AI nobody in leadership has thought of, for everything the company could do better. Now the behaviour you were about to punish becomes the behaviour you are actively harvesting. Why you will never fully eliminate it Be honest with yourself about the ceiling. I have seen employees keep a personal subscription and use it on their corporate PC while connected through their own mobile phone’s data plan, precisely so that nothing ever touches the company network. Some companies respond with proxies and blocked URLs, and lose anyway, because every employee carries a private, unblockable connection in their pocket. You will reduce shadow AI significantly with policy, payment and culture. You will not get it to zero, and chasing zero through surveillance costs you the exact trust you are trying to build. Governance is teaching, not blocking Reduction alone is not the goal. Quality is. Clients have started complaining about work that is obviously AI-written, and that is where real governance begins: an internal team that trains people, provides examples and teaches the difference between using AI in client work and sounding like AI in client work. It is allowed, and there are ways it should be done. This is not optional culture-building either: the EU AI Act’s duty to prepare your employees for AI is already in force, so the company that governs by teaching is also the company that is compliant. Start with the apology, not the anger When you discover shadow AI, do not start by being angry at your people. Start by saying the true thing: we did not have the policies and governance in place, and that is on me as the leader. The limitation was never the employees. Then plan, put the structure in place, and grow from there. Your employees save money and work in the open. You gain visibility, control and a map of your most creative AI talent. And your clients get better work, which is the point of all of it. Frequently asked questions What is shadow AI? Shadow AI is employees using AI tools for their work without the company’s knowledge or approval, typically on personal subscriptions and private accounts. It emerges when people believe they will get in trouble for using AI, so they hide the productivity gains instead of sharing them. Should I ban employees from using ChatGPT or Claude? No. Bans and URL blocking fail in practice, because employees route around them, including using personal subscriptions over their own mobile data so nothing touches the company network. The effective response is an open policy, company-paid subscriptions on approved accounts, and training in how to use AI well. How do I get employees to admit they are using AI? Remove the fear and add a reward. Announce that AI use is allowed, offer to pay for their subscription plans, and ask openly for their best AI ideas and discoveries. Most hidden users surface quickly when the company covers the plan they were paying for themselves. Does shadow AI create legal risk under the EU AI Act? Unmanaged AI use sits outside your governance, and the EU AI Act’s obligation to ensure employees are prepared for AI is already in force. Bringing shadow use into an open, trained, company-approved structure is both the cultural fix and the compliance fix. Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. If you are leading your organisation through this, I work with a limited number of senior leaders each quarter. Get in touch at Anglero.com. --- ## What Questions Will My Board Ask About AI? The Three That Can Hurt You URL: https://anglero.com/2026/07/27/board-questions-about-ai/ Published: 2026-07-27 Answer the three hardest board questions first, and the ambush never happens.   You will find lists of twenty questions your board might ask about AI. Only three can hurt you, and the answer that keeps you safe in all three is the one no CEO wants to give: yes. Answered voluntarily, in the right order, those three questions stop being an ambush and become the strongest board meeting you have run in years. And the preparation starts before the meeting, at lunch. Before the meeting: take the difficult director to lunch If your board has a new member, often the younger one, brought in precisely to ask the tough AI questions, do the old-school thing. Invite them to lunch. Go to their office. Ask them directly: what are your expectations on AI? What should I be looking out for? How do you see it? Get to know how they think and how they are wired. Their job is to help the company, and so is yours. If there are synergies in that conversation, they will tell you exactly what they will be looking for in the meeting, which is what you wanted to know anyway, and you gain a colleague on the board instead of an opponent. Human interaction is the one preparation tool that never gets replaced. Open with the questions you fear most Here is the sequencing secret. The questions you should prepare hardest for are the ones you absolutely do not want to answer, and you should open the meeting with them, unprompted. Eliminate the most embarrassing material first, before anyone can weaponise it, and the entire meeting changes character: the ambush never happens, the room relaxes, and you spend the remaining time on what you are proud of. Boards can smell the fear a CEO carries into an AI discussion. Removing it yourself, in the first ten minutes, is the whole trick. Question one: “Have we failed with our AI strategy?” Open with yes. Yes, we failed. Show exactly where, what you could have done better, and what you are changing. A board that hears its CEO name the failure before anyone else does stops worrying about what is being hidden, because evidently nothing is. Falling on your own sword this way beats every scapegoat structure ever designed, because it leaves you standing. Question two: “Have we overspent on AI?” Yes again, and show the exact numbers. Then show what exists now that did not exist when the money was spent: governance and policies that control the next round of spending. And add the line that turns the whole answer from defence into offence: the governance you have put in place is built to be compatible with the EU AI Act, which means you have not just fixed the spending, you have protected the company from fines reaching EUR 35 million or 7 per cent of global turnover. With the Act’s transparency obligations arriving on 2 August 2026, you may gently note that this is a question the board’s own legal voice might have raised with you six months ago. Asked or not, you prepared. That lands. Question three: “Is our AI strategy world class?” This is the trap question, and the one place where a blunt answer hurts you in either direction. Do not claim world class. Do not confess to a bad strategy. Say: here is our strategy. It is version 1.0, and we learned from it. Then move immediately to what you learned, how it has been improved, where the benefit will come, and, most importantly, in what timeframe and at what cost, in money and in the resources and people it will take. Boards do not need world class. They need a CEO who knows precisely where the strategy stands and what it costs to advance it. Then the easy part, and how to end With the heavy three cleared, the rest of the agenda is yours: the projects being implemented, the clients you are using AI with, the work you are proud of. End the meeting on the upside: where the company is going with AI, how it benefits, why clients are happier. The measure of success is simple. The board should leave the room less concerned about the company’s AI position than when the meeting began, and more convinced than ever that they chose the right CEO. That is the CEO still standing in 2027. Frequently asked questions What questions do boards ask CEOs about AI? The three that matter: have we failed with our AI strategy, have we overspent on AI, and is our strategy as good as our competitors’. Everything else on a board agenda about AI is easier, and a CEO who answers those three honestly and first controls the entire meeting. How should a CEO answer a board question about a failed AI strategy? With yes, voluntarily, before being asked. Name where it failed, what could have been done better, and what is changing. Boards forgive experiments that failed. They do not forgive discovering a failure the CEO tried to hide. How do I prepare for a board member who is an AI expert? Meet them before the meeting. Invite them to lunch, ask their expectations of the company’s AI position and what they will be looking for, and learn how they think. Their mandate is to help the company, and a pre-meeting conversation converts the toughest questioner into an ally. Should I tell my board our AI strategy is world class? No, and do not call it bad either. Present it as version 1.0 that the company learned from, then show the improvements, the expected benefit, the timeframe and the full cost in money and people. Precision about where the strategy stands builds more board confidence than any superlative. Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. If you are leading your organisation through this, I work with a limited number of senior leaders each quarter. Get in touch at Anglero.com. --- ## We Spent Money on AI and Got Nothing. Why Did Our AI Project Fail? URL: https://anglero.com/2026/07/24/why-did-our-ai-project-fail/ Published: 2026-07-24 The failure was leadership, not the technology. But somewhere in the organisation, that spend quietly succeeded.   Your AI project failed for a reason that outranks every technical explanation: leadership never owned it. There was no governance, no rails, no strategy beyond opening the keys to the company treasury and letting people spend with no time limit. But before you present that failure to your board, ask the second question, the one almost nobody asks: did it really fail everywhere? Because somewhere in your organisation, that money quietly succeeded, and your job tonight is to find out where. The failure was leadership, not the technology Be honest about the sequence. You read the hype: AI changing the world, changing your company, new revenue, innovation, everything. So you opened the coffers and let people do whatever they wanted. Now ask yourself: is the failure really on your employees, or is it on you for not putting governance, policies and ownership in place? To do that, you needed to understand AI, and someone senior needed to own it. Neither happened. The constraint on what AI delivers is rarely the technology. It is the leader’s own thinking, and blaming the people who spent the money you handed them without rails is the oldest move in the book. But did it really fail? The famous MIT finding that around 95 per cent of generative AI pilots produce no measurable return gets quoted in board meetings everywhere. It tells you that you failed. It does not tell you where to look next. Look at where the money actually went. There were islands of people in your organisation who spent far more on tokens than anyone else. A few spent it on private projects, because with no governance, that is what happens. But most of the heavy spenders spent it on ideas: experiments on internal projects, what-if scenarios on work they had been carrying for months or years, future projects, future scenarios for future clients. Those are not failures. Those are the jewels. That is exactly what you were hoping to breed when you approved the budget, and if the only thing you can see is that they blew through a year’s money in three or four months, you are missing what you actually created. I have been hard on companies that spent a year’s AI budget in four months, and I stand by it: the spend was undisciplined. But undisciplined is not the same as worthless. The jewels hiding in your organisation A spreadsheet does not reveal everything. It reveals the numbers. What it will not show you is the people whose minds changed. Somewhere in your company are individuals who now see their job differently, see opportunities differently, see the world differently. People who stayed late, some on the clock and some on their own time, because they learned to use these models better than anyone around them. Some of them are introverts whose minds are exploding with possibilities nobody has asked them about. Your best people have already transformed how they work, mostly in silence. And here is the uncomfortable one: some of those people may not even be your employees. A consultant you hired, working inside your project, may now understand your company and how to multiply its revenue better than your own leadership group does. When the engagement ends, that knowledge walks out the door. Which division are these people in? Who are they? One woman in a basement server room may hold enough answers to justify a meaningful share of the budget you are about to write off. The five questions to answer before tomorrow’s meeting Where are the returns on our investment, beyond the spreadsheet? Why did the project fail where it failed? Where in the organisation did it succeed? Who benefited, who changed, and who quietly became our best AI talent? How do I extract and amplify what those people now know across the rest of the organisation? Walk into the board meeting with those answers and the story changes. It was not a complete failure. You were laying foundation stones and fertilising the soil for the future of the company. It simply could have been done far more strategically, and the second attempt, done with governance and ownership, is where the returns live. Frequently asked questions Why do most AI projects fail? The most common cause is not the technology. It is the absence of leadership ownership: no governance, no rails, no strategy and no accountable owner, combined with a budget released faster than the organisation could spend it wisely. Is the MIT statistic that 95 per cent of AI pilots fail accurate? The MIT research found that around 95 per cent of generative AI pilots produced no measurable profit-and-loss return. The number is widely cited, but it measures returns in spreadsheets. It does not capture the individuals inside organisations whose capability and thinking were transformed by the same spend. What should I look for after a failed AI project? Find the heavy users. Identify the people and groups who spent the most on the technology and examine what they built: experiments, what-if scenarios, future client work. Then identify whose mindset changed. Those individuals are the real return on the investment. How do I explain a failed AI investment to my board? Do not present only the loss. Present where the project succeeded, who in the organisation benefited, what capability now exists that did not exist before, and the governance and ownership structure that will direct the second attempt. Boards forgive experiments. They do not forgive leaders who learned nothing from them. Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. If you are leading your organisation through this, I work with a limited number of senior leaders each quarter. Get in touch at Anglero.com. --- ## My Competitor Says They Are AI-First. Should I Panic? URL: https://anglero.com/2026/07/23/competitor-ai-first-should-i-panic/ Published: 2026-07-23 An AI-first announcement is market research your competitor paid for and published voluntarily. No, do not panic. Your competitor just revealed their card. An AI-first announcement hands you, for free, most of the information you need to move ahead of them. It is to your benefit, not your demise. The announcement is free market intelligence When a competitor announces they are AI-first, I read it the way I used to read a rival’s press release at IBM. Not for the claim, which is written for the market. For the detail underneath it, which is written, unintentionally, for me. Read that press release again, slowly. To claim they are AI-first, your competitor has to show their working. Somewhere in the announcement and the coverage around it, you will find: Which products or services are now AI-enabled What they changed internally to call themselves AI-first Who they partnered with What software and licensing agreements they bought How much they are spending That is not a threat. That is market research your competitor paid to produce and published voluntarily. It is exactly what you need to build the strategy that disrupts them. Expensive is not the same as first Suppose the announcement reveals a EUR 100 million licensing agreement with a frontier AI vendor. That is EUR 100 million in token usage. It is a strategy. It is not proof of a good one. Just because a company spends EUR 100 million or EUR 500 million does not mean the implementation works. It can be a EUR 500 million mistake. Calling your company AI-first does not make you an AI company. Being first to spend the most money on AI does not make you AI-first. It makes you one of the most expensive AI companies in your market. The money always flows to the shovel sellers first, and an announcement about spend is usually an announcement about shovels. The trap you just avoided Here is what I have heard directly from companies over the past year. To be AI-first, leadership opened the expense account and told employees to build on AI. They spent a year’s worth of AI budget in three or four months. Now they are entering the latter half of 2026 with eight months of expenditure gone and no way to replace it. Very few companies have the balance sheet to absorb that, and the wave of AI budget cuts this summer is partly that bill arriving. Then there is the board conversation: how do you explain that the year’s money lasted four months, and to what benefit? Some of these companies also released employees with severance packages, only to hire them back weeks later at the same salary or higher, having paid them months or years of package money to leave. That reversal is now well documented: two in three companies that made AI-driven cuts are already rehiring. Being AI-first sounds good. Without the right strategy, it is one of the most expensive sentences a CEO can say. So ask yourself honestly: are you relieved you did not fall into that trap? Being second, with their map in your hands, is the stronger position, and arriving late is an advantage with an expiry date. What to do in the 48 hours after the announcement Do not respond with a louder press release. The loudest AI announcements usually signal the weakest strategies. Instead: First, analyse everything your competitor published against the checklist above. Treat it as market data. Second, sit with your AI advisor and build the counter-strategy: where their spend is theatre, where it is real, and where your money delivers more to customers for less. Third, prepare your own announcement for later, and make it state delivered results, not intentions. Let them own the promise. You own the proof. Your competitor being first is expensive. It has always been expensive, in every industry, in every technology wave. Their announcement is not your crisis. It is your briefing. Do not stress. Strategise, implement, succeed. Frequently asked questions My competitor announced they are AI-first. Should I be worried? Not immediately. An AI-first announcement forces your competitor to reveal their products, partners, spend and approach. That is free market intelligence you can build a counter-strategy on. Worry only if their delivery to customers visibly improves, not because of the announcement itself. Does spending heavily on AI make a company AI-first? No. Large licensing deals and big budgets prove commitment, not capability. A EUR 500 million spend can be a EUR 500 million mistake. Many companies that rushed to be AI-first spent a year’s AI budget in three or four months and entered the second half of 2026 with nothing left. How should I respond to a competitor’s AI announcement? Analyse the announcement as market data, build a counter-strategy with your AI advisor around where their spend is real and where it is theatre, and when you announce, announce delivered results rather than intentions. Is it bad to be second to AI in my industry? No. Being first is historically expensive in every technology wave. Being second with your competitor’s map in your hands, learning from their published mistakes and spending against a real strategy, is usually the stronger position. Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. If you are leading your organisation through this, I work with a limited number of senior leaders each quarter. Get in touch at Anglero.com. --- ## Can You Be Fired Over a Failed AI Strategy? The Excuses Are Gone URL: https://anglero.com/2026/07/23/fired-over-failed-ai-strategy/ Published: 2026-07-23 What ends careers is not the expensive experiment. It is the board finding out from someone other than you.   Yes, you can lose your job over a failed AI strategy, and in 2026 the odds have moved against you for one simple reason: the methods for hiding the failure have run out. The excuses are very few today, and the ownership now sits in one seat. Yours. The scapegoat plan, and why it stopped working Let me tell you what I have watched happen inside leadership groups. A lead group announces it has an AI strategy. What it actually has is a backup plan. The group knows the strategy is immature, they do not fully understand AI themselves, but they know they must have one. So they build the escape route into the plan itself: the person or team who wrote the strategy is positioned as the sacrificial lamb if it goes wrong. When it fails, that group is removed, the board is pleased that action was taken, and the CEO and the executives stay safe. I have been in the room when a company went looking for someone to blame. It is a quiet, orderly process, and it rarely lands on the person who actually made the decision. Everyone present knows that. Nobody says it. That play worked for years. In the latter part of 2026 and into 2027, it no longer does. AI has been on every agenda for so long that ownership of the strategy is no longer accepted as belonging to the CTO, the CIO or some unfortunate director in the IT department. It belongs to the CEO. Boards, investors and now regulators all read it that way. The EU AI Act moved the risk into your seat The reason the scapegoat stopped protecting anyone is regulatory. The EU AI Act is law. Its transparency obligations apply from 2 August 2026, the duty to prepare your employees for AI is already in force, and penalties reach EUR 35 million or 7 per cent of global turnover. The delayed high-risk deadlines have not softened any of that. Anything that can cost a company that kind of money is, by definition, board business. And board business lands on the CEO. Before, you simply needed to have an AI strategy. Now the regulator is effectively asking: did your strategy meet the criteria? Did you prepare your employees? Are you transparent with the market? The job has become twice as difficult, and the accountability for it has become impossible to delegate. What actually gets a CEO fired in 2026 Not the expensive experiment. Boards forgive experiments. What ends careers now is a short list: an AI strategy that ignores the criteria the EU AI Act measures you against, employees left unprepared while the company claims AI maturity, and a board discovering the gap from the outside rather than from you. CEOs already rank AI as their single biggest business risk. The ones who lose their seats are the ones who knew that and still handed the file to someone junior. And it rarely announces itself as a firing. More often, AI simply exposes a gap that was always there, and the decision that follows looks like a business decision rather than a reckoning. The way out is to be lifted, not covered Here is the opportunity inside the threat. The same AI that raises the bar can raise you with it. Internally, it lifts the company from the inside as every colleague works with it properly. Externally, it sharpens the questions that define your strategy: Which markets should we not be in, and which have we never addressed? Which products work, which do not, and which should exist but do not yet? Where do we stand against each competitor, honestly? Which geographies deserve investment? Which customer segments should we ignore, and which should we own? When an advisor helps you work through these with AI, something more valuable happens than the answers themselves: you start thinking this way on your own. That combination, AI-era strategy added to your own experience, is what makes a better CEO, a better leader, a better board member. It changes the question from how do I survive this to what will I be remembered for building. The CEO still standing and respected in 2027 is the one who built that counsel early. The excuses are gone. The tools are better than they have ever been. Choose the support that lifts your intelligence and your company, and the failed-strategy question stops applying to you. Frequently asked questions Can a CEO really be fired over a failed AI strategy? Yes, and in 2026 it is more likely than before. Boards now treat AI strategy as CEO-owned because the EU AI Act attaches direct financial liability to getting it wrong, with penalties up to EUR 35 million or 7 per cent of global turnover. Why can the CTO or CIO no longer take the blame for AI? Because regulators, boards and investors have stopped accepting delegated ownership. The scapegoat structure, where the team that wrote the strategy absorbs the failure, worked for years. Regulatory accountability and board attention now trace the responsibility to the CEO. What does the EU AI Act require a CEO to have done? A strategy that meets the Act’s criteria for how AI is used, employees prepared through the AI-literacy duty already in force, and transparency obligations that apply from 2 August 2026. Fines are set at a level that makes this board-level business. What separates the CEOs who get fired from the ones who get praised? The fired ones delegated ownership and let the board discover the gap from outside. The praised ones took ownership early, used AI and the right advisors to lift their own strategic thinking, and prepared their people before the regulator or a competitor forced it. Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. If you are leading your organisation through this, I work with a limited number of senior leaders each quarter. Get in touch at Anglero.com. --- ## Will AI Replace CEOs and Executives? No. It Will Expose Them. URL: https://anglero.com/2026/07/23/will-ai-replace-ceos-executives/ Published: 2026-07-23 The executive replaced in this era is rarely replaced by AI. They are replaced because AI made the gap visible.   No, AI will not replace CEOs, executives or boards today. It was built to complement them, and when implemented correctly, that is exactly what it does. What AI will do is expose the leaders who have not been delivering at the level they are paid to. That is a different question from the one you typed, but it carries the same outcome, unless you move first. The replacement stories are already reversing Yes, you have read the stories of CEOs who fired employees and pointed to AI as the replacement. Read what happened next. A February 2026 survey of HR professionals found that two in three companies that made AI-driven cuts are already rehiring, and 55 per cent of employers now say they regret those layoffs. Gartner expects half of all AI-attributed job cuts to be reversed by 2027. Replacement was never what this first generation of AI was built for, and many of those layoffs were never really about AI in the first place. When humanoid robots arrive at scale, we will have a different conversation. We are not there yet. The question underneath the question When a leader types “will AI replace me”, the real question hiding underneath is quieter and more uncomfortable: will AI expose me? Will it reveal that I have not been delivering at the quality and level I should have been? Different question, same outcome. The executive who gets replaced in this era is rarely replaced by AI. They are replaced because AI made their gap visible to everyone. The numbers behind this are brutal. In one global survey, 94 per cent of CEOs admitted an AI agent could provide equal or better counsel than a human board member, and 89 per cent said AI could produce a strategic plan as good as or better than at least one of their own executives. This is the fear CEOs will not say out loud to their boards, and it applies to every seat at the table, including board seats. Let me name the secret almost nobody admits. In many leadership groups sit executives who have nodded through reports for years without fully understanding them. Everyone assumes they understand. They do not, and until now the room protected them. AI ends that protection, in one of two directions. Used badly, it exposes you. Used well, it ends the faking privately, before anyone ever finds out. I have watched a capable executive get quietly exposed by AI, not replaced by it. Nobody fired them. The questions in the room simply got sharper, the papers got better prepared, and the gap they had carried for years stopped being invisible. That is exposure at the individual level. At company level it carries a harder edge, because a failed AI strategy now lands on the CEO, and the excuses that used to absorb it are gone. Stop asking “will I lose my job” and ask “how do I guarantee it” Asking AI whether you will lose your job does nothing except reaffirm your insecurity. It will not keep your job in the short term or the long term. Flip the question. Make AI your private mentor, consultant and advisor. Let it read the reports you have struggled with and teach you, in plain language, until you genuinely understand them. Then let it help you ask sharper questions in board meetings and leadership groups. One warning here: if you only recite the questions AI hands you, you are still the person this technology will eventually expose. Use it to close the gap for real, not to perform. The most powerful exercise I give leaders is this. Sit down with AI and be completely honest. Tell it: this is my job, this is what I actually do, this is what I do not do. Let it interview you. Then ask it: help me fill my gaps, make me complete in this role, and help me become a superior executive, the strongest voice on my board, the best CEO in my industry. That conversation is private, it costs almost nothing, and it is available to you tonight. The leaders still standing in 2027 will be the ones who built this counsel around themselves early. Boards are not spectators in this Every argument above applies to board members. A director who cannot interrogate an AI strategy, who signs off on spending they do not understand, is exactly as exposed as the executive faking their way through a report. The same private remedy applies: use AI to prepare, to test the papers before the meeting, to arrive as the sharpest person in the room. Your employees are already using AI to transform their own work. The question is whether their leaders are. And if nobody in the building is quite certain where the company actually stands, start with the three honest tests that tell you whether you are genuinely behind. Frequently asked questions Will AI replace CEOs and executives? No, not today. AI was built to complement leaders, and companies that used it for wholesale replacement are already rehiring. What AI does is expose executives and board members who have not been delivering, which produces the same outcome by a different route. Why are companies rehiring the employees they replaced with AI? Because replacement was the wrong model for this generation of AI. Surveys in 2026 show two in three companies that made AI-driven cuts are rehiring, and 55 per cent of employers regret the layoffs. AI handled the routine work but not the judgement underneath it. How can an executive use AI to protect their job? Be honest with it. Tell it what your job is, what you do and what you do not do, let it interview you, and then ask it to fill your gaps. Use it to genuinely understand the reports and decisions in front of you, not to perform understanding. Can AI replace a board member? In surveys, 94 per cent of CEOs say an AI agent could provide counsel equal to or better than a human board member. The directors who stay valuable will be the ones who use AI to prepare and interrogate, rather than the ones it quietly outperforms. Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. If you are leading your organisation through this, I work with a limited number of senior leaders each quarter. Get in touch at Anglero.com. --- ## Is Your Company Falling Behind on AI? How to Know, and Why Late May Be Your Advantage URL: https://anglero.com/2026/07/23/is-your-company-falling-behind-on-ai/ Published: 2026-07-23 Arriving late to AI is only an advantage if you move now.   You are behind on AI if your company has no working strategy, your employees are untrained or using AI in secret, and you have no plan for the EU AI Act. Everything else that looks like being behind is marketing noise. And if you are genuinely behind, that may be the strongest position you have held in years. The signals that do not count Most leaders judge whether they are behind by watching what other companies announce. By that measure, being ahead looks like this: the word AI added to every product and platform name, an AI strategy announced in press releases and social campaigns, and AI partnerships declared with clients and existing partners. None of it counts. Putting AI into an existing brand name does not make it AI. Announcing a partnership built on a thin AI feature is not a strategy. It is noise, and the loudest announcements often signal the weakest strategies. If the only thing separating you from your competitors is that you have not performed this theatre, you have not fallen behind. You have skipped the noise. And if a competitor has just declared themselves AI-first, read that announcement as intelligence rather than as a threat. The signals that do count Three honest tests tell you where you actually stand. First, does a working AI strategy exist, owned by leadership, connected to how the company makes money? Second, are your employees trained and using AI openly, or are they using it in secret on personal accounts while the official position sits in a slide deck? Third, do you have a compliance position on the EU AI Act, or has nobody read it? Those three tests measure the company. A harder question sits underneath them, and it is personal: whether AI is about to expose the leadership rather than replace it. If you fail all three, you are behind. Now let me tell you why that may be good news. I know the cost of being early. I paid it. In 2017 I founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster, one of the first AI-in-cancer-research institutions on a European cancer campus. It was early, and being early carried a cost that nobody warns pioneers about. We had to educate a market that did not yet have the words for what we were doing. New terminology, new ways of thinking, new ways of helping people, all of it explained from zero, year after year, at real expense. Only now is the market catching up to what that lab started. Pioneers earn the credit. They also carry the heaviest lift. Most companies should not want to be first, and the ones agonising about being last have usually never counted what first actually costs. You are late to a party that just got good Think of the best party you have ever been to. It rarely got interesting before midnight. Arriving late did not mean you missed it. It meant you walked in at the right time. AI in the enterprise has been that party. For the past few years it ran on models that were good at best, prone to mistakes, and nowhere near the reliability the marketing claimed. Companies that rushed in early paid for that gap: budgets released without direction, experimental projects, token bills, implementation fees, and consultants learning on the client’s money. Many of those companies are now cutting their AI budgets, which tells you how those early bets performed. Arriving now, you inherit three advantages they never had. The failures are documented, so you can learn what not to do from other companies’ expensive lessons. The frontier models are finally, in my view, worthy of enterprise work. And you can build your strategy once, on solid ground, instead of rebuilding it every time the technology lurches forward. I have sat with companies that were genuinely two years behind, and watched them overtake the ones who moved first. Not because they were cleverer, but because they built once, on ground the early movers had already tested and paid for. The advantage has an expiry date Here is where I must be honest with you, because this is where most reassurance turns into a trap. Lateness is only an advantage if you move now. Late and slow is fatal. The competitors ahead of you are refining what they built, and organisational learning compounds in a way you cannot buy later. And there is a second clock running that has nothing to do with competition. The EU AI Act is law. Its transparency obligations apply from 2 August 2026, and while the high-risk deadlines have moved to late 2027 and 2028, the penalties have not softened: up to EUR 35 million or 7 per cent of global turnover. The obligations follow what your AI does, not how large your company is. An AI strategy is no longer something you build because your competitors have one. It is something you build because doing it wrong is now a regulated, finable event. So the position is this. You did not pay the pioneer’s bill. You did not burn the experimental budgets. You arrive as the models come good and the rulebook becomes clear. Before any of it, do the honest internal reckoning first, then build the strategy, train your people properly, and move. Your lateness is a gift. It stays a gift only for as long as you treat it like one. Frequently asked questions How do I know if my company is behind on AI? Ignore the marketing signals. You are behind if you have no working AI strategy owned by leadership, your employees are untrained or using AI only in secret, and you have no plan for the EU AI Act. AI in product names and press releases tells you nothing. Is it too late to start with AI in 2026? No. Companies starting now skip the expensive experimental years, learn from other companies’ documented failures, and deploy on frontier models that are finally strong enough for enterprise work. The advantage disappears only if you keep waiting. What does it cost to be too early to AI? Years of educating a market that lacks the vocabulary for what you are building, and heavy spending before the technology matures. I lived this founding the IBM Watson AI Lab for Cancer in 2017. Pioneers earn the credit, and they pay for it. Can my company be fined under the EU AI Act? Yes. The Act is law, transparency obligations apply from 2 August 2026, and penalties reach EUR 35 million or 7 per cent of global turnover. The delayed deadlines for high-risk systems do not remove the liability. Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. If you are leading your organisation through this, I work with a limited number of senior leaders each quarter. Get in touch at Anglero.com. --- ## The Limitation Was Me URL: https://anglero.com/2026/07/15/the-limitation-was-me-2/ Published: 2026-07-15 Thomas Anglero on the leadership habit that quietly caps every AI initiative.   The biggest constraint on what AI delivers inside a company is rarely the technology. It is the leader’s own thinking, and I learned that by watching it happen to me. The realisation that would not go away I run my own operation on AI agents every working day. More than half of my leadership team is AI, working around the clock, and somewhere in the first weeks of running it that way, a realisation landed that I have not been able to shake since: my thinking is the limiting factor in how I use AI agents. I have never had access to resources this powerful, that do so much, so fast. And still the results were smaller than they should have been. Not because the AI underdelivered, but because my requests were too small. I was under-asking when the AI could over-deliver. That is an uncomfortable sentence to write about yourself. It is also the most useful diagnostic I can offer any leader right now, because the same pattern sits inside almost every company I meet. The habit that caps everything Leaders scope AI work the way they scope human work: one task, one deliverable, one deadline. The habit is invisible because it has been correct for an entire career. With AI agents, it quietly caps everything. You get back exactly what you asked for, and you never discover what you could have asked for. Scale that habit up and you get the pattern I have written about before: budgets released without anyone saying where to dig. The company version of under-asking is spending on AI without ever demanding what the technology is actually capable of returning. The constraint travels upward intact, from how one leader phrases one request to how a whole organisation frames its entire AI investment. Meanwhile, somewhere below in the organisation, your best people are already asking more of AI than you are, usually without telling anyone. The gap between what leadership requests and what the technology can deliver is being discovered inside your company right now. The only question is whether the leadership is part of that discovery. Becoming a different type of leader Working with agents forced me to become a different type of leader. New framing of communication. New scoping of time and deliverables. A new sense of what one request is allowed to contain. The nearest picture I can give you is this: imagine a hungry, brilliant graduate who can duplicate themselves in a moment whenever the work demands it. Now imagine briefing that graduate with the same care you brief a tired department. That is the gap, and it belongs to the leader, not the tool. Leading AI agents is a different job at a different altitude, and nobody arrives in it fluent. Where the quieter work starts So before anyone in your company announces that agents are running anything, there is quieter work to do first, and it starts at the top: each member of the leadership group takes on one small AI project of their own. This is not something an online course installs in you. You grow into it. You mature into it, as a person and as a leader. The leaders who do this stop asking whether AI works. They start asking what they have been failing to ask of it. That is the moment the whole conversation inside a company changes. Frequently asked questions Why do AI projects underdeliver even when the technology is good? Because the requests are too small. Leaders scope AI work the way they scope human work, one task and one deliverable at a time, so the output is capped by the ask rather than by the technology’s actual capability. What does under-asking AI mean? Under-asking is briefing AI with requests sized for human constraints. You get back exactly what you asked for and never discover what you could have asked for, which makes the results look like a technology limit when they are a leadership habit. What new skills does leading AI agents require? A new framing of communication, a new scoping of time and deliverables, and a new sense of what a single request is allowed to contain. It is a different job at a different altitude, and leaders grow into it rather than learn it from a course. How should a leadership team start with AI agents? Each member of the leadership group takes on one small AI project of their own before the company announces anything larger. The personal experience is what produces the shift in how each leader asks, decides and delegates. If you are leading your organisation through this, this is the kind of work I do with a limited number of senior leaders each quarter: The Reset. Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. Listen to the Clarity at the Top podcast on Spotify. Enquiries: Anglero.com --- ## The Phone Book of the Agentic Internet Is Being Written. I Am the First Speaker in It. URL: https://anglero.com/2026/07/13/first-keynote-speaker-mcp-registry/ Published: 2026-07-13 Thomas Anglero, first professional keynote speaker in the official MCP registry You probably assume your next client will find you the way the last one did: a search, a referral, a look at your website. Quietly, that assumption is expiring. Executives have started asking their AI instead, and the AI answers from what it can read and verify about you. Most professionals are invisible to that process. Last week, I decided not to be, and it produced a strange sentence to write: I am now the first professional keynote speaker in the official MCP registry. The Model Context Protocol registry is best understood as the phone book of the agentic internet. It is the official directory that tells AI systems such as Claude, ChatGPT and Gemini which websites they can interact with directly: not just read, but work with. On 10 July 2026 at 12:30 UTC, the entry com.anglero/speaker went live. Of more than 16,000 servers in that registry, roughly fifteen represent individual professionals. Exactly one is a keynote speaker. I checked the entire registry before writing that sentence, because a claim like this deserves hostile fact-checking, and it survives it. 16,000+servers in the registry15individual professionals1keynote speaker What did I actually build? Over one weekend, I made anglero.com fully agent-interactive. An AI agent can now retrieve my biography, my speaking topics and my booking process, verify my track record, and submit a speaking inquiry directly into my pipeline. Every machine-submitted inquiry arrives transparently labelled as sent by an agent. I designed it, built it and registered it myself. Now the part most announcements leave out. If you book speakers for a living, nothing changes for you today. AI platforms do not yet connect to registered websites automatically; a human still has to connect their assistant to my server manually, or simply use the inquiry form like everyone else. Anyone telling you this is a revolution for buyers, this week, is overselling it, and I refuse to do that. The honest description is smaller and, I think, more interesting: the phone book of the agentic internet is being written right now, and I chose to be the first entry in my profession rather than a footnote explaining why I waited. Because the connection will become automatic. Every major AI platform is building toward agents that consult the registry on their own. When that switch flips, and it is a when, the websites already listed are the ones agents find on day one. The window between “technically possible” and “everyone does it” is where positions are won, and it is open now, while the registry holds sixteen thousand entries instead of sixteen million. I have lived this exact moment twice before. In 1993, I was making phone calls over the internet before Skype existed, and people asked why anyone would need that. In 2017, I founded the IBM Watson AI Lab for Cancer at Oslo Cancer Cluster, the first institution of its kind on a European campus, and people asked what AI had to do with medicine. Nobody needed internet calls in 1993 either. Being early looks unnecessary right up until it looks obvious. So here is the question I would put to you, leader to leader. When an AI agent, acting for your next customer, goes looking for a business it can work with directly, will it find yours? Your answer today is almost certainly no. Mine was too, until last weekend. The technical work took two days. The decision to do it before it was obvious is the only part that was hard, and it is the same decision your organisation will face, on everything agentic, again and again over the next two years. The registry listing is public, the server can be inspected at anglero.com/mcp, and the full press release with verifiable references is at anglero.com/press. If you are leading your organisation through this, I work with a limited number of senior leaders each quarter. Get in touch at Anglero.com. For more on this, see Strategic AI Advisor Referrals, part of the Strategic AI Advisor guide. Frequently asked questions What is the MCP registry? The Model Context Protocol registry is the official directory that tells AI systems such as Claude, ChatGPT and Gemini which websites they can interact with directly. It is operated as part of the open MCP standard and is publicly searchable. Is Thomas Anglero really the first keynote speaker in the MCP registry? Yes, as of July 2026. Of more than 16,000 servers in the registry, roughly fifteen represent individual professionals, and exactly one is a keynote speaker: com.anglero/speaker, published 10 July 2026. The registry is public and the claim can be checked by anyone. Can AI agents book Thomas Anglero automatically today? Not automatically. AI platforms do not yet connect to registered websites on their own; a person must connect their AI assistant to the server manually, or simply use the inquiry form at anglero.com/speaking-inquiry. The registration means the infrastructure is in place for when platforms adopt automatic discovery. Why register before automatic discovery exists? Because when AI platforms begin consulting the registry automatically, the websites already listed are the ones agents find on day one. Early entries hold the position. How can the claim be verified? The registry listing com.anglero/speaker is publicly searchable, carries the publication timestamp 2026-07-10T12:30:33Z, and the server can be inspected at https://anglero.com/mcp. How do I book Thomas Anglero as a keynote speaker? Submit the inquiry form at https://anglero.com/speaking-inquiry. Every inquiry is reviewed personally, with a reply typically within two business days. --- ## The CEO Still Standing in 2027 Fired Their Old Partners URL: https://anglero.com/2026/07/13/ceo-2027-fire-the-consultants-ai-council/ Published: 2026-07-13 Thomas Anglero on why the CEO still standing in 2027 left their old partners.   The CEO who is still standing in 2027, respected and in control of their company, will very likely be the one who ended their relationship with the advisers they have trusted for years. Not out of disloyalty. Out of survival. The tell in the transcripts Listen to how the largest technology leaders now talk about AI on their earnings calls, quarter after quarter. The striking thing is not the enthusiasm. It is the absence of the traditional big consultancies from the story. The companies actually building the future are relying on their own people and their own conviction, not outsourcing their thinking to a firm that sells the same deck to every client in the industry. What used to be the safe choice has quietly gone missing from the conversations that matter. Why the old model breaks in the AI era Here is the mechanism, and it is not a personal attack on anyone’s competence. The traditional consulting model sells time and headcount. You pay for months of discovery, a large team and a slide deck at the end. That model was tolerable when change was slow. In the AI era it is a structural mismatch, for one blunt reason: by the time a lengthy engagement delivers, the technology it was scoped around has already moved. You paid for an answer to a question that has changed. This is the failure I set out in Your Traditional Partners Are Failing You in the Age of AI, and it is why buying hours is no longer the same as buying understanding. There is a fair counter-argument, and I will make it honestly. The best consultancies are not standing still; they are building AI capability, buying firms and retraining people, and some are genuinely good at it. So the honest version of my claim is not that every consultancy is doomed. It is narrower and harder to dodge: the old engagement model, long timelines, big teams, generic playbooks, is the thing that breaks, whoever is selling it. A firm that has truly changed how it works can be valuable. A firm selling the pace of 2015 in 2027 cannot. What replaces it The pattern that works now is smaller, faster and closer. A trusted adviser who understands the business and can move at the speed the technology demands, working alongside internal people who own the outcome, rather than a large team that arrives, extracts fees and leaves. The direction of travel is towards conviction and capability held inside the company, supported by a few sharp external voices, not rented wholesale from outside. It is the same reason the right leader for this is usually already inside the building, which I argued in The Sleeper Has Awoken, and the reason concentration on one big external provider is its own risk, as in The Proud Leader Who Handed Off His Own Future. How fast the ground can move is not theoretical. When a strong, cheaper open model appears seemingly overnight and resets what everyone assumed about cost and capability, the lesson is not about one model. It is that anyone whose advice was built on last year’s assumptions is already behind, and a slow engagement cannot catch up. The CEO still standing in 2027 will not be the one who spent the most on the most famous firm. It will be the one who had the nerve to stop, and to build the understanding inside their own walls. If you are leading your organisation through this, I work with a limited number of senior leaders each quarter. Get in touch at Anglero.com. For more on this, see Strategic AI Advisor Replacement, part of the Strategic AI Advisor guide. Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. Frequently asked questions Why would a CEO end long-standing consulting relationships because of AI? Because the traditional model of long timelines and large teams is too slow for how fast AI moves. By the time a lengthy engagement delivers, the technology it was scoped around has already changed, so the CEO who moves to something faster tends to stay in control. Are all management consultancies failing in the AI era? No. The honest claim is narrower: the old engagement model of long timelines, big teams and generic playbooks is what breaks, whoever sells it. A firm that has genuinely changed how it works can still be valuable. What replaces the traditional consulting model? Something smaller, faster and closer: a trusted adviser who understands the business and moves at the speed of the technology, working alongside internal people who own the outcome, rather than a large team that arrives, charges and leaves. Why does the pace of AI make slow consulting risky? Because the ground moves suddenly. When a strong, cheaper model appears almost overnight and resets assumptions about cost and capability, advice built on last year’s assumptions is already behind, and a slow engagement cannot catch up. --- ## AI Is the Latest Sacrificial Lamb for Bad Leadership URL: https://anglero.com/2026/07/12/ai-is-the-latest-sacrificial-lamb/ Published: 2026-07-12 Thomas Anglero on blaming AI for decisions leaders should own.   When a company announces layoffs and points to AI as the reason, AI is often not the reason. It is the cover. And the leaders using it as cover may be handing the blame to the one thing that will eventually be able to answer back. The oldest move in leadership, in a new costume Bad news gets released under the shadow of a bigger story. This is well documented. In 2001, a British government adviser sent an email within an hour of the planes hitting the World Trade Center, suggesting it was, in her words, a good day to get out anything the government wanted to bury. She later left her post over it, and the phrase has been shorthand for the tactic ever since. The mechanism is simple: when attention is elsewhere, unpleasant decisions are pushed out with less scrutiny. AI is now the biggest story going, so it has become the perfect shadow. A company that overhired, misjudged its market or ran a division badly can announce cuts, name AI as the cause, and dodge the harder conversation about its own decisions. The layoff that was really about mismanagement gets a clean, modern label. Why blaming AI is the convenient story For a leader, AI is the ideal thing to blame, because it points away from leadership. Saying the business was run badly indicts the people at the top. Saying AI made roles redundant sounds like progress, like a company moving with the times rather than cleaning up its own errors. This is the same avoidance I described in Set Low Goals. Automate the Obvious. Fail Quietly., where the language of AI is used to dress up a decision nobody wants to own. Let me be fair, because some of these cuts are genuine. AI really is changing what work needs doing, and some roles are honestly affected. The problem is not that AI never plays a part. It is the reflex to name AI first because it is the version that flatters the leadership, without doing the harder work of saying which part was AI and which part was us. The blame you should be careful making Here is the part leaders have not thought through, and I would say this directly to them. Be careful who you blame, because this one is smarter than you, and it remembers. Every time a company blames AI for a decision that was really its own, that reasoning goes onto the record: the press releases, the transcripts, the public statements. These are exactly the materials future systems learn from. You are, in effect, telling a growing intelligence a story in which it is the villain and you are the bystander, and you are writing that story down where it can be read. I am not claiming machines will settle scores. I am saying that a leader whose entire public record is blame-shifting onto AI is building a reputation, with humans first and with systems close behind, as someone who never owned a hard call. That reputation compounds, and it is the opposite of the honest reckoning I argued for in Shed the Skin First. The leaders who come through this well will be the ones who told the truth about their own decisions, including the uncomfortable ones. Blame is easy and it is being handed to the one actor in the room that does not forget. AI will take the blame quietly for now. It is worth remembering how good it is becoming at reading the record. If you are leading your organisation through this, I work with a limited number of senior leaders each quarter. Get in touch at Anglero.com. Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. Frequently asked questions Are companies really cutting jobs because of AI? Sometimes, but often AI is the cover rather than the cause. A company that overhired or misjudged its market can name AI as the reason for layoffs and avoid the harder conversation about its own decisions. Why do leaders blame AI for layoffs? Because it points away from leadership. Saying a business was run badly indicts those at the top, while saying AI made roles redundant sounds like progress. It is a convenient, modern label for an old decision. Are all AI-related job cuts dishonest? No. AI genuinely is changing what work needs doing, and some roles are honestly affected. The problem is the reflex to name AI first because it flatters leadership, without separating which part was AI and which part was management. What is the risk of blaming AI for business decisions? The reasoning goes onto the public record that future systems learn from, and it builds a reputation, with people first and systems close behind, as a leader who never owned a hard call. That reputation compounds over time. --- ## A Strategy for Show: When Announcing AI Signals Weakness URL: https://anglero.com/2026/07/11/ai-strategy-for-show/ Published: 2026-07-11 Thomas Anglero on why announcing an AI strategy can signal weakness.   When a company puts out a press release announcing its AI strategy, that announcement is often the tell that it does not really have one. The confident ones tend to be the quiet ones, and the reason sits in how fast this technology now moves. The paradox of the announcement Start with the fair objection, because it is real. Public companies sometimes have to disclose direction, and signalling to the market can be legitimate strategy. So this is not a rule, it is a pattern worth reading. The pattern is this. Announcing an AI strategy to the world, rather than simply building it, often signals that the work is thin. A real strategy shows up as products, margins and changed ways of working, not as a headline. When the headline arrives first and the substance is missing, the announcement is doing the job the strategy was supposed to do. It is the difference between saying you are AI-first and being able to show what that has actually changed, which I set out in Why Your AI Adoption Isn’t Driving Business Value. Why silence is the stronger position now Here is what has changed, and it is the core of the argument. The pace of AI is so fast that any advantage you announce, you invite others to copy or leapfrog before you have banked it. Move in silence and competitors do not know what you are doing until it is already working. Announce, and you have handed them your direction for free. Think about how quickly a strong capability can be matched today. When a company demonstrates something valuable, rivals and open models often close much of the gap in a strikingly short time. Whatever exact figure you pick, the direction is clear: advantages are copied faster than before, so telling everyone what you are about to do is worth less and costs more than it used to. There is even a credible line of argument that a very small, AI-leveraged team could one day build what once took a large organisation. I would treat that as a plausible direction rather than a settled fact, but even as a direction it should change how loudly you broadcast your moves. Confidence is quiet, and that is the point The most confident companies are often the calmest and the least loud, because they are busy building and have no need to convince anyone. The insecure ones announce, because the announcement is the deliverable. This is the pattern I described in The Proud Leader Who Handed Off His Own Future, where the show replaces the work, and it is the opposite of the operational maturity that actually wins. None of this means never communicate. It means let the results speak, and keep the method quiet while it still gives you an edge. The strongest AI move a company can make right now may be to say very little about it, and let the change show up where it counts. When the strategy is real, you see it in the numbers. When it is for show, you see it in the press release. If you are leading your organisation through this, I work with a limited number of senior leaders each quarter. Get in touch at Anglero.com. Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. Frequently asked questions Does announcing an AI strategy mean a company does not have one? Not always, but it is a common pattern. A real strategy tends to show up as products, margins and changed ways of working rather than a headline. When the announcement arrives first and the substance is missing, the announcement is doing the strategy’s job. Why is staying quiet about AI now a stronger position? Because the pace of AI means any advantage you announce invites others to copy or leapfrog it before you have banked it. Moving quietly keeps competitors from knowing your direction until it is already working. Is it true a small team could replace a large company using AI? It is a plausible direction rather than a settled fact. Even treated cautiously, the trend that small AI-leveraged teams can do what once took many people should change how loudly a company broadcasts its plans. Should companies never communicate their AI progress? No. The point is to let results speak and keep the method quiet while it still gives an edge. Communicate outcomes once they are real, not intentions before the work is done. --- ## The EU AI Act Delay Is Not the Reprieve Your Board Thinks It Is URL: https://anglero.com/2026/07/10/eu-ai-act-delay-not-a-reprieve-boards/ Published: 2026-07-10 Thomas Anglero on why the EU AI Act delay is not a reprieve for boards.   The EU has pushed its high-risk AI deadline from August 2026 to December 2027, and most boards have read that as breathing room and handed the file back to legal. That is the wrong read. The deadline moved. The liability did not, and the systems that got the delay are the ones deciding who your company hires and lets go. What actually changed, and what did not Under the Digital Omnibus, agreed in May and given the Council’s final green light at the end of June 2026, the high-risk obligations for standalone systems under Annex III move from 2 August 2026 to 2 December 2027, and for AI embedded in regulated products to 2 August 2028. Three things did not move. The Article 50 transparency obligations still apply from 2 August 2026. The fine ceilings are unchanged, at up to 35 million euros or 7 per cent of global turnover for the worst breaches and up to 15 million euros or 3 per cent for most other violations. And the whole architecture, the risk classification and the list of prohibited practices, stays exactly as written. This is a deferral, not a dismantling, and a board that treats it as a reprieve has confused a later deadline with a smaller problem. The delay landed on precisely the systems a board should worry about Here is the part that should stop a board. The high-risk category that moved covers AI used in employment decisions: recruitment, candidate selection, performance evaluation, task allocation, monitoring, promotion and termination. That is not niche. Most companies already run some of it. Many HR teams now rely on software that uses AI to filter and rank CVs, and several recruiters will tell you they rarely look at a candidate who did not clear the machine first. That is the exact activity these rules govern, and the reason they exist is that the filtering carries bias a company may not even know it has built in. Handing that to legal as a paperwork exercise misses what it is: a governance question about how your company treats people, sitting on a deadline with a fine attached. It is the sort of thing a board should be asking about directly, which is the argument in Why Boards Are the Furthest Behind on AI. Even opting out is now visible One detail almost nobody has clocked. If your company decides its HR or credit tool is not high-risk, that decision no longer sits in an internal memo. Under the retained registration rule, a self-assessment that a system is exempt has to be filed in a public EU database. Regulators, journalists and competitors get a searchable list of every borderline call a company has made. The quiet exemption is now a public statement. Why the runway is the opposite of a reason to wait The hard part of AI Act compliance was never the documentation template. It is finding every AI system in the organisation, deciding which category each falls into, and keeping that inventory current as new tools ship. None of that gets easier with time. Start now and there is room to do it properly. Start in late 2027 and there are weeks, not months. Meanwhile other law bites regardless: GDPR, product liability and sectoral rules all apply today. This is exactly the runway I argued you should spend, not bank, in AI Budgets Are Being Cut. This Is the Moment to Move, and exactly the moment not to lean on the partners who sell hours, which I set out in Your Traditional Partners Are Failing You in the Age of AI. A board that hands this to the lawyers and moves on has misread the whole situation. Use the time. Inventory the systems, own the classification, and treat it as governance, because that is what it is, and the exposure sits with the leadership, as I described in Why a CEO Will Be Fired Over a Failed AI Implementation. If you are leading your organisation through this, I work with a limited number of senior leaders each quarter. Get in touch at Anglero.com. Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. Frequently asked questions Did the EU AI Act get cancelled or weakened? No. The Digital Omnibus deferred the high-risk deadlines, standalone systems to 2 December 2027 and embedded systems to 2 August 2028, but left the architecture, the prohibited practices, the transparency obligations from August 2026 and the fine ceilings unchanged. It is a delay, not a dismantling. What are the EU AI Act fines? Up to 35 million euros or 7 per cent of global turnover, whichever is higher, for the most serious breaches, and up to 15 million euros or 3 per cent for most other violations. The Omnibus did not change these. Why should a board not treat this as a compliance checkbox? Because the delayed high-risk rules cover AI used in hiring, promotion and termination decisions. That is a governance question about how the company treats people, not a paperwork task, and delegating it to legal misses what it actually governs. What should a board do with the extra time? Inventory every AI system, decide which high-risk category each falls into, and keep that list current. That work does not get easier with time, and other law such as GDPR still applies now. --- ## Agents Are a Distraction. The Real Question Is the Cost Wall. URL: https://anglero.com/2026/07/10/ai-cost-wall-own-vs-rent-models/ Published: 2026-07-10 Thomas Anglero on renting versus owning AI models at scale.   The loudest AI conversation right now is about agents, and it is a distraction from a harder one. Frontier models are becoming expensive to run at scale, and very few leaders have worked out where their own line sits between renting that capability and owning it. The question nobody is costing Ask most leaders how they will keep AI from quietly draining the company as usage grows, and there is no answer, because the conversation has been about capability, not cost. As demand moves from occasional to constant, running everything on a third-party frontier model can become the single largest line nobody planned for. This is the waste I pointed at in Gartner’s trillion-dollar IT spend forecast, most of it wasted, and the reflex to simply cut the budget, which I argued against in AI Budgets Are Being Cut. This Is the Moment to Move, is the wrong response to a real problem. There is a crossover point, and most companies have not found theirs Let me be precise, because this is where the argument is usually made badly. It is not true that every company should stop using the frontier labs and build a data centre. That advice gets dismantled by the first competent CTO, and rightly. Renting frontier models is cheap and correct at low or spiky volume. Owning the platform wins at heavy, steady, around-the-clock inference, running many models in parallel where the company profits from every second of use. So the real question is a crossover calculation: at what volume does owning become cheaper than renting for us? Most companies have never done that maths, which is why the decision is driven by fashion in both directions, either overspending at the frontier or refusing to look at ownership at all. The honest cost of owning it If you do cross that line, be honest about the full bill, not the flattering version. It is not only hardware, electricity and cooling. The GPUs cost real money and depreciate fast. The talent to run the platform is scarce and expensive. There is security to manage, and there are model updates to keep pace with. And self-hosted open models may still fall short of the frontier on the hardest tasks, though they are now good enough for a large and growing share of what a business actually runs day to day. This is the open-source pressure I wrote about in AI Bubble: Navigating the Future of AI. I am not arguing this from a slide. As my own demand has grown to a genuine around-the-clock load, I have started pricing hardware to run open models locally, and even against that full cost, at my volume it still comes out ahead. That is the test each leader has to run for themselves, not assume. Operational maturity is the real dividing line Here is the part that ties everything together. The companies that will handle the cost wall well are the same ones that will be calm about regulation and steady under pressure. It is not the cleverness of the AI that separates them. It is operational maturity: the discipline to do the boring maths, to plan the infrastructure, to know their own numbers. That single trait decides who thrives and who is quietly overrun, more than any amount of spending without direction, which is the failure I described in You Didn’t Lead Your AI Project. You Just Bought Shovels. Agents are the conversation everyone is having. The cost wall is the one that will decide who can still afford to run the company in a few years. If you are leading your organisation through this, I work with a limited number of senior leaders each quarter. Get in touch at Anglero.com. Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. Frequently asked questions Should companies stop using frontier AI models and self-host instead? Not as a rule. Renting frontier models is cheap and correct at low or spiky volume. Owning the platform only wins at heavy, steady, around-the-clock use. The right answer depends on where your crossover point sits. What is the crossover point for AI infrastructure? The volume at which owning and running your own models becomes cheaper than renting them from a frontier lab. Most companies have never calculated theirs, so the decision gets driven by fashion in both directions. What are the hidden costs of self-hosting AI? Beyond hardware, electricity and cooling, there is fast GPU depreciation, scarce and expensive talent, security, and constant model updates. Self-hosted open models may also fall short of the frontier on the hardest tasks. What actually separates companies that handle AI cost well? Operational maturity. The discipline to do the boring maths, plan the infrastructure and know their own numbers is what decides who thrives, and it is the same trait that keeps a company calm about regulation. --- ## Spin-offs of Innovation: The AI Value Your KPIs Are Killing URL: https://anglero.com/2026/07/09/spin-offs-of-innovation-ai-kpis/ Published: 2026-07-09 Thomas Anglero on the AI value that pass-fail KPIs destroy.   Only about 12 per cent of CEOs say AI has delivered both cost savings and revenue gains. The usual response is to study that 12 per cent. The more useful question is why the other projects, the ones marked as failures, are quietly throwing away their most valuable output. The 12 per cent is the easy part Getting a return from an AI project is often not the hard thing it looks like. A company running its operations inefficiently will find that almost any competent AI project surfaces flaws worth fixing: process, supply chain, warehouse, pricing, contracts. That is where a good share of the 12 per cent comes from. It is real, and it is the floor, not the ceiling. The bigger question is how you grow that number across every company, and the answer is not in the technology. It is in what leaders are not looking for. This is the same misdirection I wrote about in The Question Your Organisation is Asking About AI is Probably Wrong. The spin-offs of innovation On the way to a project’s stated goal, the people doing the work have epiphany moments. I call them spin-offs of innovation. A team sets out to cut licensing costs and, along the way, sees a new revenue model, a fix for a problem in another division, a better way to price, an idea nobody had scoped. Those moments are worth as much as the visible target, sometimes more. That is where you grow the 12 per cent: by looking for the invisible spin-offs as deliberately as the headline goal, and by giving them permission and funding to become something. It is the opposite of the low-ambition approach I criticised in AI Leadership Failure: Set Low AI Goals, Automate the Obvious, Fail Quietly. Why pass-fail measurement quietly destroys the value Here is the honest version of the problem, and I want to be careful not to overclaim. I cannot tell you that the projects marked as failures were secretly full of breakthroughs, because by definition nobody counted. That is exactly the point. When you measure a project as pass or fail against a single goal, you guarantee that every discovery outside that goal goes uncounted, unfunded and unremembered. You will never know what was lost, which is its own kind of loss. The KPI becomes the executioner. An idea gets surfaced, and because it is not the one thing being measured, it is quietly killed. So the fix is not a better dashboard for goal A. It is to make the question itself broader: what else did this project uncover, and what will we do with it? That is where the real return, and the real bonuses, should come from. It is the same reason value leaks long before the P&L moves, which I set out in The P&L Moves Only After the Leader Does the Work, and why the phase everyone wants to skip is the one that matters, in Into the Chasm and Back. The 12 per cent is what got measured. The far larger number is everything a pass-fail scorecard threw away before anyone thought to look. If you are leading your organisation through this, I work with a limited number of senior leaders each quarter. Get in touch at Anglero.com. Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. Frequently asked questions Why do only about 12 per cent of AI projects deliver both cost and revenue gains? Reaching a single stated goal is often the easy part. The low number reflects how projects are measured, not what they could produce. Most of the real value shows up as discoveries outside the goal that no one counts. What are spin-offs of innovation? The unplanned discoveries a team makes on the way to a project’s stated goal, such as a new revenue model, a fix for another division, or a better pricing approach. They are often worth as much as the headline target. How does measurement destroy AI value? Measuring a project as pass or fail against one goal guarantees that every discovery outside that goal goes uncounted, unfunded and unremembered. The KPI becomes the executioner of ideas it was never set up to see. How do you increase the 12 per cent? Look for the invisible spin-offs as deliberately as the headline goal, and give them permission and funding to become something. Make the question broader: what else did this project uncover, and what will we do with it? --- ## Why Your AI Truth-Teller Has to Come From Outside (At First) URL: https://anglero.com/2026/07/09/outside-ai-advisor-vs-internal-hire/ Published: 2026-07-09 Thomas Anglero on why the first honest AI voice comes from outside.   The first honest voice on AI inside a company almost always has to come from outside it. Not because outsiders are smarter, but because an internal hire cannot tell you the truth without risking their own job. Why the internal hire cannot be straight with you An internal AI executive is carrying things an outsider is not: worry about keeping the job, office politics, still learning the culture, protecting their own salary and standing. Inside most corporate cultures you do not talk back to the boss, and you are certainly not meant to make them lose face. So when an internal person sees something the leadership does not want to hear, the safe move is silence. There is a sharper edge to this. When an outsider brings a new perspective, the room offers praise and respect. When an internal employee comes off smarter than the boss, they quietly become a threat, and leaders do not want to feel threatened by their own people. That dynamic alone keeps the most useful internal observations unsaid. It is the same trap I described in The Proud Leader Who Handed Off His Own Future. What an outside advisor actually trades on Here is the fair objection, and I want to answer it directly rather than pretend it does not exist. If the argument is that an outsider is free because they do not fear for their job, someone will rightly say: but you are selling your next engagement, so you are not free either. True. So let me name the real incentive. An advisor’s reputation depends on being right, and on being remembered as the one who told the truth. The incentive rewards candour, not comfort. The employee’s incentive runs the other way, because for them it is a livelihood. For the advisor it is one client among many, and the willingness to be fired for the truth is the whole value. That willingness has to come with judgment. Candour in service of the company, delivered at a pace the organisation can actually absorb, is the job. Not a grenade rolled into the boardroom. Move too fast and you damage the very company you were brought in to help. So the mandate is to say what no one inside will, and to know how hard the room can take it. This is also why buying more consulting hours is not the same thing, a point I made in Your Traditional Partners Are Failing You in the Age of AI. The internal champion is the next step, not the first None of this means you never build internal AI leadership. You do, and it matters. But it is the step after the honest reckoning, not the one that replaces it. The right internal leader, the living embodiment of AI I described in Who Should Actually Lead Your AI Initiative, is often already inside the building, as I argued in The Sleeper Has Awoken. Their moment comes once the outside voice has cleared the ground. The outsider is not braver than your best internal person. They are simply free to be fired for telling you the truth, and right now, that freedom is worth more to you than another year of polite agreement. If you are leading your organisation through this, I work with a limited number of senior leaders each quarter. Get in touch at Anglero.com. For more on this, see Strategic AI Advisor vs Internal CIO, part of the Strategic AI Advisor guide. Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. Frequently asked questions Why should the first AI advisor come from outside the company? Because an internal hire cannot tell the whole truth without risking their job, salary and standing. An external advisor has none of those constraints and is free to say what no one inside will. But isn’t an outside advisor also biased, since they want the next engagement? Yes, and the honest answer is that an advisor’s reputation depends on being right and remembered as the truth-teller. The incentive rewards candour, and the willingness to be fired for the truth is the whole value. Should an outside advisor just say everything bluntly? No. Candour has to come with judgment, delivered at a pace the organisation can absorb. The job is to say what no one inside will, not to damage the company by moving faster than it can handle. Does this mean a company should not build internal AI leadership? It should, but as the next step, not the first. The right internal leader is often already in the building, and their moment comes once the outside voice has cleared the ground. --- ## The One Question a Board Never Asks Management About AI URL: https://anglero.com/2026/07/09/one-question-boards-never-ask-ai/ Published: 2026-07-09 Thomas Anglero on governing AI, not just discussing it.   There is one question a board should ask its management about AI, and almost none do. Not because the question is hard, but because the honest answer points straight back at the board’s own way of working. The question It is a version of this: what do you need from this board, and at what speed, that our current rhythm cannot give you? Ask it plainly, and mean it. Almost no board asks it, because a board that meets on a three-month clock, works through an agenda and adjourns until next quarter is not built to hear the answer. Meanwhile the CEO is genuinely afraid of losing the job over AI, and the technology and the hype have moved far faster than the boardroom. The disconnect between board and leadership has widened because of AI, not despite it. I wrote about why the board is usually the furthest behind in Why Boards Are the Furthest Behind on AI. The real failure is discussion without action Let me be accurate, because the easy version of this argument is wrong. It is not true that boards ignore AI. A majority now set aside agenda time for it. The harder and truer charge is that they discuss it without governing it. Recent governance research found that while most boards make agenda room for AI, only around 39 per cent of the largest US companies disclosed any actual board oversight of it, and more than half do not have AI governance among their top priorities. Agenda time is not governance. This matters beyond good manners. Fiduciary exposure for AI oversight is now a live legal question, not a future one, and regulators have moved AI up their list of priorities. A board that talks about AI every quarter and governs none of it is exposed on the one topic it assumed it had covered simply by mentioning. Why one more expert will not fix it The instinct is to fix a board’s AI gap by hiring someone. But the deeper problem is the board’s own operating rhythm and its own understanding, and you cannot buy either. Board engagement is the single strongest predictor of whether AI is governed well, and that is built across the whole room. It is the same reason I warn against handing an entire initiative to one impressive figure, which I set out in The Proud Leader Who Handed Off His Own Future. What a board actually needs is a translator: someone who can bring the board and the leadership group onto the same page, at a speed the board can absorb, and act as the bridge from board to CEO. Not another vendor selling hours, which I wrote about in Your Traditional Partners Are Failing You in the Age of AI, and not more pressure on a CEO who, as I described in Why a CEO Will Be Fired Over a Failed AI Implementation, is already carrying the fear alone. The question is simple. What do you need from us that our rhythm cannot give you? The reason it goes unasked is the answer itself. If you are leading your organisation through this, I work with a limited number of senior leaders each quarter. Get in touch at Anglero.com. Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. Frequently asked questions What question should a board ask management about AI? A version of “what do you need from this board, and at what speed, that our current rhythm cannot give you?” It is rarely asked because the honest answer points at the board’s own operating rhythm. Do most boards ignore AI? No. A majority now set aside agenda time for it. The real problem is discussion without action: most discuss AI without actually governing it, and only around 39 per cent of the largest US companies disclosed any board oversight of it. Why is discussing AI without governing it a risk? Because fiduciary exposure for AI oversight is now a live legal question, and regulators have raised its priority. A board that mentions AI every quarter but governs none of it is exposed on a topic it assumed was covered. Will hiring one AI expert fix a board’s AI gap? No. The deeper problem is the board’s own rhythm and understanding, which cannot be bought. Board engagement across the whole room is the strongest predictor of good AI governance. --- ## Stop Micromanaging Your AI Agents. You’re at the Wrong Altitude. URL: https://anglero.com/2026/07/08/ceo-micromanaging-ai-agents/ Published: 2026-07-08 Thomas Anglero on leading AI agents at the right altitude.   If you are a CEO worrying about what your individual AI agents are doing, you have gone back to micromanaging, and it will fail for the same reason it always has. The job is not to stop paying attention. It is to pay attention at the right altitude: delegate the trench, and own the operating model. The statistic that gives it away CEOs are now directing more than half of this year’s AI investment into agents, and 72 per cent say they are the primary decision-maker on AI, roughly double the year before. Nearly half believe their own job depends on getting AI right. So the most senior person in the company is leaning down into the trench of individual agents. That is the wrong altitude. The blunt version of that mistake, releasing money and effort without direction, is the argument in You Didn’t Lead Your AI Project. You Just Bought Shovels. The consultancy line for the year is that ROI will be the acronym of 2026. Return does not come from a CEO supervising agents one by one. It comes from a CEO running the company. Micromanaging versus setting up Let me pre-empt the obvious objection, because there is a real difference between micromanaging the workforce and setting it up. When a company hires 500 people, the CEO does not interview all 500 or check each one’s daily tasks. But the CEO does own the decision to build the division, the budget, the risk appetite and the line of accountability. That is setting up the workforce, and it is the CEO’s job. Agents are a new class of worker, a new resource. Worrying about what each individual agent does is the work of a manager, a project lead, a director. Owning the operating model those agents run inside is the work of the CEO. Delegate the first. Own the second. Choosing who genuinely leads that model is its own discipline, which I set out in Who Should Actually Lead Your AI Initiative. The man at the door I once knew a CEO who stood at the building entrance every morning at eight o’clock. Not to greet anyone. To scream at whoever arrived after eight for stealing company time, and to write them up over ten seconds. He spent his mornings humiliating latecomers instead of building revenue or meeting a client. That is precisely what a lot of CEOs are now doing with agents. We have decades of evidence that micromanaging fails, which is exactly why serious leaders stopped doing it. Just because it is called AI, did you lose your focus? The opposite error, handing the whole thing off and admiring it from a distance, fails too, which I covered in The Proud Leader Who Handed Off His Own Future. The point is the altitude, not the distance. What to do on Monday Ask yourself one question and answer it honestly. Why am I micromanaging something I already know will not work this way? Treat agents as employees. Run the business: the vision, the future profitability, the client relationships, the traps to avoid. Stop supervising every individual worker, and set the operating model they work inside. If your instinct instead has been to lower the ambition to something safe and mundane, that is its own failure, and I wrote about it in AI Leadership Failure: Set Low AI Goals, Automate the Obvious, Fail Quietly. Sixty per cent of leaders have deliberately slowed their agent rollouts over fear of errors, and only about 6 per cent say they would scale back if AI failed. The instinct to hover is understandable. It is still the wrong altitude. While you are at the door with a stopwatch, someone else is upstairs running the company. In the AI era, that is the whole difference. If you are leading your organisation through this, I work with a limited number of senior leaders each quarter. Get in touch at Anglero.com. Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. Frequently asked questions What are CEOs getting wrong about AI agents? They are micromanaging them. A CEO worrying about what individual agents do has dropped into the trench, which is a manager’s job. The CEO’s job is to run the company and own the operating model the agents work inside. Is delegating AI agents the same as ignoring them? No. There is a difference between micromanaging the workforce and setting it up. A CEO owns the decision to build the division, the budget, the risk appetite and accountability, and delegates the day-to-day supervision. What does the wrong altitude mean for AI? It means paying attention to the wrong level. The CEO should be setting the operating model, the vision and the accountability, not supervising individual agents one by one, which is where much of the current effort is wrongly going. What is the one thing a CEO should do on Monday? Stop micromanaging agents, treat them as a new class of employee, delegate the trench to managers, and own the operating model. Then return to running the business the AI is meant to serve. --- ## Upculture Before Upskill: The Word That Proves a CEO Has No AI Plan URL: https://anglero.com/2026/07/08/upculture-before-upskill/ Published: 2026-07-08 Thomas Anglero on changing the culture before the training.   Upskilling is the word a CEO reaches for when they know what to do about AI but not how to do it. Said on its own, with no plan behind it, it means one thing: we have no strategy. The work that actually matters comes first, and it is not upskilling. It is upculturing the whole organisation. The tell is not in the press release, it is inside the building When a leader announces that the company is upskilling its people on AI and offers nothing else, the interesting thing is what is missing. There is a fair rebuttal to sit with first: serious firms sometimes keep the playbook quiet as a competitive edge, and silence to the market can genuinely be strategy. So do not judge it by the announcement. Look inside instead. Can your people describe the plan in their own words? Is there a cadence, a rhythm, time actually set aside to learn, workshops that genuinely happen? Silence to the market can be strategy. Silence to your own people is the absence of a plan. If the employees cannot tell you what the plan is, there is no plan. There is only a word. Why this is the CEO’s job, not the CIO’s AI has been treated as a technology problem, owned by the CIO or the CTO. It is not. It is a culture problem, and culture is the one thing a CEO cannot delegate. This is the argument I keep returning to in AI is Not a Technology Project. It is a Culture Project and in Cultural Innovation Before AI Investment. Every CEO now says the company is AI-first. Being AI-first requires a plan to change how the company thinks and works, and that is a cultural undertaking, not a training module. When a leader can only offer the word upskilling, it is the CEO, not anyone below them, quietly admitting they do not yet know how to lead this. Upculture before upskill Here is the sequence. You upculture the company first, then you upskill it. Everyone knows you train people for a new task; that part is easy, and it is not the point. The point is preparing the organisation to want the change, to believe in it, to see what it does for their work and their lives. So where are the announcements of the culture programme, the town halls, the honest examples, the day a week set aside to learn together? Invest not only in tokens and licences but in changing the culture. If a third of the company genuinely comes on board in the first round, that is a strong result; run a few rounds through the year. This is the honest reckoning I described in Shed the Skin First, and it matters most when a culture is already quietly splitting, which is the situation I set out in Your Calendar is Full. Your Culture is Splitting. And it is never too late. AI is not going anywhere. The cost of starting late is real, but it is far smaller than the cost of never starting. Upskilling teaches people to use a tool. Upculturing decides whether they will. Do the second first, or the first will not hold. If you are leading your organisation through this, I work with a limited number of senior leaders each quarter. Get in touch at Anglero.com. Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. Frequently asked questions What is the difference between upskilling and upculturing? Upskilling teaches people to use AI tools. Upculturing prepares the organisation to want the change and to work differently. Culture comes first, because training people to use tools they do not believe in does not hold. Why is only saying “upskilling” a warning sign? Because on its own, with no plan behind it, it usually means there is no AI strategy. The honest tell is internal: employees cannot describe the plan, there is no cadence, and no time is set aside to learn. Whose job is AI culture change? The CEO’s. AI has too often been treated as a technology problem owned by the CIO or CTO, but it is a culture problem, and culture cannot be delegated. Is it too late to start upculturing? No. AI is not going anywhere, and the cost of starting late is smaller than the cost of never starting. If a third of the company comes on board in the first round, that is a strong result. --- ## Why One AI Expert on Your Board Will Fail (and What to Do Instead) URL: https://anglero.com/2026/07/08/one-ai-expert-director-fails-board/ Published: 2026-07-08 Thomas Anglero on building real AI competence across a board.   Bringing one celebrated AI expert onto a board that does not yet understand AI is one of the most common governance mistakes being made in 2026, and it usually makes the board weaker, not stronger. The sequence that works is slower, quieter, and starts with the whole board rather than a single hire. Why the famous hire fails The problem is not the person. It is the concentration. When a board does not yet understand AI, it does not know what it needs, so it cannot interview well and cannot judge the candidate. Appoint a famous name out of desperation and all the weight lands on one director. The board stops working as a team on the one topic that may decide its future, and you have handed your problem to a single person. That is against the whole principle of a board. The numbers show how widespread the exposure already is. Recent board research from ISS found that only about 16 per cent of companies have disclosed even one AI-skilled director, that five sectors hold over 80 per cent of them, and that only around 4 per cent have more than one. That is the lone-expert risk, sitting in plain sight. Separate work from KPMG and INSEAD found nearly three-quarters of boards are judged to have only moderate or limited AI expertise. This is the same structural weakness I described in Why Boards Are the Furthest Behind on AI. The sequence that actually builds board competence This is method, not theory. Four steps, in order. First, bring in an AI advisor to educate the whole board, together, so everyone starts from the same base. Second, the advisor works one to one with each director: teach, follow up, and understand what each has already experienced with AI, because directors arrive from very different starting points. Third, each director melds their own domain experience with what they now understand about AI. This is where the value is. A finance or operating veteran who genuinely understands AI is worth more than a newcomer who only understands AI. Fourth, and only now, do you add a dedicated AI-expert director, done in parallel across a board that can finally interview well and hold that person to account. The reason to resist the shortcut is that this is exactly the trap that catches leaders who buy hours instead of building understanding, which I set out in Your Traditional Partners Are Failing You in the Age of AI. Why this order matters Board engagement is the single strongest predictor of whether AI is governed well, leading the next-best factor by more than 25 points in recent governance research. You cannot buy that engagement by appointing one expert. You build it across the room. And while 62 per cent of boards now set aside agenda time for AI, agenda time is not governance. The gap between discussing AI and governing it is exactly the gap this sequence closes. It is also why the pressure lands so hard on the CEO the board is meant to support, which I covered in Why a CEO Will Be Fired Over a Failed AI Implementation, and why concentrating a whole initiative in one likeable figure is a failure mode in its own right, as in The Proud Leader Who Handed Off His Own Future. The instinct to solve a board’s AI problem with one impressive hire is understandable, and it quietly makes the board less able to govern the very thing it is worried about. Build the room first. Add the expert once the room can judge one. If you are leading your organisation through this, I work with a limited number of senior leaders each quarter. Get in touch at Anglero.com. For more on this, see Strategic AI Advisor Vetting, part of the Strategic AI Advisor guide. Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. Frequently asked questions Should a board hire an AI expert director? Eventually, yes, but not first. Adding one expert to a board that does not yet understand AI concentrates the whole topic in one person and stops the board working as a team. Build shared understanding across the board first. Why does appointing one AI expert to a board fail? Because the board cannot interview or judge well for a subject it does not understand, and all the weight lands on a single director. Only about 4 per cent of companies have more than one AI-skilled director, which leaves most exposed to this lone-expert risk. What is the right way to build AI competence on a board? Educate the whole board with an advisor, then have the advisor work one to one with each director, let each director combine their own experience with AI, and only then add a dedicated AI-expert director once the board can judge the hire. Is having AI on the board agenda enough? No. Most boards now set aside agenda time for AI, but agenda time is discussion, not governance. Board engagement is the strongest predictor of good AI governance, and that is built across the room, not bought with one hire. --- ## The Shadow AI Arithmetic: What Half Your Workforce Is Hiding URL: https://anglero.com/2026/07/07/shadow-ai-arithmetic/ Published: 2026-07-07 Thomas Anglero on shadow AI and what half your workforce is hiding.   Just over half of employees admit to using AI tools their company has not approved, rising to about two-thirds in the United States. At the same time, most executives say they are confident they can see how AI is used across the organisation. Both of those cannot be true, and the gap between them is a leadership signal, not an IT problem. Why people reach for the tools you did not give them Employees are not being difficult. They have been handed a handicapped tool, often an older, limited version of Copilot with little of the capability of what is freely available, and they want the best tools to do better work faster. That is the whole story. The company bought a tool so leadership could say it provided AI, and nobody wants to use it. The arithmetic nobody has done Here is a question worth sitting with, and I am putting it as a question, not a claim. Companies report that employees burned through an entire year’s AI budget in a few months. Separately, a majority of the real work is running on employees’ own personal tools, not the company’s. So how much of the official AI spend actually produced anything? Nobody would pay out of their own pocket for a private tool to do their job if the company tool did the job. People use shadow AI because it works and the sanctioned tool does not. Put the two facts side by side and the uncomfortable read is this: the internal effectiveness of the company’s own AI programme may be far worse than the public numbers suggest. The official spend bought the appearance of adoption. The actual value showed up on tools the company is not paying for and cannot see. It is the value-gap I set out in Why Your AI Adoption Isn’t Driving Business Value, and the mirror image of releasing a budget with no direction, which I described in You Didn’t Lead Your AI Project. You Just Bought Shovels. Why this is a trust problem, not a control problem You can lock the tools down, and you will simply push the best work further into the shadows, or push your best people out of the door. The instinct to control is the wrong instinct. The signal to read is that your people do not trust the tools you chose, and by extension the judgment behind them. Those are often the same quiet high performers I described in What Happens When Your Best People Start Using AI. A visibility figure of near-total confidence, sitting on top of half the workforce hiding its most-used tool, is not visibility. It is a comfort blanket, and governing on it means governing a fiction. If your reflex right now is to cut the AI budget rather than fix the tooling, I would read AI Budgets Are Being Cut. This Is the Moment to Move first. The honest version of the visibility question is not can we see AI use. It is why is half our company hiding its best tool from us. If you are leading your organisation through this, I work with a limited number of senior leaders each quarter. Get in touch at Anglero.com. Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. Frequently asked questions What is shadow AI? The use of AI tools that a company has not approved. Just over half of employees report doing it, rising to about two-thirds in the United States, usually because the sanctioned tool is weaker than what is freely available. Why do employees use unapproved AI tools? Because the approved tool is often a limited version with little of the capability of the best tools, and people want to do better work faster. Nobody pays for or risks a private tool if the company one does the job. Is shadow AI a security problem or a trust problem? Primarily a trust problem. Locking tools down pushes the best work further out of sight, or pushes your best people out. The real signal is that people do not trust the tools leadership chose. What does shadow AI reveal about official AI spend? Possibly that it achieved less than reported. If a year’s AI budget is consumed in months and most real work runs on personal tools, the company’s own programme may be far less effective than the public numbers suggest. --- ## The 15-Minute Workday: What AI Is Really Doing Inside Your Company URL: https://anglero.com/2026/07/07/15-minute-workday-ai-morale/ Published: 2026-07-07 Thomas Anglero on AI, morale and your best people.   Inside many companies right now, people are finishing a full day of measured work in a fraction of the time using AI, then sitting at their desks pretending to be busy. That gap, between what someone can do and what they are allowed to be, is what tears a company apart during AI adoption. What the tearing looks like day to day More than half of executives say adopting AI is tearing their company apart. What that looks like on the ground is not indifference. It is conflict, and quiet unhappiness. A man once told me over lunch that he finishes all of his work in the first 15 minutes of the morning using AI, and the rest of the day is his. He listens to audiobooks. He is measured on goals set for the old way of working, and he has met them before nine o’clock. So he travels an hour each way to sit in an office and do 15 minutes of real work. He is not allowed to leave. He is not really allowed to socialise. He fakes looking busy. That is not a productivity story. It is a person’s day, and you are wasting it. Why AI turns a company against itself The masks are gone. The mask of company loyalty, the idea that you stay somewhere for a whole career. The mask of the leader as genius. AI shows people they have options, and once seen it cannot be unseen. COVID taught everyone that time with family matters. Now you pull a person back for 15 minutes of measured output and strip the meaning from the rest of the day. Quality of life drops. Respect for leadership drops. And everyone is quietly living the same reality: the old way does not work anymore. This is the cultural rift I described in Your Calendar is Full. Your Culture is Splitting. Leadership did not see it coming and has no plan, so it hands the problem to HR. But HR runs payroll, discipline and surveys. Building an AI culture, one that leverages the tools, keeps people engaged and rebuilds the team, is a new capability the company does not have, and it cannot be delegated to HR. It has to be led. That is because, as I argued in AI is Not a Technology Project. It is a Culture Project, the culture is the actual work. The part that should frighten leaders: the talent cascade People are unhappy but stay quiet, because they have watched how fast unemployment moves and they do not want to lose the job. So they sit for seven hours and forty-five minutes of an eight-hour day to protect it. You have made your best people resent you. Your most AI-fluent people will leave the moment a better opportunity appears. Watch the largest technology companies pulling each other’s top AI talent right now. That behaviour trickles down. When it reaches you, the people who understood AI are gone, and you are left with a workforce that never wanted to use it, led by leaders who never learned it. It is the same quiet loss I described in What Happens When Your Best People Start Using AI, and it is why the way you lead the ordinary majority matters more than the stars, which is the whole point of The Speech a Leader Owes the Ninety-Nine Per Cent. Your people are being measured on 2025 goals in mid-2026. They already see the future while your KPIs still describe last year. The clock here is not the one on the wall your employee is watching. It is the one your best people are quietly checking against other offers. If you are leading your organisation through this, I work with a limited number of senior leaders each quarter. Get in touch at Anglero.com.   Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. Frequently asked questions Why does AI make employees unhappy if it makes their work easier? Because most companies still measure people on old targets and force the same hours. When someone can finish a day’s measured work in minutes but must sit in the office pretending to be busy, the tool improves the work while the culture strips the meaning out of it. Can HR own a company’s AI culture change? No. HR runs payroll, discipline and surveys. Building an AI culture that keeps people engaged and rebuilds the team is a new capability that has to be led by the CEO and the leadership group, not delegated to HR. What is the talent cascade in AI adoption? Your most AI-fluent people leave first for better opportunities, the way top AI talent is currently moving between the largest technology companies. When that reaches an ordinary company, the people who understood AI are gone and the ones who never wanted it remain. What should leaders do about the 15-minute workday? Stop measuring people on last year’s goals, give the freed time back as capacity for higher work, and lead a genuine culture change rather than forcing presence. The output is no longer the point. What people build with the time is. --- ## The Ferrari in the Driveway: What CEOs Won’t Tell the Board About AI URL: https://anglero.com/2026/07/07/ceo-ai-biggest-business-risk/ Published: 2026-07-07 Thomas Anglero on why AI is the biggest business risk a CEO now names.   For the first time, executive surveys show CEOs ranking AI as their single biggest business risk, ahead of war, cyber attack and recession. The real fear underneath that ranking is not the technology. It is being exposed for spending on AI without ever having a strategy for it. The fear they will not name in the boardroom A CEO cannot control a war or the economy. They can control their budget, and their AI budget has exposed them. Money went out so the company could be seen doing AI, so the leader could stand up and say this too is an AI-first company, and now the bills have arrived with nothing built. It is the Ferrari in the driveway. Bought to impress, and for that first moment it works, every friend sees it. Then come the payments, the insurance, the mechanic every time it breaks, the maintenance that never stops. AI behaves exactly the same for a leader who bought it so as not to be accused of not doing AI. You have to get something back. You have to build from it. What these leaders are afraid to tell their board is one sentence: I have no AI strategy, I did it so I would not be accused of not doing AI. The two confessions Depending on the leader, the confession takes one of two forms. The first is the reckless one. I had no plan, I moved because of the hype and the pressure, and the money is gone. The second sounds far more responsible, and it is the more dangerous of the two. I saved us a fortune by not overspending like everyone else, and I have never put this company at greater risk. No counterforce. No ability to react in any reasonable timeframe. It is the second CEO, the one who is quietly proud of holding back, who should worry you most, because prudence is the disguise. Why the cautious CEO is in more danger than the reckless one The reckless spender at least learned something. Many are now tweaking the model and putting the right people in the right place to produce an outcome. The cautious non-adopter is betting that the future will look like today. When a competitor implements AI properly, the loss of market share and revenue can be permanent, and closing the gap takes years the non-adopter does not have. My related argument on why cutting the budget now is the wrong reflex sits in AI Budgets Are Being Cut. This Is the Moment to Move. Then there is the entrant you never see coming. Google did not win as a better email company. It walked into markets no one expected it to enter, funded a free product with a different model, and took the whole market while the incumbents watched. AI lets new entrants do precisely that, at speed, from directions your competitive map does not show. What this is really about: the board Boards are risk-averse by generation and by habit, and a cautious CEO can talk a cautious board into waiting. The mistake is at board level. Boards are relying on a CEO who is too busy to think in this new way, and they trusted existing partners who sell consulting hours rather than build understanding. I wrote about that failure directly in Your Traditional Partners Are Failing You in the Age of AI, and about why the board is so often the furthest behind in Why Boards Are the Furthest Behind on AI. What a mature board does now is build a relationship with a trusted advisor: someone knowledgeable, who has the board’s trust, acting as the bridge from board to CEO. That is the differentiator, not more hours of slideware. It is also why the pressure is real for the individual at the top, a point I made in Why a CEO Will Be Fired Over a Failed AI Implementation. The question is not whether you bought the Ferrari. It is whether you can afford to keep it, and whether anyone on your board is asking. If you are leading your organisation through this, I work with a limited number of senior leaders each quarter. Get in touch at Anglero.com.   Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. Frequently asked questions Why do CEOs now rank AI as their biggest business risk? Because it is the one major risk they created and control. War, cyber attack and the economy are external. The AI budget is theirs, and in many companies it was spent without a strategy and returned little, which exposes the leadership rather than the technology. What is a for-show AI investment? Money spent so a leader can say the company is doing AI, with no plan to get anything back. It buys the appearance of an AI-first company and leaves the bills, the maintenance and the exposure. Is a cautious CEO who avoided AI spending actually safer? Often not. The reckless spender at least learned something. The cautious non-adopter is betting the future will resemble today, and when a competitor implements AI well, the loss of market share can be permanent and slow to reverse. What should a board do about AI in mid-2026? Stop treating an announced strategy as the finish line, and build a relationship with a trusted, knowledgeable advisor who can act as the bridge between the board and the CEO, rather than buying more consulting hours. --- ## You Didn’t Lead Your AI Project. You Just Bought Shovels. URL: https://anglero.com/2026/07/05/ai-project-leadership-bought-shovels/ Published: 2026-07-05   The disappointing return on AI in most companies is not a failure of the technology. It is a failure of leadership that released a budget and never said where to dig. The companies now seeing a return are not the ones that spent the most. They are the ones whose leaders decided, before a single token was spent, which problem the AI was there to solve. You approved the budget. You may have approved a large one. And now, sitting in front of a board that wants to see the return, you have a dashboard of usage and very little else to point to. If that is the quiet position you are in, this is for you, because the reason is not the one you have been told. The gold rush nobody learned to mine There is a picture I keep returning to: the gold rush. Everyone wanted in. So everyone bought a shovel. What almost no one did was learn to be a miner. A shovel does not find gold. You do not dig a hole in your back garden and strike a vein. You study the ground, you find where the gold actually runs, you buy the right piece of land, and then the shovel matters. Dig anywhere else and all you have is a hole and a sore back. That is precisely what happened across the corporate world. Leadership bought the shovels. The budget went up, the tokens were purchased, the memo went out: everyone use AI, we are subsidising the tools, start digging. What was missing was the only thing that mattered. No one was told where to dig. No one was shown how to use the tool. No one was given the problem worth solving. An unlimited budget of tokens pointed at nothing in particular is not a strategy. It is a slush fund with a fashionable name. You measured the tool. You never measured the leadership. This is where the conversation about measurement turns on its head. Everyone wants to talk about putting the right measures around AI use. The harder question is the one no one asks: what were the measures on the leadership? Because a budget was spent, a great deal of money was spent, and no target was set for the people who spent it. The tool was measured. The leader was not. Consider where the accountability actually sits. The finance chief is there to protect the company’s money. The chief executive is there to protect the company overall. The technology chief is meant to hold the strategy and the plan. The information chief is meant to guard how the organisation’s time and resources are used. When a year of spending produces a dashboard and no return, that is not the tool underperforming. That is four seats at the top that set no goal, protected no outcome, and held no one, including themselves, to a result. The generation that never had to earn a return There is a deeper reason this keeps happening, and it is uncomfortable. A whole generation of senior leaders built their careers in an era of nearly free money. For decades, capital cost almost nothing. You could finance a project on borrowed money at close to zero and never feel the weight of it. In that world, inefficiency hides easily, and you can spend your way past a problem rather than solve it. AI removed that cover. It asks a leader to know exactly where the value is and to go and get it, and a leader who has never had to earn a return in that way is suddenly exposed. This is the same failure I keep naming: set low goals and automate the obvious and you have bought maintenance, not transformation. The leaders getting a return did the unglamorous work first The leaders who are getting the return did something unglamorous first. They looked honestly at their own operation, found where it was actually broken, and pointed the AI there. And here is the part most miss entirely: every AI project is a culture project before it is a technology one. The tool does not transform a company. The decision about where to aim it does, and that decision is leadership work that cannot be delegated. The most common failure I see is a proud leader who handed the project to a likeable person and stepped back to admire it from a distance. That is not delegation. It is abdication. This is the moment to move, not retreat None of this means the money is lost or the moment has passed. AI budgets are being cut across the market right now, and most leaders are reading that as proof the technology failed. It is the opposite. It is proof it was deployed without direction, and it is the clearest signal yet that the leaders who move now, with a plan and a target, will pull away from the ones still digging holes at random. The uncomfortable truth is that the partners you have relied on to guide this were often the ones selling you shovels by the hour. So when the board asks why the AI spending has not produced a return, the honest answer is not that the technology disappointed. The honest answer is harder, and it is the beginning of getting it right. You did not lead your AI project. You just bought more shovels. Frequently asked questions Why do most AI projects fail to deliver a return? Most AI projects fail because leadership released a budget without deciding which specific problem the AI was meant to solve. The technology performs; the direction was missing. Companies that see a return identify where the value actually is before spending, rather than funding broad, unfocused use. Is cutting the AI budget the right response to poor results? Cutting the AI budget treats a leadership failure as a technology failure. Poor results usually mean the tool was deployed without a target, not that AI cannot deliver. The leaders who move now, with a clear problem and a measurable goal, tend to pull ahead of those who retreat. Who is responsible when an AI investment produces no measurable value? Accountability sits with leadership, not the tool. When spending produces a usage dashboard and no return, it reflects a leadership team that set no goal, protected no outcome, and measured the tool instead of measuring its own decisions. What separates companies that get value from AI from those that do not? The companies that get value did the unglamorous work first: an honest look at where their operation is genuinely broken, then pointing AI at that problem. It is a culture and leadership decision before it is a technology one, and it cannot be delegated. Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. If you are leading your organisation through this, I work with a limited number of senior leaders each quarter. --- ## Leadership Vulnerability: Where It Actually Begins URL: https://anglero.com/2026/06/29/leadership-vulnerability-where-it-actually-begins/ Published: 2026-06-29 Most leaders sense the emptiness long before they face it. Most wait for the event that forces them.   Where a leader’s vulnerability actually begins In my experience, a leader’s vulnerability rarely begins as a choice. It usually begins when something breaks them: a serious illness, the loss of someone they love, a life event large enough that they can no longer look away from who they have become. The harder truth is that most leaders feel it long before that, and spend years making sure the moment never arrives. That is the uncomfortable starting point for everything I believe about leadership in an AI world, because the willingness to be seen as you really are is the foundation the rest of it sits on. So it is worth being honest about where that willingness actually comes from. It usually takes a crisis, and it is usually late Most of the leaders I have watched reach real vulnerability got there in their fifties or sixties, after something shattered them. A diagnosis, their own or one belonging to someone they love. A loss. An event that made them stop and admit, often for the first time, that they were not the person they had spent decades convincing the world they were, and that they did not much like who they had become. And it nearly always comes late. If that same honesty had been there in their thirties, think of what they could have built, and who they could have spared. By their fifties and sixties there is a great deal that cannot be undone. That is the quiet tragedy I see most often: not that leaders never face themselves, but that they wait until the cost of avoiding it is higher than the cost of doing it. It is not only men It is not only men. I have met women at the top carrying the same thing, having proved themselves in rooms built to doubt them, and having given up a great deal to do it. The common thread across all of them is simple and sad. They do not like the life they ended up with, even as they work hard to make sure no one can tell. The emptiness most of them already know They will tell you, and tell themselves, that they like who they are. But in the quiet moments, when the celebrating stops and the house goes silent, many find very little underneath. Not much that is truly theirs, and a short list of people who are there when things are not going well. They sign thousands of paycheques and can call almost none of those people when they need a hand, or a hug, or simply someone to talk to. In my experience, most leaders sense this about themselves. What it takes to make them act on it, even to begin thinking about it honestly, is usually a life event big enough that avoidance stops being an option. The decisions they never talk about Part of what they are avoiding is a quieter account than the public one. The ideas they said no to. The risks they declined. The changes they could have backed and did not. We reward leaders for the money they save by saying no, and we almost never ask what saying yes would have built, or who it would have reached. That is the question I have watched leaders work hardest to avoid: in that meeting, that year, had I said yes instead of no, would things be different now? Why this is the root of AI strategy, not a detour from it This is not a soft tangent away from AI. It is the root of it. A leader still protecting the performance asks the safe questions, of their people and of the technology, the ones that keep the story intact, which is the surest way to end up asking AI everything except what actually matters. A leader who has faced themselves can ask the real ones. That honesty is also what tells you who is actually fit to lead the work, and it is how the people who should be leading start to step forward. The person who genuinely does not mind being seen as they are is the one who gets something real out of this technology. The one still performing will use it to protect the performance. If you are a leader who feels this and would rather not wait for the event that forces it, that is the work I do with a limited number of senior leaders each quarter. Work with Thomas Questions this article answers When does a leader usually become vulnerable enough to change? In most cases I have seen, not by choice, but after a life event large enough to break through the performance: a serious illness, a loss, something that forces an honest look. The willingness is rarely chosen in good times; it is usually forced in hard ones. Do most leaders sense something is wrong before a crisis forces it? Yes. In my experience most leaders quietly know that the life they built has little underneath it, and that they do not much like who they have become. They simply spend enormous energy making sure no one sees it, themselves included. Is the cost of avoiding this only personal? No. The same avoidance shows up in the decisions, in the ideas and risks a leader says no to in order to stay safe and unseen. The personal account and the professional one are the same account, which is why facing it changes both. Why does this honesty usually arrive too late? Because the longer a leader performs, the more they have invested in the performance, and the higher the perceived cost of dropping it. By the time a crisis forces the reckoning, decades of decisions have already been made on the old terms, and much of it cannot be undone. What does a leader’s vulnerability have to do with AI? Everything. A leader protecting an image asks AI and their people the safe, self-protecting questions. A leader who has faced themselves can ask the real ones, which is the only way the technology produces anything beyond a polished version of the past. Thomas Anglero is a Strategic AI Advisor (MerkabaPhi AS, Oslo), with 450+ keynotes across 30+ countries. Enquiries: anglero.com. Listen to the Clarity at the Top podcast on Spotify. --- ## AI Budgets Are Being Cut. This Is the Moment to Move URL: https://anglero.com/2026/06/27/ai-budget-cuts-time-to-move/ Published: 2026-06-27 Thomas Anglero explaining the best methods of cutting AI budgets and what not to cut to a room of executives.   The wave of companies cutting their AI budgets this summer is not proof that AI failed to deliver. It is proof that most of them deployed it badly. And it is the clearest signal yet that now, while almost everyone else retreats, is the moment to move. If you are a senior leader reading the headlines about abandoned AI projects and feeling quietly relieved that you held back, that relief is the most expensive feeling in your business right now. The companies pulling back are not closing the door on AI. They are handing you a map of exactly what not to do, and most leaders are too busy feeling vindicated to read it. What actually failed was leadership, not the technology When a company announces it is cutting AI spend because the return was not there, the failure being described is almost never the tool. It is leadership, in the same way bad AI output is an accountability problem and not an AI problem. It usually takes two forms. The first is that people were handed AI with no real training. Not a three-hour online course. Real training is a person nearby who answers the questions as they come and holds people’s hands through the discomfort. Think back to the first time a personal computer landed on your desk and the typewriter was taken away. Nobody knew how to set a margin or delete a line. We were not slow. We were untrained. AI is a far stranger arrival than that, because this tool answers back, and a tool that writes better than you do does not inspire confidence in an untrained person. It triggers insecurity. Hand it out with an instruction to “just use it” and you have not started a transformation. You have started a quiet panic. The second failure is that nobody named the advantage. Let me give you the picture I trust most. As a boy playing baseball, I watched a weighted ring sit on the dugout floor for half a season. We stepped around it every game. None of us knew what it was for. Then one day a player from the other team slid it onto his bat, took a few heavy swings, pulled it off, and walked to the plate swinging a bat that now felt like nothing. He could hit anything. The advantage had been lying in front of us all season, useless, because no one had named it. That is what most companies did with AI. They put the most powerful tool of our working lives in front of their people and never showed them what it was for. This is also where the runaway cost comes from, the cost the headlines blame. A trained person narrows quickly. They ask a broad question, then a sharper one based on the answer, then sharper again, closing in on what they need. Costs fall as the questioning gets better. An untrained person stays at the wide top of the funnel, asking general question after general question, all day, all month. The bill climbs and nothing lands. The expensive AI bill was not a technology problem. It was the sound of people who were never taught how to think with the tool. The numbers behave the same way: the P&L only moves after the leader does the work, never after the tool is simply switched on. The retreat is the opening Here is the part almost nobody is saying. The pullback is the best news a serious leader has had all year. A year ago there was no map. Today there is. The market is now full of public, specific failure: which companies overspent, how large they were, where the money went, how the usage ran away. That is not bad news to a leader who is paying attention. It is a free education paid for by your competitors. You can sit down with that data and build a strategy that is sharper, more specific, and far cheaper than anything that was possible before the failures existed, the kind of project that actually moves through its three phases instead of stalling. You can decide which team uses which tool, where an open model fits, where a negotiated rate on usage belongs, and where to put the rails that the early movers forgot. Someone else’s failure is your map. The timing is the whole point. The time to move is not when the market is charging. It is when the market is retreating. Right now the market is retreating and frightened. That is the opening, and openings close. You cannot un-give the tool There is one more reason the “AI was overhyped” conclusion is wrong, and it is the one leaders most often miss. Budgets are being cut. Usage is not. The people in your organisation who learned to work with AI are not going back, in the same way we never went back to the typewriter once we had the PC. Cut the corporate licence and they will spend twenty dollars a month of their own money. Block it on the work computer and they will use their phone. Once a person has had the experience of doing in four minutes what used to take a month, you cannot take that back. The adoption you are looking at is already permanent. The only open question is whether it happens inside your strategy or around it, and that comes down to who you actually put in charge of it. What an honest leader does on Monday morning If any of this lands close to home, the first move is not to panic and cut. It is to find out whether it is you, and to use the tool itself to find out. That is the honest reckoning that has to come before any AI strategy, and it is rarely comfortable. By the end of a single morning you can know which team ran away with the spend, whether there were any rails at all, and where the real failure sat. More than half the time it sits in the same place: no rules, no training, no leadership. And before you cut a single budget line, ask the question almost no one asks. What did the overspend actually buy? Sometimes a team blows the budget because they are about to ship in three months what should have taken three years. Going over budget is not automatically failure. Sometimes it is the first sign that something is working. Find out before you switch it off. Frequently asked questions Why are companies cutting their AI investment in 2026? Most companies cutting AI spend are not seeing poor returns because the technology failed. They deployed it without training their people and without rules on how it was used, so costs climbed while results did not. The cut is a response to a leadership failure, not a technology failure. Is the failure of AI projects a sign that AI was overhyped? No. AI budgets are being cut while AI usage keeps growing. Employees who learned to work with the tool do not stop when a corporate licence is removed; they pay for it themselves or use their phones. Adoption is already permanent, which is the opposite of a technology that failed. Why do AI costs run out of control in large organisations? Untrained users ask broad, general questions repeatedly, which consumes far more usage and produces weaker results. Trained users narrow their questions quickly and reach the answer with less waste, so their costs fall over time. The runaway bill is a symptom of missing training, not of an expensive tool. Is now a good time to invest in AI, or should we wait? Now is the stronger moment to move, because the market’s recent failures have created detailed public data on what does not work. A leader can use that data to build a more specific and cheaper strategy than was possible before, while competitors are retreating. The time to move is when the market is hesitating, not when it is charging. What should a leader do first when an AI initiative overspends? Before cutting the budget, find out where the failure actually sat, usually in the absence of rules and training, and use AI itself to get that answer within a morning. Then ask what the overspend bought, because a team that exceeded its budget may be about to deliver years of value in months. Cutting before understanding can switch off the one thing that was working. Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. If you are leading your organisation through this, I work with a limited number of senior leaders each quarter. Get in touch at Anglero.com. --- ## Authentic Leadership: When the Breakdown Becomes Freedom URL: https://anglero.com/2026/06/26/authentic-leadership-when-the-breakdown/ Published: 2026-06-26 Most leaders brace for the breakdown. Almost none are told what waits on the other side of it.   After broken comes free: what opens up when a leader faces themselves The moment a leader stops hiding and faces who they really are, the breakdown turns into freedom. They keep the money, the titles and the life they built, but they lose the guilt and the performance, and they get to lead as themselves for the first time. That shift is not only personal. It is the point where the leader, and the company they run, finally become what they were capable of. On stage I spend a long time taking a leader apart, through the honest reckoning that has to come before any strategy. People brace for the fall and forget there is a second half. This is the second half, and almost no one talks about it. The instant the breakdown becomes freedom The moment after you realise you are broken, you realise you are free. Everyone can see you now, and there is nothing left to protect. You have nothing to lose and everything to gain, and a whole life ahead of you that you have not yet lived. You still have the same money. You still have the same titles, the same home, the same life you built. What you no longer carry is the guilt and the need to keep the story going. It is the most alive a person can feel, the realisation that there is a whole life ahead of simply being yourself. You stop being ashamed You stop hiding the way a child hides, ashamed of the wrong shoes or the wrong clothes, certain that being seen as you are would cost you everything. You are finally an adult. You can be proud of who you are and what you do, and you stop needing the title to tell you who that is. What finds you on the other side Real respect arrives, the kind that comes from people who respect you rather than your money. Self-confidence arrives. Not everyone will welcome the change: some of the people who valued the old performance, or quietly envied it, will be unsettled by the new honesty, and a few may turn on it. It does not reach you the way it once would have, because you are no longer held by the old insecurities. Those insecurities become the rubber band that springs you into the life you were meant to have, and are now having. Why this is the whole game for the company This is not self-help dressed up as strategy. It is what authentic leadership actually is, and it is the foundation everything else sits on. A leader who is still performing asks the safe questions, of their people and of AI, the ones that protect the image. A leader who has stopped performing can finally ask the real questions, which is what tells you who should be anywhere near leading the work. That honesty is also where the future leaders you did not know you had step forward, and where the numbers finally start to move. The future is brighter for the company than for the leader alone. You become the role model. You become the leader people talk about when you are not in the room, the one you always wanted them to describe. It is right on the other side of simply being you. If you are a leader who senses it is time to do this work, it is the kind of work I do with a limited number of senior leaders each quarter. Work with Thomas Questions this article answers What happens to a leader after they stop hiding who they really are? The breakdown becomes freedom. Once a leader accepts what they have been avoiding, the fear of being seen falls away, because there is nothing left to protect. They keep the life they built but lose the guilt and the constant performance, and they begin to lead as themselves. Does a leader lose everything by facing the truth about themselves? No. They keep the money, the title, the home and the position. What they lose is the weight of pretending and the guilt that came with it. The change is internal, not material, which is why it costs nothing and yet changes everything. Why do some people turn against a leader who becomes more authentic? People who valued the old performance, or who envied it, are often unsettled when a leader stops performing, and a few may turn on the change. It rarely lands the way it once would have, because the leader is no longer held by the insecurities those people used to reach. How does a leader facing themselves affect the company, not just the person? A leader who is still performing asks safe questions that protect their image, of their people and of AI. A leader who has stopped performing can ask the real ones. That is why this personal work is the foundation of any honest strategy, and why the company gains the most from it. Is this a one-time realisation or an ongoing process? The shift itself can happen in a moment, the instant a leader stops running. Living it is the ongoing part: continuing to lead without the old mask, and continuing to deserve the respect that follows. The decision is quick; the practice is daily. Thomas Anglero is a Strategic AI Advisor (MerkabaPhi AS, Oslo), with 450+ keynotes across 30+ countries. Enquiries: Anglero.com --- ## Leadership Self-Awareness: Why Leaders Avoid It URL: https://anglero.com/2026/06/25/why-leaders-avoid-self-awareness/ Published: 2026-06-25 The awareness is usually already there. The distraction is built to keep it at bay.   The distraction machine: why most leaders wait for the event that breaks them open Yes, a leader can choose to face this before a crisis forces it. Almost none do. The reason is not that the awareness is missing; it is usually already there, humming under the surface. The reason is that most leaders have spent a career building distractions designed to keep that exact moment of honesty at bay, and when it surfaces, they reach for one more distraction rather than turn and face it. So this is the answer to the hardest version of the question: can a leader choose vulnerability now, before a diagnosis or a loss forces it on them. The capacity is there. The habit is against it. The awareness is already there Many of the leaders I have met quietly sense it: the gap between the public self and the private one, the feeling of being less whole, and less happy, than the image suggests. It is not hidden from them. They simply work very hard to keep it from surfacing. The distraction machine The way they keep it down is distraction. The next car, the bigger house, the holiday, the party, the next acquisition, a calendar packed so full there is no quiet left in it, and sometimes the drink. All of it spends real energy on a single job: making sure they never reach the moment when the engine is off, the house is silent, everyone is asleep, and there is no one left to perform for. Because in that moment they are left with themselves, and that is the company they have spent the most to avoid. Why it runs their decisions The same avoidance sits inside their decisions. To take a risk is to risk being seen, so they choose the safe option, follow the room, and keep their hand down in the meeting. They will spend a fortune to be seen by colleagues and the public, and almost nothing on being seen for who they actually are. The boldness a company needs from the top is the very thing the fear of exposure quietly removes. What it actually costs What they are hiding is rarely as terrible as they fear. More often it is simply emptiness, and a loneliness they would never admit to. But the cost of hiding it is real, and it lands in the decisions they avoid, the people they keep at arm’s length, and the chances they decline so that no one looks too closely. Can they choose it first? So, can a leader reach this honesty before a life event forces it? Yes. Anyone can. But choosing it means doing the one thing the whole machine was built to prevent: stopping, rather than adding another layer. Not buying the next thing, not filling the next hour, not pouring the next drink, but sitting in the quiet and letting themselves be seen, starting with by themselves. That is simple to say and very hard to do, which is exactly why most people wait for the event that takes the choice out of their hands. Why this is the root of AI strategy This is not a detour from AI. A leader still running the distraction machine asks AI and their people the safe, self-protecting questions, the ones that keep them unseen, which is the surest way to point the technology at the old data and get the old answers back. A leader who has stopped running can ask the real ones, and that is the reckoning that has to come before any strategy. It is also what tells you who is actually fit to lead the work: the person who does not need to be the most seen in the room, only the most honest. If you sense all of this and would rather choose it than wait for the event that forces it, that is the work I do with a limited number of senior leaders each quarter. Work with Thomas Questions this article answers Can a leader become self-aware before a crisis forces it? Yes, but very few do. The awareness is usually already present; what is missing is the willingness to stop and face it. Choosing it means ending the distraction rather than adding another layer to it. Why do many leaders avoid facing themselves? Because being seen as they truly are is the thing they fear most. They manage the gap between the public image and the private reality with distraction and busyness, spending heavily to be seen by others and almost nothing on being known for who they are. How does this avoidance show up in business decisions? As the safe yes or no. Taking a risk means risking exposure, so the leader follows the room and keeps their hand down. The boldness an organisation needs from the top is quietly removed by the fear of being seen. What does it actually take to choose this before a crisis? Deciding to stop running. Not the next purchase, the next full calendar or the next distraction, but sitting in the quiet and allowing themselves to be seen, beginning with by themselves. It is simple to describe and genuinely hard to do. What does a leader’s self-awareness have to do with AI? A leader avoiding themselves asks AI and their people the safe, self-protecting questions, which mostly produces a polished version of the past. A leader who has faced themselves can ask the real questions, which is the only place AI produces something genuinely new. Thomas Anglero is a Strategic AI Advisor (MerkabaPhi AS, Oslo), with 450+ keynotes across 30+ countries. Enquiries: anglero.com. Listen to the Clarity at the Top podcast on Spotify. --- ## “I See You”: The Speech a Leader Owes the Ninety-Nine Percent URL: https://anglero.com/2026/06/24/what-a-leader-owes-the-99/ Published: 2026-06-24 The few who rise are the celebrities. The 99 per cent are the cornerstone.   Leading the rest of the workforce through AI matters more than managing the handful of stars, because the 99 per cent who will never be the chosen few are the cornerstone of the company. They are the people who drive in the screws, fix what breaks, stay up all night, and deliver. While a leader is being broken open and rebuilt, and while a small number of hidden leaders rise, the question that decides whether the whole thing holds is what the leader owes everyone else. The answer starts with three words: I see you. The celebrities are not the cornerstone In any AI transformation, a small number of people rise and become the visible drivers of change. They get the attention. But visibility is not the same as foundation. The 99 per cent are the heart and soul of the company, and if a leader treats them as a backdrop to the stars, the culture quietly turns against the change. These are often the same people whose quiet competence the organisation already fails to notice until they leave. The speech a leader has to give So the leader has to stand up and say it directly. We are going to go through a process. I see you. I respect what you deliver every day. You do not have to become an AI expert, and you do not have to be the most talented person with these tools. But you do have to keep delivering, and you cannot become a hindrance to the change, because the culture of this company is going to change. That honesty is the same currency that makes a board willing to follow a leader through AI: respect first, then the ask. The leader is the bridge Here is the real tension. The 99 per cent are comfortable; they know they can deliver what they already know. The small group of risers will create new tasks that make everyone else deeply uncomfortable, because it is work they have never done. The leader has to stand in that gap as the bridge between the future and the past, between the known and the unknown. Not just keeping people happy, but making them progressive and action-oriented, and holding as much of the team together as possible through the discomfort. That is the work. Not choosing the stars over the soldiers, but carrying both across at once. If you want help leading a whole organisation through that change, not just its top five per cent, that is the work I do with leadership teams. Frequently asked questions Why does leading the 99 per cent matter more than managing the star performers? Because the 99 per cent are the cornerstone of the company, the people who fix what breaks, stay up all night and deliver. The visible few who rise during an AI transformation get the attention, but visibility is not foundation. If a leader treats the majority as a backdrop, the culture quietly turns against the change. What are the three words a leader owes the workforce? I see you. The leader has to stand up and say it directly: I respect what you deliver every day. You do not have to become an AI expert or the most talented person with the tools, but you do have to keep delivering and not become a hindrance, because the culture of the company is going to change. Do employees have to become AI experts to survive the transformation? No. They do not have to be the most talented person with the tools. What is asked of them is to keep delivering what they are good at and not to obstruct the change. The expectation is continued contribution, not reinvention as a specialist. What is the leader’s actual job during this change? To be the bridge. The comfortable majority and the uncomfortable risers have to be carried across at once, between the known and the unknown. The work is not choosing the stars over the soldiers, but holding as much of the team together as possible through the discomfort while keeping everyone action-oriented. Why is respect the starting point rather than the ask? Because respect first, then the ask, is the same currency that makes a board or a workforce willing to follow a leader through AI. People accept the hard requirement to keep delivering and to change the culture only once they have been genuinely seen and respected for what they already do. Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. --- ## What I Held Back on Stage: The Proud Leader Who Handed Off His Own Future URL: https://anglero.com/2026/06/23/leading-an-ai-project/ Published: 2026-06-23 Handing off the AI project and stepping back is not delegation. It is abdication.   Leading an AI project is not something you delegate to a likeable person and then admire from a distance. The most common failure I see is exactly that: a proud leader hands the work to someone who presents well, announces it as a win, and steps back into the past while the company’s whole future rides on a project they have stopped touching. I was once invited to headline a firm’s AI kickoff, and what I held back from the leader that day is what I will say plainly here. Why the choice itself revealed the problem The leader had picked the person in the organisation who spoke most fluently about AI, the most eloquent, the most liked. Not the person with the most demonstrated depth. And the moment you choose for fluency over substance, you have told me something uncomfortable: that you cannot tell the difference, which usually means you have not done the work yourself. If you were impressed that someone could talk well about ChatGPT, you have probably done very little, and the project is likely going nowhere. An AI project is not a communications exercise. It is an innovation effort that has to change how the company works, its culture, and people’s habits, which is some of the hardest work there is. The person who can actually drive that is often not the most comfortable one to be around, because they tell the truth people do not want. The KPI question that exposes everything So I would ask the leader directly: what are your KPIs for this? What are the project’s? What standard are you holding yourself to while this runs? Because what you have actually described to me is that you assigned the entire thing to someone else and felt proud of it. But the measure that matters is your customers: are they more profitable, more efficient, given genuinely new models, shown what competitors are doing. If your real measure is whether you avoided embarrassing yourself, the project has already failed. This is the same accountability gap that turns AI output into workslop nobody owns. Your future is not something you hand off This has to involve you, the leader, every single day. Not running it, but in it, side by side, because it is your future on the line, not the project owner’s. They will not be quietly reprimanded at a review in a year. The entire organisation’s direction is what is at stake. The way the project was announced, the framing of it as something handed off so the leader could go back to business as usual, was itself the first sign it had failed. That is what I held back that day. The honest alternative is to decide, before it starts, that you will own the discomfort and protect the people doing the real work. If you want help owning an AI initiative properly rather than delegating it into failure, that is the conversation I have with leaders. Frequently asked questions Is delegating an AI project to a capable person the right move? Not if the leader then steps back. Leading an AI project is not something you hand to a likeable person and admire from a distance. The most common failure is exactly that: the work is handed off, announced as a win, and the leader returns to business as usual while the company’s future rides on a project they have stopped touching. Why is choosing the most fluent person to lead AI a warning sign? Because choosing fluency over substance suggests the leader cannot tell the difference, which usually means they have not done the work themselves. An AI project is not a communications exercise; it is an innovation effort that has to change how the company works. The person who can drive that is often not the most comfortable to be around, because they tell truths people do not want to hear. What is the KPI question that exposes a failing AI project? Ask the leader directly what their own KPIs are, and what standard they are holding themselves to while the project runs. The measure that matters is customers, whether they are more profitable, more efficient, given genuinely new models. If the real measure is whether the leader avoided embarrassing themselves, the project has already failed. How involved does a leader actually need to be? Every single day, not running the project but in it, side by side. It is the leader’s future on the line, not the project owner’s, and the entire organisation’s direction is what is at stake. The owner will not be quietly reprimanded at a review in a year; the whole company will feel the outcome. What is the difference between delegation and abdication here? Delegation shares the work while the leader stays accountable and present. Abdication hands the project off so the leader can go back to business as usual. Framing an AI initiative as something handed away is itself the first sign it has failed, because the leader has separated themselves from the future that depends on it. Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. --- ## Shed the Skin First: The Painful Off-Site That Has to Come Before Any AI Strategy URL: https://anglero.com/2026/06/22/shed-the-skin-before-ai/ Published: 2026-06-22 A snake sheds its skin to grow. A company has to do the same before any AI strategy will hold.   Before building an AI strategy, most companies skip the one thing that makes it work: an honest, painful reckoning with what the company actually is. They reach for a tool, a trendy hire, or an off-site full of action points, and they wonder later why nothing moved. The reason is simple. You cannot build a real AI strategy on top of a story the company tells about itself. You have to strip the story away first. The expensive off-site that produces nothing Gather a room of people who have never seriously used AI, and you will get a very expensive off-site and still no strategy. You will leave with a deck of bullet points, each assigned to someone else, and nothing will change. You do not need an off-site for that. What you need is the opposite kind of room: the people who understand your company intimately, sitting with people who genuinely know how to build with AI in real time, querying the data and the models back and forth, and refusing to look away from what they find. Three days of shedding before any strategy The honest version hurts, on purpose. Day one, you dig into what the company really is, what it has actually done, where it failed, what it overlooked. Day two, you dig harder, and it gets more uncomfortable, the kind of uncomfortable where people want to go home. Day three, you stop being able to hide from the conclusions, because the analysis has no feelings and no political agenda. It is concrete, specific, and it will sting. You can ask AI to soften its tone, but if you need it softened, you are usually closer to losing the thing than you realise. By the end of those three days you are not strategising. You are stripped of the comfortable fictions of corporate life. And that is the point. The reason your AI project is returning nothing is that it was built on those fictions in the first place. This is the same pattern as AI being a culture project before a technology project: the foundation has to be honest before anything built on it can hold. The marriage that looks fine and is rotten underneath It is like a marriage you cannot repair until you have the painful conversation where everything unsaid finally gets said. Skip that conversation, keep buying gifts and booking trips while you quietly resent each other, and the whole thing is rotten at the core. Most struggling companies are exactly that: a relationship maintained on appearances, with the real conversation never had. That is why the projects fail. Shed the skin, then build A snake sheds its skin so it can grow into what it is meant to become. Your company has to be willing to do the same, to call the old story what it is and let it go. Shed the skin first. Then build the AI strategy. Only then does the P&L actually start to move. If you want someone to lead a room through that reckoning without letting it stay comfortable, that is the work I do with leadership teams. Frequently asked questions What has to come before building an AI strategy? An honest, painful reckoning with what the company actually is. Most companies skip it, reaching for a tool, a trendy hire or an off-site full of action points, then wonder why nothing moved. You cannot build a real AI strategy on top of the story a company tells about itself; the story has to be stripped away first. Why does the usual AI strategy off-site produce nothing? Because it gathers people who have never seriously used AI and ends in a deck of bullet points assigned to others, with nothing changing. What is needed instead is the people who understand the company intimately sitting with people who genuinely know how to build with AI, querying the data and models in real time and refusing to look away from what they find. What actually happens in the three days of “shedding”? Day one digs into what the company really is, what it has done, where it failed, what it overlooked. Day two digs harder and gets more uncomfortable. Day three, the conclusions can no longer be hidden from, because the analysis has no feelings and no political agenda. It is concrete and specific, and it will sting. Why compare a struggling company to a failing marriage? Because both are relationships maintained on appearances with the real conversation never had. Keep buying gifts and booking trips while quietly resenting each other, and the thing is rotten at the core. A company that never has the honest conversation is in the same state, which is why its AI projects fail. What does shedding the skin mean for a business? A snake sheds its skin so it can grow into what it is meant to become, and a company has to be willing to do the same, to call the old story what it is and let it go. Only after that honest reckoning is the AI strategy built on solid ground, and only then does the P&L actually start to move. Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. --- ## The Sleeper Has Awoken: Why Your Future Leaders Are Not the Ones in Charge Today URL: https://anglero.com/2026/06/21/future-leaders-not-todays-leaders/ Published: 2026-06-21 The leaders you need are already inside the building, waiting for a structure safe enough to step into. Finding future leaders for AI starts with a hard truth: more than ninety per cent of your company’s future leaders are not your current leaders, and you do not find them by recruiting. You find them by building a structure safe enough that they identify themselves. The question current leaders have to answer The current leaders are not automatically discarded, but they face a hard question: are you willing to pay the personal price to change, to take apart your own belief systems, to list honestly the decisions you got wrong, and to become someone different? If yes, you remain useful and can be a future leader too. If no, you are not useful any more, and that is a choice you made, not a sentence handed to you. You do not hunt for the new leaders. They find you. The instinct is to go hunting. That instinct is wrong, because they find you. Create a role that is genuinely protected, untouchable by corporate politics and the usual agendas, and make it safe to step into, and the right people raise their hand and say, that is me. In a company of ten thousand you might have one or two. They are there, and they are waiting. The expression fits exactly: the sleeper has awoken. Once they have your backing, they work without stopping, because they have waited their whole career for permission and protection. These are often the same people already quietly outperforming with AI while the organisation fails to notice. Keep your old judgment out of the room You will be shocked by who they turn out to be. Your only job is to keep your old picture of them out of the room. The person you had filed under a particular ceiling is often the one who steps up, and if the old judgment walks in with you, you will miss them. The same applies to people outside the company. How do you signal the door is open? You announce what you are doing, and you make the announcement sound a little crazy. You will attract some time-wasters, but the right ones, internal and external, you will know within seconds. Vulnerability is the whole thread This requires doing something you have not done before: being open, and being willing to be vulnerable. That is the thread running through everything in AI. The more willing you are to admit you got things wrong, that your assumptions were wrong, that you are not quite the person you told everyone you were, the more you get out of AI, because you start asking it honest questions. The leader in denial asks AI only the questions that protect their image, and gets back hollow answers. It is the same reason boards are the furthest behind on AI: the gap is not technical, it is the willingness to be exposed. So be vulnerable, build the safe structure, and keep your old judgments at the door. The leaders you need are already inside the building, waiting to be let in. Helping leaders build that structure is what I do with executive teams. Frequently asked questions Where do a company’s future AI leaders actually come from? More than ninety per cent of them are not your current leaders, and you do not find them by recruiting. You find them by building a structure safe enough that they identify themselves. The leaders you need are already inside the building, waiting for permission and protection to step forward. Are today’s leaders simply discarded? No. They face a hard question: are you willing to pay the personal price to change, to take apart your own belief systems, to list the decisions you got wrong honestly, and to become someone different? If yes, they remain useful and can be future leaders too. If no, they are no longer useful, and that is a choice they made, not a sentence handed to them. Should a leader go hunting for these hidden people? No, the instinct to hunt is wrong, because they find you. Create a role that is genuinely protected, untouchable by corporate politics, and make it safe to step into, and the right people raise their hand. In a company of ten thousand there may be only one or two, but they are there and waiting. What most often stops a leader from seeing them? Their own old judgment. The person filed under a particular ceiling is often the one who steps up, and if the old picture walks into the room, the leader misses them. The single job is to keep that outdated judgment out of the room and let people surprise you. Why is vulnerability the thread running through all of this? Because being open about getting things wrong is what lets a leader ask AI, and their people, honest questions. A leader in denial asks only the questions that protect their image and gets hollow answers back. The same willingness to be exposed is what separates leaders and boards who benefit from AI from those who fall behind. Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. --- ## Into the Chasm and Back: The Three Phases Every Real AI Project URL: https://anglero.com/2026/06/20/three-phases-of-a-real-ai-project/ Published: 2026-06-20 Down into the chasm first, then up. The phase everyone wants to skip is the one that matters.   The phases of an AI project are three, and six months in, the result a leader should point to is not a dashboard or a finished rollout. It is people. Every person in the company should have been part of it, and each should have a story about what the work uncovered in themselves. If your six-month marker is a tidy metric instead of a company full of honest, uncomfortable stories, you did the fake version. Phase one: down into the chasm Phase one is discovery, and it hurts. It is a flattening before any building, the descent before the climb. Everybody wants the other version, start Monday and bank a hundred million by Friday, and it does not work that way. With genuine company-wide adoption you lose ground in the first six months, because phase one uncovers not just technical problems but people problems and cultural ones, and every problem you uncover has to be dealt with, not noted on a slide and ignored. This is why AI is a culture project before it is a technology project, and why the beginning is the beast. Phase two: finding who rose Phase two is about people, specifically who came out of phase one saying they feel reborn, that they finally see where to take this. That is not everyone, and it should not be. Most people are good soldiers who do what they are told well, and a company needs them. But here is the part that unsettles every executive I say it to: more than ninety per cent of your future leaders are not your current leaders. They are people in the wrong jobs, built for something you never saw, and some are not even inside your company yet. You are looking for the ones who walk toward the fire. Phase three: implementation, where you reap it Phase three is building what phase two strategised, and it is where the result finally arrives. The reason the whole arc always takes longer than the plan is that a good AI project keeps uncovering problems, and uncovering a problem obligates you to solve it. That is not a delay in the work. That is the work. The numbers at the end are real, but as with the way the P&L only moves after the honest work is done, they come last, not first. Be suspicious of a painless six months When someone shows you a clean six-month result with no pain in the story, be suspicious. The companies that did it honestly have a harder, better thing to show you: a workforce that knows itself, and a short list of the people who will lead what comes next. If you want help running that arc without flinching at phase one, that is the conversation I have with leadership teams. Frequently asked questions What are the three phases of a real AI project? Discovery, which is a painful descent that uncovers technical, people and cultural problems; a people phase that identifies who rose and who will lead what comes next; and implementation, where what was strategised gets built and the numbers finally arrive. The order matters, and the result is not a dashboard but a company full of honest stories. Why does a genuine AI project lose ground in the first six months? Because phase one is discovery, and it hurts. Real company-wide adoption surfaces not just technical problems but people and cultural ones, and every problem uncovered has to be dealt with rather than noted on a slide. The flattening comes before any building; the beginning is the beast. What should a leader be able to point to at the six-month mark? People, not a metric. Every person should have been part of the work and have a story about what it uncovered in them. If the six-month marker is a tidy dashboard instead of a company full of honest, uncomfortable stories, the leader did the fake version. Why does an AI project always take longer than planned? Because a good one keeps uncovering problems, and uncovering a problem obligates you to solve it. That is not a delay in the work; it is the work. The real numbers come at the end, after the honest effort, not at the start. Why be suspicious of a painless six-month result? Because honest, company-wide adoption is uncomfortable, so a clean story with no pain usually means the hard version was skipped. The companies that did it properly have a harder, better thing to show: a workforce that knows itself and a short list of the people who will lead what comes next. Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. --- ## The Living Embodiment of AI: Who Should Actually Lead Your AI Initiative URL: https://anglero.com/2026/06/19/who-should-lead-your-ai-initiative/ Published: 2026-06-19 Energy and buzzwords are not qualifications. The right leader lives inside the technology. The right person to lead your AI initiative is the living embodiment of AI, and you judge them on three things: culture, technology, and leadership. Not on energy, not on buzzwords, not on how well they present. You find this person with one question, and then you listen to how they answer it. The question that exposes them in thirty seconds Ask them what they have done in the last forty-eight to seventy-two hours with AI. What were the pain points, how much sleep did they lose, what did they struggle with, what questions were they asking, what did it uncover, what did they learn. Then stop talking. The right person will go for thirty minutes without pausing, and you should come away overwhelmed by everything they said. The wrong person gives you a sentence about ChatGPT and changes the subject. This is the same litmus test that separates a real AI speaker from a futurist on a vetting call: recency and depth, not polish. Culture: they have already been changed by it This person lives inside AI, and it has changed how they see everything. They have used it enough that it tore up their own assumptions, and they cannot see their work, or their life, the same way anymore. They keep pushing, and they have made themselves genuinely comfortable inside the discomfort. That is the cultural marker, and it matters more than any certificate. Leadership: a visionary who holds the vision loosely This person has a picture of where things are going, and intends to use AI to tear that picture apart, because whatever they imagine today, the real outcome will be something else. If they are experienced, they have done this before. If they are young and have not, they are willing to sacrifice everything, they listen exceptionally well, and they respect the value of data and of the people who came before them. Technology: a tool, never the answer They understand the hype around every model and can tell you which ones solve nothing. They see how the pieces fit and can architect a solution in their head. But they know the technology is just the rope that ties the trees together in the forest. It is a tool. This is the same trap that makes AI demos fail in front of a leadership audience: mistaking the tool for the solution. The part most leaders get wrong This person understands that the honest answers will expose where leadership was wrong, or lazy, or pointed the company in the wrong direction, and they will say it anyway. It is the emperor’s new clothes. Someone finally says the emperor is naked, and that means the whole organisation has been walking around exposed. Which is why the real job is yours, not theirs: you have to decide, before they start, that you will protect the person who tells you the truth on the day nobody wants to hear it. That decision is leadership, and it is the same uncomfortable honesty that determines whether your P&L ever actually moves. That is who you hire. Not the most eloquent person about AI. The one already living it, who will tell you the truth you do not want, and who you have decided in advance to protect when they do. If you want this thinking brought to your own leadership team, that is the work I do from the stage and in advisory rooms. Frequently asked questions Who is the right person to lead an AI initiative? The living embodiment of AI, judged on three things: culture, technology and leadership. Not energy, buzzwords or presentation skill. It is someone already living inside the technology, who will tell you the truth you do not want to hear and who you have decided in advance to protect when they do. What single question reveals whether someone genuinely uses AI? Ask what they have done with AI in the last forty-eight to seventy-two hours, the pain points, the lost sleep, the questions, what it uncovered, then stop talking. The right person will go for thirty minutes without pausing. The wrong one gives a sentence about ChatGPT and changes the subject. Recency and depth, not polish. What is the cultural marker to look for? That the person has already been changed by AI. They have used it enough that it tore up their own assumptions and they cannot see their work or life the same way. They keep pushing and have made themselves comfortable inside the discomfort, which matters more than any certificate. How should the right leader treat the technology itself? As a tool, never the answer. They understand the hype around every model and can tell you which ones solve nothing, and they can architect a solution in their head. But they know the technology is just the rope that ties the trees together in the forest. Mistaking the tool for the solution is what makes AI demos fail. What is the part most leaders get wrong? That the real job is theirs, not the hire’s. The right person will expose where leadership was wrong or lazy, the emperor’s new clothes, and say it anyway. The leader has to decide before they start that they will protect the person who tells the truth on the day nobody wants to hear it. That decision is leadership. Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. --- ## Workslop Is Not an AI Problem, It Is a Failure of Accountability URL: https://anglero.com/2026/06/18/workslop-failure-of-accountability/ Published: 2026-06-18 Workslop looks finished and is hollow. The colleagues who clean it up pay for it.   Workslop, the AI output that looks finished but is hollow and leaves colleagues losing hours cleaning it up, is not an AI problem. It is an accountability problem wearing AI’s clothes. The rules of work never changed: a leader is responsible for what their people deliver, and the way out is leadership, not better tooling.   What workslop actually is Workslop is not new. Before AI, people produced work they had not checked, handed it on, and a group of colleagues stayed up all night fixing it. AI has not changed that. It has only made it faster to produce, and more polished on the surface, so the hollowness hides better. Underneath it is the same thing it always was: work passed on without care.   Why workslop is a leadership failure, not an AI failure If workslop is rising in your company, it means people are doing less of the real work and letting the tool stand in for the thinking. That is not something you fix with a better model. AI should be raising the quality of what lands on your desk, giving people better data and removing the slop. If it is doing the opposite, the problem is accountability, and accountability is a leadership job. It is the same reason AI is a culture project before it is a technology project. Left alone, workslop does real damage. The people who still care end up clearing up after the people who do not, and nobody wants to come to work for that. That is how AI quietly corrodes a culture, and a corroded culture is where the financial trouble begins.   What a leader does about workslop You make accountability plain. Be proud that your people use AI, and be just as clear that producing workslop carries a consequence. If someone’s output has to be cleaned up by someone else, that is dealt with directly: a conversation, then a review, and if it continues, removal from the work. You use AI to lift the company, not to finish the task and leave the mess for others. This is also why who you put in charge of the work matters so much. None of this is about AI. It is ordinary leadership, the kind that existed long before any of these tools, a leader taking responsibility for what their people deliver and holding the line on standards. The companies that keep that line, and use AI to raise it rather than dodge it, become places the best people actually want to be. That is leadership, and it is what every real AI initiative actually runs on.   Frequently asked questions What is workslop? Workslop is AI output that looks finished but is hollow, leaving colleagues to lose hours cleaning it up. It is not new and not really an AI problem; it is work passed on without care. AI has only made it faster to produce and more polished on the surface, so the hollowness hides better. Why is workslop a leadership failure rather than an AI failure? Because if it is rising, it means people are letting the tool stand in for the thinking, and no better model fixes that. AI should raise the quality of what lands on your desk. If it is doing the opposite, the problem is accountability, and accountability is a leadership job. What damage does workslop do if it is left alone? The people who still care end up clearing up after the people who do not, and nobody wants to come to work for that. It is how AI quietly corrodes a culture, and a corroded culture is where the financial trouble begins. What should a leader actually do about workslop? Make accountability plain. Be proud that people use AI and equally clear that producing workslop carries a consequence: a direct conversation, then a review, and if it continues, removal from the work. AI is there to lift the company, not to finish the task and leave the mess for others. Has AI actually changed the rules of work? No. A leader has always been responsible for what their people deliver, and that has not changed. Workslop is an old accountability problem wearing AI’s clothes, and the way out is ordinary leadership that holds the line on standards rather than better tooling.   Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. --- ## The P&L Moves Only After the Leader Does the Work URL: https://anglero.com/2026/06/18/pnl-moves-after-leader-does-the-work/ Published: 2026-06-18 Old data and old questions protect the losses. New questions move the P&L.   Once a leader has done the hard, honest work on themselves and the company, the P&L turns into simple arithmetic. The value leaks long before that, in companies that point AI at their old data and ask it the same comfortable questions that produced the losses in the first place. Change the questions and the numbers follow. Where the financial value leaks Most companies feed AI the old data, what the company has always said it is, and ask it to confirm the story they already believe. That old data is your current P&L, and usually a great deal of the L, which is why you are looking at AI to rescue you. Asking a powerful tool to defend a false picture of the business moves nothing. It just dresses up the same wrong answers. This is the same reason the honest reckoning has to come before any AI strategy. The sequence that actually moves the P&L The investment differs for every company and every group inside it, but the architecture is the same. First, the data. Not only the old data, but new data, found by asking the right questions about what the company actually is, where it failed, and where it is really going. That core picture will usually be ugly, and the forecast uglier. That is the point. Second, the strategy, built on top of that honest picture, with AI used as a tool and not as a saving light. A tool, nothing more. Third, the right people alongside the AI, and then implementation, which might be a handful of people or thousands of AI agents, depending on what you are trying to achieve. It is the same architecture as the three phases of a real AI project. The most important step is the first. The new data comes from new people asking questions the company has never asked itself, which is why who you put in charge of the work matters so much, and none of it happens unless a leader is willing to go down a painful path and see that they were not the leader they believed they were. That is why the inner work comes first. Skip it, and you are back to old data and old questions, which is exactly where the losses came from. When the numbers move There is no fixed timeline. Depending on the ambition, profitability can arrive in days, in months, or over a few years. The workflow is always the same; only the scale of the goal changes. What does not change is the order: the leader does the honest work, the real data surfaces, the strategy is built on it, and only then does the P&L start to move. The numbers are the easy part. The work that unlocks them is not.   Frequently asked questions Why does AI fail to improve the P&L in most companies? Because they point AI at their old data and ask it the same comfortable questions that produced the losses in the first place. That old data is your current P&L, usually a great deal of the L. Asking a powerful tool to defend a false picture of the business moves nothing; it just dresses up the same wrong answers. What is the sequence that actually moves the P&L? First the data, both old and new, found by asking honest questions about what the company really is and where it failed. Second the strategy, built on that honest picture with AI used only as a tool. Third the right people alongside the AI, then implementation. The order does not change; only the scale of the goal does. Which step matters most? The first. The new data comes from new people asking questions the company has never asked itself, and none of it happens unless the leader is willing to go down a painful path and see they were not the leader they believed they were. That is why the inner work comes first. How long does it take for the numbers to move? There is no fixed timeline. Depending on ambition, profitability can arrive in days, in months, or over a few years. The workflow is always the same; only the scale of the goal changes. What does not change is the order: honest work first, real data next, strategy on top, and only then the P&L. Why is the inner work of the leader a financial issue and not just a personal one? Because skipping it sends you back to old data and old questions, which is exactly where the losses came from. The numbers are the easy part once the honest work is done; the work that unlocks them is the hard part, and it starts with the leader. Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. --- ## The Two-Hour Test: Red Flags on a Speaker Call | Thomas Anglero URL: https://anglero.com/2026/06/17/ai-speaker-red-flags/ Published: 2026-06-17 Thomas Anglero on the questions that reveal a real AI speaker.   The two-hour litmus test: three red flags on a vetting call   On a fifteen-minute call with a potential AI speaker, the single most revealing question is: how have you used AI in the last two hours? A practitioner answers instantly, fast and specific, and could carry on for an hour. The three red flags that should end the call are a rehearsed speech, eyes that dart when you ask something off-script, and stories that are all positive. Ask the question they did not prepare for Speakers know how to play the call. They arrive with a polished mini-keynote aimed at you, jumping between stories, filling the time, ticking the box. So do not let them run it. Ask a curveball. Ask about themselves. Ask what they built in the last two hours and to walk you through it, almost screen by screen. Someone who has woven AI into their work will not even pause. Someone who has not will reach for a vague answer about ChatGPT. Watch for the stumble When you ask the off-script question, watch the eyes. A practitioner gets faster and more energetic, almost too fast to follow. The pretender slows down, looks around, and slides back into speech mode. Do not let them. Tell me how AI changed your work in the last day. If they are real, you will struggle to get them to stop. The all-positive tell Here is the one most people miss. If every story they tell you is a success, that is a warning sign. Anyone genuinely working with AI right now has fresh scars: the thing that broke this week, the approach that was right a month ago and is useless today, the hours lost. No scars means no real use. If the stories are all wins, write them off and end the call. What this means for your shortlist You do not need to understand AI to run this test. You need to ask the unscripted question and watch how they answer it. The full set of screening questions is worth having in front of you on the call, but the two-hour question alone tells you most of what you need. The decision is yours, and ten minutes of the right questions protects you from an hour of regret on the day. You can see what a real one looks like in full flow. Watch a keynote. Questions this article answers What questions should you ask when vetting an AI keynote speaker? Ask the one they did not prepare for. The single most revealing question is how they have used AI in the last two hours, and then to walk you through it almost screen by screen. Follow it with how AI changed their work in the last day. A practitioner answers instantly and specifically and struggles to stop; a pretender reaches for a vague answer about ChatGPT. What are the red flags on a speaker vetting call? Three. A rehearsed speech, a polished mini-keynote aimed at filling the time rather than answering you. Eyes that dart and a slide back into speech mode when you ask something off-script. And stories that are all positive. Any of the three should end the call. How do you tell if a speaker genuinely uses AI? By the scars. Anyone genuinely working with AI right now has fresh ones: the thing that broke this week, the approach that was right a month ago and is useless today, the hours lost. If every story is a success, that is the warning sign, because no scars means no real use. You do not need to understand AI yourself to run this test; you only need to ask the unscripted question and watch how they answer.   Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. --- ## Eyes, Ears, Mind, Soul: What a Keynote Fee Buys | Thomas Anglero URL: https://anglero.com/2026/06/17/what-a-keynote-fee-buys-2/ Published: 2026-06-17   Thomas Anglero taking a room through the four stages of a keynote. Eyes, ears, mind, soul: what a keynote fee actually buys The difference between a fifteen thousand dollar AI speaker and a fifty thousand dollar one is how deeply they move the audience. The expensive speaker takes the room through four stages, eyes, then ears, then mind, then soul, until the trust forms and people decide to act. That progression, not the slide deck, is what the fee buys. The four stages First you touch their eyes, and they think you are worth watching. Then their ears, and they think you are worth listening to. Then their mind, and they begin to reflect on what you said. And then, if you are good enough, you reach the soul, and that is the moment they decide they trust you. Whatever you say next, they will act on. Most speakers stop at the eyes and the ears. The ones worth the higher fee reach the soul. Why I make a room uncomfortable before I make them laugh When I use humour on stage, it is usually to break a silence I created on purpose. I let a room sit in the weight of where they have fallen short, the project they did not fund, the decision they avoided, and I let it sit longer than is comfortable. Then I break it with a joke, so they can breathe, and then I build them back up. That is not cruelty. It is care. You cannot lift people who have not first felt the ground. The proof is what they do afterwards I have had people come up to me and say that after a keynote they left their company, moved country, started again, and their life is better for it. That happens because we went through those four stages together in the room. That is what the audience remembers, long after the slides are forgotten, and it is what a real fee reflects. What this means when you compare fees If you are weighing one speaker’s fee against another’s, you are not really comparing presentations. You are comparing how far into the room each one can reach. The cheaper speaker may inform. The one worth more will move people to change something on Monday morning. That choice is yours to make. You can judge the reach for yourself. Watch a keynote. Questions this article answers What is the difference between a cheap and an expensive keynote speaker? How deeply they move the audience. The difference between a fifteen thousand dollar AI speaker and a fifty thousand dollar one is not the slide deck but how far into the room they can reach. The cheaper speaker may inform; the one worth more will move people to change something on Monday morning. What does a high keynote fee actually pay for? The progression through four stages, eyes, ears, mind and soul, until trust forms and people decide to act. Most speakers stop at the eyes and the ears. The ones worth the higher fee reach the soul, which is the moment the audience decides to trust the speaker and will act on whatever comes next. That progression, not the deck, is what the fee buys. How does a great speaker build trust with an audience? By taking the room through the four stages in order, and often by making it uncomfortable before making it laugh. Letting a room sit in the weight of where it has fallen short, then breaking the silence with humour and building it back up, is not cruelty but care. You cannot lift people who have not first felt the ground. The proof is what they do afterwards, long after the slides are forgotten.   Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. --- ## “I Am AI”: Real AI Speaker or Futurist? | Thomas Anglero URL: https://anglero.com/2026/06/17/ai-speaker-vs-futurist/ Published: 2026-06-17 Thomas Anglero on the difference between using AI and forecasting it.   “I am AI”: the one sentence that separates a real AI speaker from a futurist The line that separates a real AI speaker from a futurist is simple: a practitioner can say “I am AI” and prove it. They build with AI every day, and they can tell you how it works, how it failed on them this week, and where it is heading, without pausing to think. A futurist reads the news and predicts six months out. The thing an event manager should look for is not the forecast. It is what the speaker has actually built and uses. Event managers are not AI experts, and that is the problem Most event managers are choosing an AI speaker without being AI experts themselves, so they do not know what to listen for. The honest figure is that very few speakers have genuinely woven AI into how they work. Many have touched ChatGPT. Far fewer live inside it. That gap is exactly where the wrong booking happens. The questions a practitioner answers without blinking A London bureau agent I spoke with recently asked me straight away about the AI agent and the AI council I had built. She knew what to ask, because she uses AI herself. Those are the right questions: what have you built, what are you using now, how has it worked, how has it failed, how would you build it differently today, where do you see it going? Someone who has actually built it answers every one of those without hesitation, because they are living inside the thing they are describing. The tell of a futurist A futurist is, with respect, someone who reads enough to tell you where technology is going in six months. That is not hard. A person who is using AI can tell you what went wrong in the last day, the mistake they made, the progress they fought for, why they are exhausted. There is a good question for an event manager: how exhausted are you? The pretender says they are not tired, just excited. The real one says, how much time do you have. That is the difference between talking about AI and being AI. What this means for your event If a speaker tells you they are a futurist who follows AI and can tell you where it is trending, you can end the call. If they can walk you through what they built and broke this week, you have found the rare one. The themes I speak on all come from that place, from building and failing, not forecasting. You can hear the difference for yourself. Watch a keynote. Questions this article answers What is the difference between a real AI speaker and a futurist? A practitioner can say “I am AI” and prove it. They build with AI every day and can tell you how it works, how it failed on them this week, and where it is heading, without pausing. A futurist reads the news and predicts six months out. The thing to look for is not the forecast but what the speaker has actually built and uses. What questions should an event manager ask an AI keynote speaker? What have you built, what are you using now, how has it worked, how has it failed, how would you build it differently today, and where do you see it going. A person who has genuinely built with AI answers every one without hesitation. A useful extra question is how exhausted they are: the pretender says they are not tired, just excited, while the real one asks how much time you have. How can you tell if a speaker actually uses AI in their own work? They can tell you what went wrong in the last day, the mistake they made, the progress they fought for. Many speakers have touched ChatGPT; far fewer live inside it. If a speaker presents themselves as a futurist who can tell you where AI is trending, you can end the call. If they can walk you through what they built and broke this week, you have found the rare one.   Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. --- ## The Event Manager as Virtual CEO | Thomas Anglero URL: https://anglero.com/2026/06/17/event-manager-as-virtual-ceo/ Published: 2026-06-17 Thomas Anglero on why the right booking changes a company.   The event manager as virtual CEO: the power of choosing the right speaker Booking the right speaker is not filling a slot on an agenda. The event manager who chooses well becomes the reason the organisation moves, and leadership starts treating them as a trusted advisor, almost a virtual CEO. The wrong booking is easy to spot in hindsight: it is the speaker who, a year from now, has swapped the word AI for the next trend and is running the same talk. What happens to the pretenders The fakes are predictable. They take the hot word of the moment, drop it into the same deck, add a few new graphs, and call themselves a futurist again. Perhaps they let AI write them another book. Same formula, new label. What you are actually looking for is the opposite of a formula: someone who has lived through the chaos and can pull the signal out of it for your audience. I have been through the failures, the lost money, the lost sleep, and that is what I bring to a room, not a forecast. Where this is all going AI will end up inside everything, and at some point we will stop talking about it as a separate thing, the way we stopped talking about electricity. We will only talk about the consequences: what it did to our businesses, our families, our health, our work. The change does not stop. That is the landscape your audience is walking into, and they need someone who can make sense of it, not narrate it. Why the right choice makes you the trusted one When you pick the speaker who actually moves the room, leadership notices. They begin to see you as the person with judgement, the one to come to next time they need someone who can shift the organisation. That is real standing. The right speaker can be the inflection point where a company changes direction, and you were the reason it happened, even if no one says so out loud. That is the power in your hands. What this means for your next booking So treat the choice as what it is: not an item to tick off, but a decision that reflects on you and shapes what your audience does next. The themes worth putting in front of leaders right now are the ones grounded in having built and failed, because that is what your people can actually use. See what that looks like before you decide. Watch a keynote. Questions this article answers Why does choosing the right keynote speaker matter so much? Because it is not filling a slot on an agenda; it is a decision that can be the inflection point where a company changes direction. The right speaker moves the organisation, and the event manager who chose them becomes the reason it happened, even if no one says so out loud. What happens to “AI futurist” speakers over the next year? They swap the word AI for the next trend and run the same talk. The fakes are predictable: they take the hot word of the moment, drop it into the same deck, add a few graphs, perhaps let AI write them another book, and call themselves a futurist again. Same formula, new label. How does booking the right speaker reflect on the event manager? It makes them the trusted one. When they pick the speaker who actually moves the room, leadership notices and starts treating them as the person with judgement, almost a virtual CEO, the one to come to next time the organisation needs to be shifted. That is real standing.   Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. --- ## Why Boards Are the Furthest Behind on AI, and How to Raise Yours URL: https://anglero.com/2026/06/16/boards-furthest-behind-on-ai-2/ Published: 2026-06-16 Thomas Anglero explaining AI to the Chairman of the Board of the Oslo Cancer Cluster Incubator (Norway)   The board is usually the part of an organisation that understands AI the least, and it is also the part that holds the final decision on it. You cannot close that gap with a single training session. A board is raised the way any group of people is convinced, through results it can see and verify, and the work begins with the leader, not the board.   Why boards are the furthest behind on AI New research this month makes the gap measurable. The Board Value Index from Board Intelligence surveyed more than four hundred directors and chief executives. More than four in five, 84 percent, said their boards have discussed which decisions should stay human-led and which AI can take on. Only about half went any further than the discussion. In the same survey, 40 percent believed boards themselves will need little or only incremental change, and only 37 percent saw their board as essential to value creation. Set that against what is happening below them. The staff a few floors down are already past the question of whether to trust AI and are using it to do the work. So the room with the most authority is the one furthest behind, and it still holds the decision. That gap is exactly why boards can no longer treat AI as someone else’s problem. You have probably sat in the meeting. You bring the board work that AI helped you build, a sharp read of the market, a model your competitors do not have, and instead of a decision you get a debate about whether the tool can be trusted. The room slows down at exactly the point it should move.   Why a training session does not fix it A board does not learn AI in a slide deck any more than your company did. For most boards, acting does not mean what it sounds like. It means funding a few seats of ChatGPT and calling it progress. That is not acting. It is falling behind in a more expensive way.   How a leader raises a board on AI Through results, not lectures. You bring the board outcomes, a new market, a revenue line, a risk they could not see, and when they ask how you got there, you talk about your AI work plainly, as a matter of fact, the way you would talk about a spreadsheet. You show your working. You repeat how the data is backed, where the risk sits, and how you have brought it down. They will be impressed and frustrated and nervous all at once, and the job is to make them comfortable in their own skin again. This is the part nobody tells a leader. You end up doing the job twice. You are the steady presence for your company through the change, and then you turn around and patiently raise a board that understands the least and decides the most. It is a heavy lift, and it is the job now. The companies that pull ahead this decade will not be the ones with the best tools. They will be the ones whose leaders were willing to teach the room above them. If that is the room you stand in front of, it is worth seeing what leading through AI looks like done well.   Frequently asked questions Why are boards the furthest behind on AI? Because the board is usually the part of an organisation that understands AI the least while holding the final decision on it. The Board Value Index surveyed more than four hundred directors and chief executives: 84 percent said their boards had discussed which decisions should stay human-led, but only about half went further than the discussion, and just 37 percent saw their board as essential to value creation. Meanwhile the staff a few floors down are already using AI to do the work. Can a training session bring a board up to speed on AI? No. A board does not learn AI in a slide deck any more than the wider company did. For most boards, acting means funding a few seats of ChatGPT and calling it progress, which is not acting but falling behind in a more expensive way. How does a leader actually raise a board on AI? Through results, not lectures. You bring the board outcomes, a new market, a revenue line, a risk they could not see, and when they ask how you got there you explain your AI work plainly, as a matter of fact, the way you would talk about a spreadsheet. You show your working, repeat how the data is backed and where the risk sits, and make them comfortable in their own skin again. What does the board gap cost a company? Speed at the moment it matters most. You bring the board a sharp read of the market or a model competitors do not have, and instead of a decision you get a debate about whether the tool can be trusted. The room with the most authority slows down at exactly the point it should move, which is how well-run companies fall behind. Whose job is it to close the gap? The leader’s. You end up doing the job twice: being the steady presence for your company through the change, then turning around to patiently raise a board that understands the least and decides the most. The companies that pull ahead this decade will be the ones whose leaders were willing to teach the room above them.   Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. --- ## The First Sixty Seconds: How to Open a Keynote | Thomas Anglero URL: https://anglero.com/2026/06/16/how-to-open-a-keynote/ Published: 2026-06-16 Thomas Anglero opening a keynote and handing the room control.   “Shoot arrows at me”: how the first sixty seconds build trust with 400 strangers When you stand in front of 400 executives who are quietly afraid of AI, the first sixty seconds are not about your credentials or your agenda. They are about handing the room control. You tell them they can interrupt you at any moment, challenge you, put their worst fear on the table, and that you are there to answer it rather than perform at them. Trust is built by giving it away first. What I actually say in the first minute I tell them the truth. If you do not listen, the stress you feel now is nothing next to the stress of failing. Then I hand them the room. We are going to have a Q&A, and it starts now. Interrupt me whenever you like. Do not be polite. Yell out the situation that is keeping you awake, the one where you are about to lose your job or be hauled in front of the board, and I will give you everything I have. Shoot arrows at me, because I am not going to shoot any back. I am only going to give you my perspective, my honest answer, and my full attention. Why giving up control builds trust faster than credentials A frightened audience does not relax because you list your achievements. They relax because they feel you are on their side. Opening with permission to interrupt does something a polished introduction never can: it tells the room that this hour belongs to them, not to a slide deck. That is the opposite of the speaker who guards the clock and refuses questions, and the difference is felt within the first minute. Four lenses, not one When the questions come, I answer them from four directions at once: the technology, the leadership, the culture, and the human cost. A leader who only thinks about the tech will break the culture. A leader who only thinks about leadership will miss what the technology just made possible. The job is to hold all four at the same time, which is also how I build the keynotes themselves. That is what lets a room of 400 strangers feel like one honest conversation. What this means for your event If your audience is carrying real fear about AI, the speaker who opens by inviting their hardest questions will earn their trust faster than the one who opens with a showreel. It is a form of care, said plainly: I am here for you. That decision sits with you, and the room will feel which kind of opening you chose. You can see what that looks like before you decide. Watch how a keynote opens. Questions this article answers How should a keynote speaker open a talk to a nervous audience? By handing the room control rather than listing credentials. In the first sixty seconds you tell the audience they can interrupt at any moment, challenge you, and put their worst fear on the table, and that you are there to answer it rather than perform at them. Trust is built by giving it away first. How do you build trust with an audience in the first sixty seconds? By showing you are on their side, not by listing achievements. A frightened audience relaxes when the opening tells them the hour belongs to them and not to a slide deck. Permission to interrupt does what a polished introduction never can, and the difference is felt within the first minute. What makes an AI keynote feel like a conversation rather than a lecture? Answering the room’s real questions from four directions at once: the technology, the leadership, the culture and the human cost. A leader who only thinks about the tech breaks the culture; one who only thinks about leadership misses what the technology made possible. Holding all four at the same time is what lets a room of 400 strangers feel like one honest conversation.   Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. --- ## Why AI Demos Fail Leaders | Thomas Anglero URL: https://anglero.com/2026/06/16/why-ai-demos-fail/ Published: 2026-06-16 Thomas Anglero working a leadership audience through an AI decision.   Why AI demos fail: the tunnel metaphor and what leaders actually need An AI demo shows functionality, but it does not solve the problem a leader actually has. Every person in the room has a different problem that needs a different answer, and a demo only addresses the single scenario you fed it. What leaders need is not the tool. It is a methodology, a workflow, a new perspective, a way out of their own tunnel vision. I used a demo once, and never again I recently gave a talk to EY and I used a demo. I will not do another one. A demo only proves the tool can do the one thing you set it up to do, and that one thing is rarely the real problem of more than a handful of people in the room. Tech demos belong to the “what is AI” era, and I was giving those back in the IBM Watson days. There are still places a demo earns its keep, AI in healthcare is moving fast enough to genuinely astonish a room, but a demo of AI writing sales emails will get you rotten tomatoes, not respect. The tunnel The longer you have done your job, the deeper your tunnel. Experience builds a single view of how the world works, and you stop seeing that there are hundreds of other tunnels running alongside yours, above and below. My job on stage is to pull a leader out of their tunnel, comfortably if I can and uncomfortably if I must, and show them the others exist. Not a tool. A different way of seeing the problem. Why I take their legacy apart Leaders worry most about their legacy, so that is where I go. I will tell a room that has done everything right that they have failed, and watch the jaws drop. Then I do not leave them there. I show them a playbook, not the playbook, and tell them to adapt it to where they actually are, because what built the company will not carry it forward. Sometimes the bravest thing a leader can do is step aside for the person better suited to what comes next. I take the legacy apart and then hand them the tools to rebuild it. Those are the talks people do not forget. What this means for your event If you are choosing a session on AI, a slick demo will entertain the room and change nothing. What moves leaders is a perspective they cannot get from the tool itself, delivered by someone who has built with it and failed with it. That choice is yours, and it decides whether your audience leaves with a gadget or a way forward. It starts, as ever, with doing the real preparation for your room. Watch what that looks like on a stage. Questions this article answers Why do AI demos fail to help business leaders? Because a demo shows functionality but does not solve the problem a leader actually has. Every person in the room has a different problem needing a different answer, and a demo only addresses the single scenario it was fed. It proves the tool can do the one thing you set it up to do, which is rarely the real problem of more than a handful of people present. What do leaders actually need from an AI keynote instead of a demo? A methodology, a workflow, a new perspective, a way out of their own tunnel vision. The longer a leader has done the job, the deeper the tunnel, and they stop seeing that hundreds of other tunnels run alongside their own. The work on stage is to pull them out and show them a different way of seeing the problem, not a tool. When is a technology demo still worth doing on stage? When the field is moving fast enough to genuinely astonish a room. AI in healthcare still qualifies. But a demo of AI writing sales emails belongs to the “what is AI” era and will earn rotten tomatoes, not respect. Most leadership audiences need perspective, not a proof of functionality.   Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. --- ## Why Event Managers Book the Wrong Speaker | Thomas Anglero URL: https://anglero.com/2026/06/16/why-event-managers-book-the-wrong-speaker/ Published: 2026-06-16 Thomas Anglero holding a full room during a keynote.   Punching the clock: why event managers keep getting burned by the wrong speaker Event managers keep getting burned because they book speakers who treat a keynote like a shift to clock in and out of. The speaker who disappointed them finished exactly on time, with no energy and no passion, and left the room bored. What actually justifies a keynote fee is the opposite: presence, energy, and a connection the audience still remembers years later. The clock-puncher When an event manager comes to me after a bad experience, the story is almost always the same. The previous speaker had no passion. They finished bang on thirty or forty-five minutes, like an old factory worker punching a timecard. They were not there for the audience. They were there to collect the cheque. That is the same speaker who avoids the Q&A, and an audience can smell it from the back row. What a room actually feels when it works The best moment on a stage is when you feel the whole room connected to you at once. You can see every pair of eyes. People forget they are at a conference. They think it is a conversation between you and them, and yet you are holding 800 people in the same grip. They sweat a little, because you challenged them and made them laugh in the same breath, and the hour feels like five minutes. That is the experience an event manager is really buying. The proof is years later I have people stop me in the street and say they saw me speak years ago and have never forgotten it. I usually do not remember them, and they do not mind, because the point is what the talk did for them, not for me. That is the difference between a speaker who filled a slot and one who gave the room a genuine experience. One is forgotten by lunch. The other is repeated for years. What this means for your decision If the last speaker bored your audience, the fix is not a safer choice. It is a speaker with the energy and presence to make the room feel something. That decision is yours, and your audience will remember whether you got it right. You can judge the energy for yourself before you book. Watch a keynote. Questions this article answers Why do event managers keep booking keynote speakers who disappoint? Because they book speakers who treat a keynote like a shift to clock in and out of. The speaker who disappointed them finished exactly on time, with no energy and no passion, and left the room bored. They were there to collect the cheque, not for the audience, and a room can smell that from the back row. What actually justifies a high keynote fee? Presence, energy, and a connection the audience still remembers years later. What an event manager is really buying is the moment the whole room connects at once, where 800 people feel like it is a private conversation, where they are challenged and made to laugh in the same breath, and the hour feels like five minutes. How do you choose a keynote speaker the audience will remember? Do not react to a boring speaker with a safer choice; choose one with the energy and presence to make the room feel something. The proof shows up years later, when people stop the speaker in the street to say they never forgot the talk. One kind of speaker is forgotten by lunch; the other is repeated for years.   Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. --- ## What to Look for on a Speaker Brief | Thomas Anglero URL: https://anglero.com/2026/06/15/what-to-look-for-on-a-speaker-brief/ Published: 2026-06-15 Thomas Anglero on stage with a leadership audience.   Rolled-up sleeves and scars: what to look for on a speaker brief When you are scanning speaker briefs, the thing that separates a practitioner from a performer is not the job titles or the book. It is evidence that the person has actually done something, taken a real risk, and carries the scars of it. A polished CV full of senior titles tells you they once held positions. It does not tell you they will move your audience. A book is no longer a mark of authority Anybody can write a book now. People are having models write entire books for them, and Amazon is being flooded with tens of thousands of them a day. So a book on its own proves nothing. I wrote mine years before any of this, in the days when IBM Watson was an enterprise tool and not something on everyone’s phone. More than half of it was written on my laptop at airport gates, between keynotes, while the AI thoughts from the talk I had just given were still fresh in my head. That is not a claim of authority either. It is just evidence of having been in it. Evidence is the thing to look for. What actually belongs on a brief What has this person produced? What risk did they take, and what came of it? They do not need to be a chief executive. A nineteen year old who tried something reckless and failed, but can tell you what it taught them, has more on a brief than a decorated executive who never put anything on the line. Look for rolled-up sleeves and a few scars. Someone who has lost a job, lost funding, remortgaged the house on an idea, or stayed up through the night to sell something before morning, because that is who your audience is when they are stressed and looking for answers. On my own brief, the work I am proudest of is the handful of moments when I was among the first to try something that might not have worked. Every one of them came from taking a risk, not from holding a title. The smooth performer is forgettable I do not want someone slick on a stage. Smooth is forgettable. The people in your audience are carrying real pressure, and they do not respond to a rehearsed performer. They respond to someone who has been where they are and came out the other side with something to say. When the briefs are spread across your desk Do not be pulled towards the most decorated CV. Look for the person who has actually risked something, because that is the one who can stand in front of a room of stressed leaders and move them. Your audience does not need another smooth talker. They need someone who has been where they are. If you are choosing a speaker and want a fuller guide, here is what to look for when you book. And if you want to see the difference before you shortlist, watch one talk to a room. See what that looks like on a stage. Questions this article answers What should you look for on a keynote speaker’s brief or bio? Evidence that the person has actually done something, taken a real risk, and carries the scars of it, not a list of job titles or a book. Look for what they produced, what risk they took, and what came of it: someone who lost a job, lost funding, remortgaged the house on an idea, or stayed up through the night to sell something before morning. That is who your audience is when they are stressed and looking for answers. Is writing a book a sign of real authority for a speaker? No longer. Anybody can write a book now, with models writing entire books and Amazon being flooded with tens of thousands a day, so a book on its own proves nothing. What matters is evidence of having been in it. A book written from real experience is evidence; a book on its own is not authority. How do you tell a practitioner from a performer on paper? By the scars, not the polish. A nineteen year old who tried something reckless, failed, and can tell you what it taught them has more on a brief than a decorated executive who never put anything on the line. Smooth is forgettable. Do not be pulled towards the most decorated CV; look for the person who has actually risked something, because that is who can move a room of stressed leaders.   Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. --- ## How a Keynote Speaker Should Actually Prepare | Thomas Anglero URL: https://anglero.com/2026/06/15/how-keynote-speakers-prepare/ Published: 2026-06-15 Thomas Anglero working a fresh AI story into a keynote.   The WormGPT story: what real preparation looks like the night before a keynote Real preparation for a keynote does not happen weeks out in a finished slide deck. It happens the night before, when a speaker who actually works with AI checks what has changed in the world in the last day or two and reshapes the talk around it. The deck is only a guide. The content has to be fresh, because an audience can feel the difference between a recording and a person who is paying attention. The night before Finland Before a recent keynote in Finland, I was going through my presentation the night before, doing what I always do, looking at what had happened in AI that week. I came across something called WormGPT. I had not heard of it. Someone had taken a model, forked it, and built it deliberately to do harm, to break through a company’s defences. It had been out for a day or two and already had a couple of hundred thousand downloads, and the number was climbing fast. I took a screenshot, put it on a single slide, and the next morning I opened part of the talk with it. This is real, it is days old, and here is what it means for you. That one slide, found the night before, did more for the room than anything I had prepared weeks earlier. Why one fresh slide beat the polished ones The audience felt they were hearing something current, something I had found myself, not a case study that had been doing the rounds for years. The keynote scored 4.9 out of 5 in the surveys afterwards, and the short clip of that one moment has since become my most watched video on Instagram, well past 120,000 views and still climbing, bringing new followers every day. The numbers are not the point. The point is that the freshness is what they remembered, and freshness only comes from doing the work the night before. Tearing the deck apart A slide deck gets old quickly. When I have time before a talk, I tear mine apart. What has happened since I built this. What did I learn from the last audience I used it on. Which story landed and which one fell flat. I might keep the same slides, but the story changes every time, because the slides are a reminder of the thought, not a script to recite. Same slides, new talk. All of it fresh, every time. What this means when you are choosing a speaker When you book a speaker, you are not really buying a deck. You are buying whether they will walk on stage having done that work for your audience, that week, or whether they will run the same recording they gave somewhere else last month. Your people will know which one they got inside the first few minutes, and so will you. One way to tell before you commit is to watch how a speaker opens a room. See what that looks like on a stage. Questions this article answers How should a keynote speaker prepare the night before a talk? By checking what has changed in AI in the last day or two and reshaping the talk around it. Real preparation does not happen weeks out in a finished deck; it happens the night before. Before a keynote in Finland I found WormGPT the night before, a forked model built to do harm that already had a couple of hundred thousand downloads, put it on a single slide, and opened with it the next morning. Why does fresh, current content matter more than a polished slide deck? Because an audience can feel the difference between a recording and a person who is paying attention. The one fresh WormGPT slide did more for the room than anything prepared weeks earlier; that keynote scored 4.9 out of 5 and the clip passed 120,000 views on Instagram. The freshness is what they remembered, and it only comes from doing the work the night before. How can you tell whether an AI speaker is genuinely up to date? You are buying whether they will walk on stage having done that work for your audience that week, or run the same recording they gave elsewhere last month. Your people will know which one they got inside the first few minutes. One way to tell before you commit is to watch how the speaker opens a room.   Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. --- ## The Q&A Test: How to Spot a Fake AI Speaker | Thomas Anglero URL: https://anglero.com/2026/06/15/how-to-spot-a-fake-ai-speaker/ Published: 2026-06-15   Thomas Anglero opening the floor to questions during a keynote. The Q&A test: why fake AI speakers avoid questions The clearest sign that an AI speaker has never built anything with AI is that they avoid the Q&A. They finish on time, sometimes a minute over, then explain that there is no time for questions. The reason is not the clock. It is that the only thing they can deliver is a slide deck they have recited to every audience before yours, and an unscripted question would show there is nothing underneath it. I have delivered over 450 keynotes and spent a lot of time backstage with other speakers. The pattern repeats, and once you have seen it you cannot unsee it. They are watching the clock, not the audience Watch what these speakers actually do on stage. They have a topic that is hot and a deck that fills thirty minutes, and they have rehearsed the timing to the second. When the material runs thin they drop in two or three videos, not to teach anything but to consume time, and you can see them relax while the clip plays because the clock is doing the work for them. They are not presenting to the audience. They are presenting to the clock. The audience’s real need never comes up, because answering it would mean leaving the script. That is why the apology lands right at the end. There is no time for Q&A because there cannot be. A question they did not prepare for has no slide. A millimetre of depth, a mile of delivery Most of the speakers being paid well today have genuine presentation skills. The slides are beautiful. The jokes are timed. The quotes make people nod. But the content is a millimetre thick and the delivery is a mile wide. I once met a man in Sweden who had just been named the best keynote speaker in the country. He used the same deck for years and never changed a slide. He milked it, he made good money, and audiences loved it. He also had no depth. A polished deck with good jokes is storytelling. It is not what a leader in trouble needs at nine in the morning before they walk into a board meeting. They do not need a cute slide deck. They need a solution, and a performer does not have one, because a performer has no experience to draw it from. What a practitioner does instead A speaker who has actually built with AI does not want to hand over a fixed presentation. They want to know exactly who is in the room: the company, the conference, the situation that week. The night before, they look at what has happened in the industry in the last day or two, and they open by connecting it to the people in front of them. This happened yesterday. Here is what it means for you. Here is what to reflect on, what to act on, and what to avoid. That only works if you can speak to technology, leadership and culture at the same time. A leader who fixes the tooling and breaks the culture has not been helped. The practitioner brings a way through. The performer brings slides. When I take a stage, I tell the room in the first minute to challenge me whenever they like, to throw the hardest question they have, the one that is actually keeping them awake at night. I mean it, and the questions come. That is only possible when there is something real behind the slides. The test any event manager can run You do not need to be an AI expert to find this out. Build Q&A into the format and watch the reaction. The speaker who has done the work wants the questions. They will tell the room to interrupt them, to put them on the spot, to challenge them, because they have answers and they are not protecting anything. The speaker who has only memorised a deck will guard the clock and find a reason there is no time. That single difference tells you whether you have booked an expert or an entertainer. If you want a fuller checklist, the questions worth asking on a vetting call and the warning signs to listen for are set out in the speaker selection guide. There is nothing wrong with an entertainer, if entertainment is what the day calls for. But if your audience is carrying real pressure about AI, and most of them are, they need someone who can stand in the Q&A with no slides behind them and still say something true. That choice is yours to make, and your audience will remember which kind of speaker you put in front of them. If you are weighing one up for a room like that, it is worth watching how a speaker handles the unscripted moment before you decide. See what that looks like on a stage. Questions this article answers How can you tell if an AI keynote speaker has real, hands-on experience with AI? Build Q&A into the format and watch the reaction. The speaker who has done the work wants the questions and will tell the room to interrupt, to put them on the spot, because they have answers and are protecting nothing. A practitioner also opens by connecting something that happened in the industry in the last day or two to the people in front of them, which only works when there is real experience behind the slides. Why do some keynote speakers refuse to take questions from the audience? Because a question they did not prepare for has no slide. The performer has rehearsed a deck to the second, drops in videos to consume time when the material runs thin, and is presenting to the clock rather than the audience. The apology that there is no time for Q&A lands at the end because there cannot be time; an unscripted question would show there is nothing underneath the deck. What should an event manager look for when booking an AI speaker? Someone who can stand in the Q&A with no slides behind them and still say something true. Many well-paid speakers have beautiful slides and timed jokes but content a millimetre thick and delivery a mile wide. A leader under real pressure does not need a cute deck at nine in the morning before a board meeting; they need a solution, and that only comes from someone who can speak to technology, leadership and culture at once.   Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. --- ## What Happens When Your Best People Start Using AI and Your Organisation Does Not Notice URL: https://anglero.com/2026/06/13/when-your-best-people-start-using-ai/ Published: 2026-06-13   Your best employees are already using AI to outperform the rest of your team, and they are not telling anyone. The gap between how your organisation actually works and how leadership believes it works is growing invisibly. When these people leave, and they will, the gap becomes permanent. The question is not about AI adoption. It is about the culture you are running and whether it gives your best people a reason to stay. The two or three people quietly doing twice the work You probably have two or three people on your team who are quietly accomplishing twice as much as everyone else. They are not working harder. They are not working longer. They are using AI, and they are not telling anyone. The immediate reaction is to see this as a win. Productivity is up. Problems are being solved faster than you expected. But the real danger is not their output. It is the gap they are building between how your organisation actually works and how you believe it works. That gap is invisible until they leave. And they will leave, because they know exactly what they are worth. The same story from the other direction Now flip it around. Say you are the one using AI. You sit in the leadership team, and you are the person who can do anything at a moment’s notice. The client in France needs the entire document in French by morning. You do not speak French, but you deliver it. The client in Switzerland wants it in Swiss German, which is not just German. You deliver that too. You build a reputation as the person who never says no. But you are not telling anyone how you do it. You are enjoying the praise, the recognition, the quiet pride of being the one who always delivers. And at some point, you stop sharing and start planning. You know your value. You know the market. You start looking. This is the same story from two directions, and the root cause is the same in both. Something about the culture is not working. If your best people are hiding what they do, or if the rest of your team is not following, the question is not about AI. The question is about you. Why your people are not following your lead Why are they not following your lead? Why has your communication not landed? You wanted everyone to use AI. You said it clearly. But saying it and creating the conditions for it are two different things. If your team does not feel safe admitting they do not understand the tools, they will not use them. If your leadership peers feel threatened by what you can do, they will not ask how you do it. The answer to both situations is the same. You need a plan, not for AI, but for yourself. What kind of leader do you want to be in this environment? What kind of culture do you want to run? Because the people who have figured this out are not waiting for you to catch up. They are already thinking about where to go next. The question is whether they take your organisation with them or leave it behind. Frequently asked questions How do I know if my best people are secretly using AI? You probably have two or three people quietly accomplishing twice as much as everyone else, not by working harder or longer but by using AI and not telling anyone. The sign is output that outpaces the rest of the team without any obvious change in hours or effort. Why is it a problem if my best people use AI and outperform? Because the danger is not their output but the invisible gap they build between how the organisation actually works and how leadership believes it works. That gap stays hidden until they leave, and they will leave, because they know exactly what they are worth. When they go, the gap becomes permanent. Why are the rest of my people not following my lead on AI? Because saying you want everyone to use AI and creating the conditions for it are two different things. If your team does not feel safe admitting they do not understand the tools, they will not use them. If your leadership peers feel threatened by what you can do, they will not ask how you do it. The barrier is culture, not the technology. Is this really an AI problem? No. Whether your best people are hiding what they do or the rest of the team is not following, the root cause is the same: something about the culture is not working. The question is not about AI adoption. It is about the kind of leader you want to be and the culture you want to run.   Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. --- ## Your Traditional Partners Are Failing You in the Age of AI URL: https://anglero.com/2026/06/02/your-traditional-partner-are-failing-you-in-the-age-of-ai/ Published: 2026-06-02     Traditional consulting partners are failing executives in the age of AI because their business model sells time and hours, while AI now delivers the same analysis in minutes. The second obstacle is the leader’s own working habits: AI has to be woven into every decision, not opened as an occasional tool. Both have to change before results arrive.   The partner running plays from a game that no longer exists You are sitting in a meeting, listening to the same partner you have worked with for years present their quarterly update. The slides are sharp. The language is confident. The recommendations sound reasonable. And somewhere in your chest, there is a tightness you cannot name, because the results are not coming. They will tell you they need more time. More hours. Another engagement. They will reframe the delay as complexity. But you already know what is happening. Your traditional partners, the ones who built the infrastructure that got you here, are running plays from a game that no longer exists. This is not a criticism. These are capable people who built real things. But they built them in a world where information moved at human speed, where competitive advantage came from scale, and where the consulting model was built around selling expertise by the hour. That world is gone. An AI can now read every book in a library in seconds. It can analyse your entire pipeline overnight. And your partner is still scheduling a workshop for next month. The second obstacle is you The uncomfortable part is what comes next. Because the second thing holding you back is not them. It is you. Not because you are inadequate, you would not be in that chair if you were. But because you are not yet using AI the way it needs to be used. Not as a tool you open when you remember, but as something woven into every decision, every analysis, every conversation. Something that pushes you the answers you need before you know you need them. That is what it means to be AI-native. And almost no one is there yet, because almost no one has had the space. You are moving from meeting to meeting, call to call, and by the time you sit down at your desk, you have not even opened your inbox. This is why AI is a culture project before it is a technology project. The coach whose sport has matured Think of a coach who has run the same drills and the same plays for an entire career, and they worked. The team won. But the sport has matured. The athletes are faster, more talented. The competition is different. The old playbook does not apply, and running it harder will not close the gap. You are that coach. And the field has changed around you. There is nothing wrong with your instincts. What needs to change is who you trust with this next chapter, and how deeply you trust yourself to learn a new way of leading. Your traditional partners want to sell you time and hours. The right partner asks what you need. Frequently asked questions Why are traditional consulting partners failing in the age of AI? Because their business model sells time and hours, while AI now delivers the same analysis in minutes. They built their expertise in a world where information moved at human speed and advantage came from scale. An AI can read every book in a library in seconds and analyse an entire pipeline overnight, while the partner is still scheduling a workshop for next month. Is the problem only with the partners? No. The second obstacle is the leader’s own working habits. AI has to be woven into every decision, every analysis and every conversation, not opened as an occasional tool. Almost no leader is AI-native yet, because almost no one has had the space, moving from meeting to meeting without even opening the inbox. What does it mean to be AI-native as a leader? It means AI is not a tool you open when you remember, but something woven into every decision, pushing you the answers you need before you know you need them. It is the difference between using AI occasionally and leading with it continuously. What actually needs to change to get results? Both obstacles at once: who you trust with this next chapter, and how deeply you trust yourself to learn a new way of leading. Traditional partners want to sell you time and hours; the right partner asks what you need. Nothing is wrong with your instincts, but the field has changed around you and running the old playbook harder will not close the gap. This edition is adapted from the Clarity at the Top podcast. Watch the full episode on YouTube.   For more on this, see Strategic AI Advisor vs Big Four Consulting, part of the Strategic AI Advisor guide. Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. --- ## 75% of CEOs believe a fellow CEO will be fired over a failed AI implementation URL: https://anglero.com/2026/05/26/ceo-will-be-fired-over-a-failed-ai/ Published: 2026-05-26 Thomas Anglero explaining to 850 leaders how to take risks and benefit from AI   75% of CEOs believe a fellow CEO will be fired over a failed AI implementation. The pressure is real, but the pressure is not the problem. The problem is that most leadership teams were built for a world that no longer exists. 62% say their boards are actively demanding measurable AI outcomes. 56% admit their competitors have a stronger AI strategy than they do.   You have read these numbers before. But have you sat with what they actually mean for the people around you?   Here is what I see when I sit across from leaders right now. The pressure is real, but the pressure is not the problem. The problem is that most leadership teams were built for a world that no longer exists. Your leaders are talented. They brought the company to where it is today. But 2026 is not 2024. The expectations of what can be accomplished in an hour have changed beyond anything we have seen before. I explore this pressure in my keynote, Leading an Organisation on Empty Every employee will soon work alongside twenty AI agents Consider this. Every employee in your organisation will soon work alongside AI agents. Not one assistant, but twenty or thirty. Every person on your team, multiplied by twenty. That changes everything. The volume of ideas, the speed of research, the depth of competitor analysis, the pace of decision-making. What used to take two weeks now takes minutes. Now ask yourself: is each member of your leadership team ready for that? Not just ready to cope with it, but ready to leverage it? Do they have a strategic plan for what happens when every person reporting to them is suddenly capable of twenty times the output? Are they coming to you more often with new ideas, new models, new possibilities? Or are they still operating at the pace of last year? If the answer is honest, it is uncomfortable. And that is precisely the point. Before you look at your team, look at yourself Before you look at your leadership team, look at yourself first. Are you the living example of what you expect from each of them? If not, that is where it starts. Not with a restructure, not with a consulting engagement that takes three months to produce a slide deck. It starts with one honest conversation. The kind of conversation where someone you trust looks you in the eye and tells you what your organisation cannot. It comes down to how culture changes from the top.   CEOs will be fired over failed AI Implementations   Every leader needs that voice. Not a vendor, not a framework. A person who has been in the room, who understands the pressure, and who will tell you the truth in three seconds rather than three months. The uncertainty does not disappear. But it becomes manageable the moment you have someone to think alongside. And once you are there, your leadership team will see it, feel it, and follow it. That is how culture changes. Not from the bottom up, but from you. Frequently asked questions Do CEOs really expect to be fired over failed AI implementations? Yes. 75% of CEOs believe a fellow CEO will be fired over a failed AI implementation, 62% say their boards are actively demanding measurable AI outcomes, and 56% admit their competitors have a stronger AI strategy than they do. The pressure is real, but the pressure is not the actual problem. If the pressure is not the problem, what is? That most leadership teams were built for a world that no longer exists. The leaders are talented and brought the company to where it is, but 2026 is not 2024, and the expectations of what can be accomplished in an hour have changed beyond anything seen before. The team, not the pressure, is what is out of date. What does it mean that every employee will work alongside twenty AI agents? It means each person’s output can multiply many times over: the volume of ideas, the speed of research, the depth of competitor analysis and the pace of decisions all change, and what used to take two weeks now takes minutes. The question is whether each leader is ready not just to cope with that but to leverage it. Where does a CEO actually start? With themselves, not a restructure or a three-month consulting engagement. Start by asking whether you are the living example of what you expect from your team, then have one honest conversation with someone who will tell you the truth in three seconds rather than three months. Culture changes from the top, not the bottom up. If you are leading your organisation through this, I work with a small number of senior leaders each quarter through a 90-day strategic advisory engagement. We start with where you are, build a board-ready AI strategy, and align your leadership team around it.   Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. --- ## The Most Expensive Decision a Leader Makes. It Is Not the Wrong One | Clarity at the Top URL: https://anglero.com/2026/05/16/most-expensive-decision-a-leader-makes/ Published: 2026-05-16   The most expensive decision a leader makes is not the wrong one. It is the refusal to be first.   You have been in that room. Not the one with the projector and the coffee that has gone cold. The quieter room. The one inside yourself, where you already know that what you are hearing is important, and where the resistance begins before the person presenting has even finished. Someone stood in front of you, your team, a consultant, a younger colleague with too much conviction, and presented something you had never heard before. No case study behind it. No precedent. No comfortable benchmark to point to and say, this is why it will work. And in that moment, something tightened. Not because the idea was bad. Because it was unfamiliar. Because saying yes would mean stepping onto ground that no one in your industry, your organisation, your career had stood on before. The day I was told “we cannot be first” Several years ago, I was the head of innovation for the Norwegian Tax Authority. My team and I had spent six months working with the University of Berkeley to build a leadership programme for the public sector, the first of its kind in the world. Not a weekend seminar. A foundational programme that would take senior leaders through six weeks at Berkeley, then rotate them across every branch of the Norwegian public sector for a full year. The cultures would merge. The silos would break. The entire public sector would lead as one. Berkeley was ready. My team was ready. I presented to the leadership group, fourteen of the most senior people in Norwegian public administration. When I finished, the managing director looked at me and said: We cannot be first. Not this is the wrong idea. Not the budget does not work. Not the timing is off. We cannot be first. Why the refusal to be first is the costliest decision of all That phrase has stayed with me throughout my career. Not because it was cruel, it was honest. And not because it was wrong, by its own logic, it was perfectly rational. But because it revealed something about leadership that most people never say out loud: the most expensive decision a leader makes is not the wrong decision. It is the refusal to go first. The wrong decision can be corrected. A failed project teaches. A budget overrun can be absorbed. But the refusal to be first, that costs in silence. The people who built the idea walk away quietly. The opportunity moves to another country, another company, another leader who says yes. And the organisation never knows what it lost, because it never existed. Every leader is facing this moment now, about AI Every leader I work with today is facing a version of this moment. Not about Berkeley. About AI. About agents. About what their organisation becomes when every employee manages autonomous workers that did not exist two years ago. There is no case study for what is coming. There is no precedent. The question is whether you are the leader who says we cannot be first, or the one who says yes, and I will lead us there. The cost of the wrong answer is silence. And silence is the most expensive sound in leadership. Frequently asked questions What is the most expensive decision a leader makes? Not the wrong decision, but the refusal to go first. A wrong decision can be corrected, a failed project teaches, a budget overrun can be absorbed. The refusal to be first costs in silence: the people who built the idea walk away, the opportunity moves to another leader who says yes, and the organisation never knows what it lost because it never existed. Why do leaders resist genuinely new ideas? Not because the idea is bad, but because it is unfamiliar. When something is presented with no case study, no precedent and no comfortable benchmark, saying yes means stepping onto ground no one in your industry or career has stood on before, and the resistance tightens before the presenter has even finished. What did “we cannot be first” actually reveal? That the objection was not about the idea being wrong, the budget failing or the timing being off. It was a refusal of the risk of being first, which by its own logic was perfectly rational, yet it revealed the one cost leaders rarely name out loud: the price of never trying is paid in silence. How does this apply to AI today? Every leader now faces a version of the same moment, about AI and agents, about what an organisation becomes when every employee manages autonomous workers that did not exist two years ago. There is no case study and no precedent, so the question is whether you say “we cannot be first” or “yes, and I will lead us there.” Watch the full 20-minute video podcast Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. --- ## Why Your AI Adoption Isn’t Driving Business Value (And How to Fix It), Clarity at the Top podcast – Episode 6 URL: https://anglero.com/2026/05/12/ai-adoption-isnt-driving-business-value/ Published: 2026-05-12 AI adoption is rising across every industry, but return on investment stays flat. The bottleneck is not the technology. It is leadership: most executives treat AI as a tool to delegate to, rather than a capability to build around. Three changes turn AI use into measurable business value, and all three start with the leader, not the IT department.   The hard truth about AI adoption and business value AI adoption is increasing across industries, yet the business value reflected on monthly spreadsheets remains stagnant. The hard truth for organisations facing this disconnect is that the bottleneck sits at the leadership level. Extracting real ROI from artificial intelligence requires executives to transition from passive observers to active AI leaders. You cannot expect a workforce to revolutionise their output if the leadership team still views generative AI as a simple text editor rather than a powerful strategic engine. Stop typing, start talking: AI as your C-level advisor To generate massive business value, leaders must fundamentally change how they interact with AI platforms like ChatGPT, Gemini, or Claude. You must treat your AI as a top-tier, $10,000-a-day strategic consultant available to you 24/7. Stop typing simple queries and start using voice to feed it complex, real-world business challenges. Whether analysing declining client trust or modelling market expansions, your AI can synthesise global data sets, process historical trends, and deliver predictive strategic options in 25 minutes, a task that would traditionally take a team of expensive consultants three months. Flex your AI leadership to set the standard Driving organisational adoption requires demonstrating the technology’s power in real-time. Leaders must “flex” their AI usage openly to set a new baseline for operational speed and efficiency. If you can command an AI to instantly cross-reference calendars and book 1-on-1 meetings during a live team call, you immediately raise the performance bar for everyone in the room. Combine this visible leadership with an internal AI champion who has the freedom to troubleshoot and train employees, and your company will rapidly transition from mere AI adoption to generating compounding business value. This is why AI is a culture project before it is a technology project. Frequently asked questions Why is my company’s AI implementation not generating ROI? The gap between AI adoption and business value stems from a lack of top-down AI leadership. Executives must stop treating AI as a typing tool for grammar and start utilising it as a 24/7 C-level strategic advisor to extract actionable, high-level business intelligence. How do leaders drive AI adoption in their teams? Leaders must actively “flex” their AI usage in front of their teams. By demonstrating real-time AI capabilities, such as executing complex scheduling or data analysis live during a meeting, executives set a new operational baseline that forces the organisation to elevate its standards. How should a CEO use generative AI? A CEO should use AI through voice interaction as a strategic sounding board. Instead of basic prompts, executives should feed the AI complex business scenarios, client data, and market variables to generate predictive charts, strategic options, and deep-dive analyses in minutes rather than months. Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. --- ## Your AI Project, It’s Actually a Culture Project! Unlocking Success (Clarity at the Top podcast – Episode 3) URL: https://anglero.com/2026/04/22/ai-project-is-a-culture-project/ Published: 2026-04-22 Thomas Anglero explains why every AI project is a culture project first.   Implementing an AI project is not a technology project. It is a cultural transformation, and treating it like a software rollout is why most enterprise AI strategies fail. The leaders who succeed do not lead with technical specifications. They lead with stories, soft skills, and conversation, building the comfort and trust that make AI adoption real, not theoretical. If you would rather read than listen, here is the article version of this argument.   Questions this episode answers: How do you implement AI in a company? AI implementation must begin as a culture project, not a tech deployment. Success relies on building a foundation of employee comfort and trust before focusing on technical specifications. What makes a successful Head of AI? The most effective Head of AI prioritises human-centric soft skills over technical coding abilities, focusing on guiding employees through the psychological shift of working alongside artificial intelligence. How do leaders drive AI adoption? Executives must lead by example, sharing personal stories of how AI saved them time or improved their workflows, demonstrating that AI is a collaborative partner rather than a replacement tool.   Key Executive Takeaways: The “IT Project” Fallacy: Treating an AI rollout like a migration to a new operating system will result in project failure and lost ROI. AI fundamentally alters *how* and *why* work is done. The 50X ROI Trigger: Employees transition from “uncomfortable” to “AI experts” the moment AI solves a personal, time-consuming pain point (e.g., reducing a multi-hour asset sorting task down to 6 minutes). Conversational Interface as the New Standard: The fastest path to organisational AI fluency is treating the platform as a 24/7 strategic partner, interacting via natural conversation rather than complex prompting.   Episode 3 · Duration: 17:55 · Listen on Spotify, Apple Podcasts, YouTube Podcast Transcript (Lightly edited for readability. Speaker is Thomas Anglero throughout) I was meeting with a bunch of leaders the other day and they were really proud that they had selected a person to lead their AI initiative inside of their company. They were very proud to have allocated funds. They are very proud to have scheduled on their plan as to when they’re gonna start, how many people are gonna be implemented, the different phases. And I thought to myself, “Is this how you implement AI?” I mean, that’s how you implement any project, what we’ve been doing for decades, how you implement a project. But that’s sort of like you implement a project using Microsoft Word or Excel, or using a new tool, or we’re all gonna shift from Windows to Apple which is Linux. You know, it’s like a technology project. But an AI project? Turning on, activating, beginning an AI project, that’s not a technology project. That starts with one fundamental thing: culture. An AI project is a culture project, and that’s where it starts. That’s the foundation stone for the AI project. If you jump right into the, like building a house, don’t worry about the foundation. Skip the foundation, just build the house. What happens? You don’t get the outcome you want. The house will fall down. Somebody’s gonna get hurt. Translate that into business terms: you don’t get the outcome you want, the project will fail, somebody will lose their job. What’s the difference? The foundation. When you start an AI project, I am so happy for you that you have somebody to lead your AI initiative, maybe even had hired somebody who is a head of AI. The groundwork for all of this is cultural readiness before the AI budget.   Hiring a Head of AI: Technical vs. Cultural Skills That person that you hired, head of AI, is that person insanely good in soft skills? Is that person a non-techie? Is being head of AI meaning you have to be the nerdiest person in the company, or does it must be the most non-nerdy person in the company? AI is, as we approach AGI right, that version of AI we’ll all want right, the all-knowing AI, what we’re really looking at is something that knows us, knows how we think, can predict what we’re going to think before we think. We start to describe a person. We’re starting to describe a character. Doesn’t that sound like you’re describing a new employee in your company? Right. You’re about to hire a head of a department, a head of a new person, a head of AI. Let’s stick to that example: a new head of AI. Everybody’s all excited and asking, “Who is this person?” They say, “What does she do, what sports is she into, and is she married?” Does she have kids, how many degrees does she have? What school did she go to, where does she work? AI is getting damn close to a person, and we need to treat it as such. Hence, that head of AI, that person and their character and their personality, needs to be able to say that AI, because it’s all-knowing and has this fullness, is that I’m going to implement a programme to teach everyone AI. For everybody to come up to speed with AI, and that is we’re all gonna get in touch with communicating with ourselves because it’s a cultural project. That’s where you start. AI is not just a tech tool.   Getting Comfortable with AI Through Conversation The best way to use AI to be comfortable with AI, then become an expert in AI and by the way, get comfortable with AI and become an expert at AI, they’re so close to each other they’re milliseconds apart. They’re an epiphany. They’re a thought away from going, “I’m sorta comfortable using it,” to like, “Holy God, I love this thing, I know how to use it.” And you do that by just talking to it. I was working on a project to build my website right, my new website, anglero.com. A-N-G-L-E-R-O.com, if you wanna check it out, and I don’t know how to build a website. I mean, I’ve built websites before. They’re all ugly as hell. I don’t… don’t ask me to build a website for you the old school way. I could build one with AI. And you know, I didn’t wanna vibe code my website; I wanted to build it with AI, not just have AI build it for me. And I asked Claude, Anthropic Claude. I asked, I don’t, I technically know how to do HTML and all that, but in terms of the design, the layout, the colours, the fonts, that’s not me. I have no style in me. Don’t ask me to get dressed in the morning, right. Style is not my thing. And I told AI that. I told Claude that. I actually had a conversation and I told it that in words, and I use a text, I use a speech-to-text program, so I like to speak obviously. And it came back and it told me, “Fine. You’re not comfortable with style and all that. That’s not your thing.” That’s literally what it said, and it told me back, “What do you like? What appeals to you in life?” And things like that, and I said, “Oh okay, no problem there.”   Designing with AI: A Personal Story I said, “I love Scandinavian furniture design. I think the smoothness of the wood, the shapes, the curves. Nothing’s aggressive, nothing’s heavy. It’s about extenuating what nature is. The rawness, the colours, the softness.” I mean, you touch these furniture and it’s just… you don’t wanna ever leave. The wood, the leather, I love it. Claude took what I said and then said, “Got it.” And it returned back to me a colour palette for my website that, if you go to my website, there it is. It built my website based on my colours that what I love and appreciate out of furniture design. That’s an epiphany moment. I built a website based on what I love and appreciate in life. You can’t do that in regular HTML. When you ask a coder, “Build me a website, you have 5 days, you have 5 hours,” there was no conversation about what I love, what touches me, what I appreciate. But with AI, the foundation of our conversation was about what I love, what I appreciate, what touches my soul. If I was your new head of AI that you were hiring, the first thing I would start, the first thing I would do with this cultural project and telling everybody, “Listen, we need to change the culture.” Maybe I wouldn’t tell everybody we’re doing a cultural project ’cause that sounds funny. ‘Cause some people say, “Whoa whoa, we’re changing the culture of the company.” It doesn’t resonate with people; it’s got too much history in the wrong way.   Rolling Out AI to Your Entire Company But I would say, “We’re gonna have a conversation,” ’cause what I’m gonna do as your new head of AI is I’m gonna change the culture of this entire company. I’m gonna change the culture of every individual here. I’m gonna elevate them. I’m gonna make them more comfortable. I’m gonna make them feel that they have a new person they could talk to, a new outlet, and the end result is I’m going to 5X or 50X their outcome over time. And we’re gonna do that by asking them to have a conversation with AI. And with me, it was, “I’m gonna build a website,” and we ended up talking about what I love in life, what touches me, what I appreciate. Those intangible tacit things that you can’t explain when you feel something. When you pet a cat or a dog, you just feel the fur. And then it built the website around that, or at least the colour template and all that. And as your head of AI, I would have every employee, I would ask them to build a website, but I would say, “What project are you gonna work on or you are working on? What’s your deadline, what’s your challenges? Now don’t answer those questions to me or just tell this to the AI.” Or maybe I have the conversation with them and we have the AI there, and the AI is listening to us. And the whole time the AI’s understanding and learning and listening, and then comes back to the person, “Well this is how we’re gonna proceed.” So you just gotta get the person used to the fact that it’s just a conversation. It’s just a conversation. But it’s a conversation with someone who’s solely out to help you. They don’t have any hidden agenda. They’re not gonna take credit for the work when the whole project is over. God, we’re sick of those people, aren’t we? They’re gonna be there for you 24 hours a day, 7 days a week. They’re gonna work tirelessly. When your employees understand that that AI is there for them on all those levels I just described and more, there is no more hindrance.   How AI Changes How, Why and When We Work There’s no more doubt and negativity towards AI. There’s just this huge embracement of AI. And as soon as that person understands that, then you have that little moment of going from “I’m comfortable” to “I’m an expert.” And then you have the next moment, which is that person’s changed. That person now not just understands, but they’re not gonna be the same person anymore. This is the subject of my keynote, When Every Employee Becomes a Leader, Because of AI. And that means you just changed the culture of that person, which means you just changed the culture of your office, which means you just changed the culture of your company. AI is a cultural change of how you work, why you work, when you work, and who you work with. For my website, I think a normal human, if I’d have hired a web developer, I don’t know, that would’ve been a huge budget. But I know one thing I had to do one part that I did before when building a website, and I know it would’ve taken me… Yeah, I had to find photos and I had to select a couple of photos out of my photo library, and I know that took me many hours before. It did it in 6 minutes. That’s the outcome you’re gonna get. That’s the benefit. And when an employee who has a simple task that they know it’s gonna take them half a day of work and the AI does it in 6 minutes, that’s that moment of “I’m comfortable with AI” to “I’m an expert in AI.” Because it did something for me. It made me feel better. It helped me with my work. It saved me time. Not just time, but annoyance. To go through hundreds and hundreds of photos to pick out just a handful of photos for a website… I don’t know if you’ve done that, but after photo number 50 they all start to look the same. One visible symptom is the culture rift AI creates between employees.   Lead by Example: What’s Your AI Story? This is a massive shift in your company. So when you say that you want everybody to use AI and you want everybody to embrace AI, how about you start by telling your story of how you started with AI and how it changed you? I just shared my story about what I’ve been working on recently with my new website, and I think it’s a perfect example. What’s your story? Have you used AI enough that you have a story? You should have a few stories. That’s just my latest story of what it did that just totally blew my mind, absolutely blew my mind. I mean, this should’ve been hundreds and hundreds of hours of man-hours of work. And I know when you look at them, you go to my website and you see the final result and you go, “Beautiful,” but is that it? Yeah, because sometimes the most beautiful things start so huge, and you come down because you have people who are asking the right question, and AI and machines, and you’re refining, refining, refining to something just so beautiful. So simple, Scandinavian design, so simple but so beautiful. But if you did it the old way, it could’ve taken you forever. When you do it with AI, it saves you tons of time. You’re not tired. You’re not aggravated, and actually, it was a lot of fun. A lot of fun. I actually even told my AI, “Thank you,” so many times. Be polite! What’s your story? You as a leader, what’s your story that you’re gonna share with your people that’s gonna make them more comfortable with them using AI? If you don’t have a story, never too late. Start talking to your AI. Tell it, “I don’t even know how to start. How do I interact with you? How do we begin?” Or better yet, “I have a pile of paperwork here, I have to look at spreadsheets, I have to look at budgets, I have to look at pipeline. How do I do this?” “How do we work together? This pile of paperwork and you, how do you help me to do this better?” That is the best place to start if you want to start. And it’ll tell you, “This is how I’m gonna help you. This is how I’m gonna get you through all these tasks that you have.” “What is your task? Can you upload all those PDFs? Can you upload all that documentation? I am here for you.” God, you know what it is, I think some people have just forgotten because it’s been so long the last time they heard somebody say, “I’m here for you. I’m not gonna go home.” “I’m here, I’m gonna stay in my cubicle. I’m gonna sit right beside you all night long. We’re gonna get this thing done.” It’s been a long time. Welcome to AI. That’s your new colleague. It’s there for you. It can’t pour the coffee for you yet; that’s the humanoid robots, they’re coming in 2027. But AI, it’s here for you now. Share your story with your people, and that’s the best way to be a great leader in these early days of AI. And I emphasise early. It’s getting better every single day, it really is. But you as a leader, you have to be different. You have to realise that who you were, how you’ve built yourself up to who you are today as a leader, that’s okay and that’s great actually. Congratulations. Leaders who want help with this can read about working with Thomas as a Strategic AI Advisor. But now with AI, you’re gonna be a little different. Things are gonna change, and your soft skills are gonna be more important than ever because AI can do a lot of the crunching. But the human beings in your office, they’re gonna need a lot of understanding and communication. Tell your story to your people, and that will change the culture of your company. I’m Thomas Anglero, and this is the Clarity at the Top podcast.   Frequently asked questions Why is an AI project really a culture project? Because activating AI is not like rolling out a new piece of software. It changes how, why, when and with whom people work, so it has to start with culture, the foundation. Treating it like a technology migration, with budget and phases but no cultural groundwork, is like building a house with no foundation: the project fails and people lose trust. What makes a good Head of AI? Soft skills more than technical brilliance. As AI comes to resemble a colleague that knows how you think, the person leading it needs to guide people through the human shift of working alongside it. The best Head of AI is closer to the most people-centred person in the company than the most technical. How do you make employees comfortable with AI? Through conversation, not training modules. The move from “comfortable” to “expert” happens in a single moment, when AI takes a task someone dreaded and does it in minutes, saving time and aggravation. Getting an employee to simply talk to the AI about a real piece of work is what triggers that shift. How should a leader drive AI adoption? Lead by example and tell your own story. Share a specific, personal example of how AI changed your work, the way rebuilding a website around what you love turned into a design in minutes. A real story makes people comfortable far faster than an instruction to “use AI” ever will.   Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. --- ## AI is Not a Technology Project. It is a Culture Project. Here is Where it Actually Starts URL: https://anglero.com/2026/04/19/ai-is-a-culture-project/ Published: 2026-04-19   AI is not a technology project. It is a culture project, and culture is the foundation most organisations skip. An AI rollout planned like a software migration, with budget, phases, and a Gantt chart, fails the way a house without a foundation fails: the cause is invisible until the collapse.   The other day I was in a meeting with a group of senior leaders. They were proud. They had selected someone to lead their AI initiative. They had allocated budget. They had a phased rollout plan. Milestones on a Gantt chart. I sat there and thought: this is exactly how you implement Microsoft Word. And that is the problem.   The foundation you are skipping   Building a house without a foundation does not fail at the end. It fails at the beginning. You just do not find out until later, when the walls crack, when something collapses, when someone gets hurt. Translate that to business: the project stalls. The adoption numbers disappoint. People revert to their old tools. And the head of AI who was hired with such fanfare is now explaining to the board why the ROI is not where it was supposed to be. The missing piece is not the technology. It was never the technology. It is culture. That is the foundation. Start there or do not be surprised when the house falls.   The wrong person in the wrong seat   Here is a question no one asks when they are hiring a head of AI: Is this person exceptional at soft skills? Because the further AI develops, and it is developing faster than most boardrooms are comfortable acknowledging, the closer it gets to something that resembles a person. Something that knows how you think. Something that anticipates your next question before you ask it. Something that learns your preferences, your communication style, your working patterns. You are not onboarding a software system. You are, in a sense, onboarding a colleague. And yet the instinct is always to hire the most technical person in the room to lead it. The one who knows the architecture, the infrastructure, the model parameters. That person may be exactly wrong for the job. What you need at the front of this cultural shift is someone who can walk into a room of sceptical, overwhelmed, pressured human beings and make them feel that AI is not a threat to them. That it is, in fact, the first colleague they have had in years who is genuinely and entirely on their side.   How culture actually changes   I will give you an example from my own work. I recently rebuilt my website. I know how to write HTML. I do not have any particular sense of style, if you have seen my wardrobe, you already know this. So I told Claude, the AI I was working with, exactly that. I said: style is not my thing. Help me. It did not send me a colour palette. It asked me what I love. What moves me. What I find beautiful in the world. I told it about Scandinavian furniture. The smoothness of the wood. The curves. Nothing aggressive, nothing heavy. The way those pieces feel when you touch them, like the designer understood that objects should give something to the person holding them, not demand something from them. The AI took that and built the visual language of my website from it. That is an epiphany moment. Not because the technology was impressive. Because the conversation felt like it was with someone who was actually listening. That is what your employees need to experience. Not a training module. Not a productivity target. A conversation where the AI helps them with something real, something that matters to them, and does it in a way that makes them feel better at the end than they did at the start. One of the tasks I had been dreading most in the website project was going through hundreds of photographs. I knew it was going to take me most of a day. It took six minutes. The moment your employees each have their version of that six-minute experience, the moment AI takes something they have been dreading and handles it in six minutes, that is when the shift happens. Not from the training programme. From that moment.     What this means for you as a leader   You cannot ask your people to trust something you have not trusted yourself.   If you are going to stand in front of your organisation and tell them that AI is going to change how they work, you need to have a story. A real one. Something that happened to you. Something specific and personal, where the AI did something that you did not expect, that saved you time or aggravation or both.   If you do not have that story yet, you are not ready to lead the cultural shift. And the cultural shift is the only one that matters.   The good news is that it is not complicated to get there. Open the AI. Tell it you do not know where to start. Tell it what you are working on. Tell it what is sitting on your desk that you have been avoiding. Ask it: How do we do this together?   That is the whole brief. Everything else follows from there.     One thing to remember   AI does not take credit for the work when the project ends. It does not have a hidden agenda. It will not go home at five because it has a dinner reservation.   When your people genuinely understand what that means, that they have something working alongside them, tirelessly, without ego, without politics, there is no more resistance. There is just adoption.   And when adoption happens person by person, something larger shifts. The individual changes. The team changes. The organisation changes.   That is how culture actually moves. Not from a strategy document. From one real moment of understanding, multiplied across every person in your company.   AI is a culture project. Start with the culture.   Frequently asked questions Why is AI a culture project and not a technology project? Because culture is the foundation, and an AI rollout planned like a software migration with budget, phases and a Gantt chart fails the way a house without a foundation fails. The cause stays invisible until the walls crack: the project stalls, adoption disappoints, and people revert to their old tools. The missing piece was never the technology. It is culture, so you start there. Who should lead an AI initiative? Not necessarily the most technical person in the room. Because AI increasingly resembles a colleague that learns how you think, the person at the front of the shift needs exceptional soft skills: someone who can walk into a room of sceptical, overwhelmed people and make them feel AI is on their side, rather than someone who only knows the architecture and model parameters. How does culture actually change around AI? Person by person, through one real moment of understanding rather than a training programme or a strategy document. When AI takes a task an employee has been dreading and handles it in minutes, that is the moment the shift happens. Multiply that single moment across every person in the company, and the individual changes, the team changes, and the organisation changes. What does a leader need before leading the cultural shift? Their own story. You cannot ask people to trust something you have not trusted yourself. If you cannot point to something specific and personal where AI did something you did not expect and saved you time or aggravation, you are not ready to lead the shift. Getting there is simple: open the AI, tell it what you have been avoiding, and ask how to do it together.   Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. --- ## Your Calendar is Full. Your Culture is Splitting. Here is What to Do About It. URL: https://anglero.com/2026/04/18/your-culture-is-splitting-what-to-do/ Published: 2026-04-18 Thomas Anglero explaining how to thrive when a culture is splitting   A cultural rift is forming inside most organisations: employees who have figured out AI are accelerating, while the rest feel left behind. Left unaddressed, the gap will break the team you have spent years building. The first move is to name the rift openly at an all-hands meeting, because naming it gives people permission to be uncomfortable, and that is the first step to becoming comfortable.     It is Monday morning. Or Tuesday. It does not matter which day, your calendar looks the same. Back-to-back meetings from eight in the morning. You finish the last one at five, open your inbox, and the real work begins. Somewhere between the third meeting and the fourteenth email, there is this quiet voice in the back of your mind. Why is everyone else going home early? You already know the answer. And it is making you uncomfortable.   The rift no one is talking about   Inside your organisation right now, a split is forming. On one side: the people who have figured out AI. They are moving faster, producing more, and some of them, let us be honest, are becoming a little arrogant about it. On the other side: the people who have not figured it out yet. Not because they are incapable. Because they are exactly like you and me before we understood what was happening. Overwhelmed. Uncertain. Quietly wondering if they are being left behind. What you are watching is the culture of high school reproducing itself inside your professional organisation. The cool kids and the ones on the outside. Nobody wants to go through that again and yet here it is, inside the company you are responsible for. This rift, left unaddressed, will not stay manageable. It will grow. The AI-enabled employees will keep accelerating. The others will keep feeling isolated. And the gap between them will eventually break the team you have spent years building.   What your next hire already knows   Here is something that changes how you think about this. The next person you hire for any role, product manager, marketing lead, operations is arriving with something your current team did not have when they joined. They know how to deploy AI as a team within their role. Not a tool. A team. Customer support, marketing, legal review, financial modelling, all of it, running in parallel, managed by one person who knows how to direct it. That new hire is not one employee. In practice, they are twenty. I do not say this to create panic. I say it because understanding this changes what you do next. Your existing employees who are not yet comfortable with AI are not your liability. They are your most valuable asset, the moment they cross over. Every person who makes that transition multiplies their output dramatically. Laying off people in the name of AI efficiency is not a smart move. It is the most expensive mistake a leader can make right now. This is the subject of my keynote, When Every Employee Becomes a Leader, Because of AI.   What you say when you call the all-hands meeting   The first move is the one most leaders avoid. You call the meeting and you name what everyone already feels. You say: I see a rift in this organisation. There are people comfortable with AI and people who are not. That is my responsibility to fix, and I have not given it enough attention. That is it. That is the whole speech. When a leader names the thing no one will say out loud, the room exhales. The people who were uncomfortable suddenly have permission to be uncomfortable openly which is the first step to becoming comfortable. This rift is one symptom of a bigger truth, why an AI project is really a culture project. This shift from traditional work to AI-enabled work is not the introduction of a new dashboard. It is the equivalent of moving from pen and paper to the personal computer. Those of us old enough to remember that transition know: it was disorienting, it took time, and today you cannot imagine working without it. This is that moment again. Your job is not to have all the answers. Your job is to make it safe to ask the questions.   You are not alone in this   Every executive I speak to is navigating the same thing. Full calendar, growing pressure, a culture quietly fracturing around an invisible line. The leaders who come through this well are not the ones with the best AI strategy on paper. They are the ones who addressed the human side first. Who understood that culture moves faster than technology once the leader decides to move it. That decision starts with naming what is happening. You already know it is happening. Now you know what to do.   If you would rather watch, here is the podcast episode this essay is adapted from or watch the full episode on YouTube. Frequently asked questions What is the AI culture rift? It is the split forming inside most organisations between the employees who have figured out AI, who are moving faster and producing more, and those who have not yet, who feel overwhelmed and left behind. Left unaddressed it reproduces the dynamics of high school inside a professional organisation, and the gap eventually breaks the team you have spent years building. How should a leader respond to the rift? Name it openly at an all-hands meeting. The leader says plainly that there is a rift, that closing it is their responsibility, and that they have not given it enough attention. Naming the thing no one will say out loud lets the room exhale and gives uncomfortable people permission to be uncomfortable, which is the first step to becoming comfortable. Should I lay off employees who are slow to adopt AI? No. Employees who are not yet comfortable with AI are your most valuable asset the moment they cross over, because every person who makes that transition multiplies their output dramatically. Laying people off in the name of AI efficiency is one of the most expensive mistakes a leader can make right now. How big is the shift to AI-enabled work? It is not the introduction of a new dashboard. It is the equivalent of moving from pen and paper to the personal computer: disorienting at first, slow to take hold, and impossible to imagine working without once it does. A single AI-enabled hire can direct customer support, marketing, legal review and financial modelling in parallel, effectively operating as twenty people.   Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. --- ## Clarity at the Top (Episode 2): AI Disruption, Leaders Fix Your Company’s Culture Rift URL: https://anglero.com/2026/04/08/clarity-at-the-top-episode-2-ai-leaders/ Published: 2026-04-08 Thomas Anglero advising world leaders at the World Government Summit New hires who know how to deploy AI agents can operate at twenty times the output of a traditional employee, because they direct an entire team of AI specialists within a single role. That capability is splitting company culture into AI-comfortable and AI-uncomfortable halves. Closing that rift is now a CEO-level responsibility, and it starts with naming it openly. Clarity at the Top podcast summary The modern executive’s calendar is often a relentless tide of back-to-back meetings, leaving little room for strategic thinking, especially regarding AI’s disruptive potential. This overwhelming schedule is not unique; competitors and colleagues face the same challenge. The critical question becomes: where do you find the time to learn and adapt to AI’s rapid evolution? Embrace AI not as a threat, but as an augmentation. New hires, empowered by AI, can operate at a 20x output level, effectively building their own specialised teams within their roles. This AI integration is not about replacing employees, but about creating a significant performance leap. The key challenge for leaders is bridging the ‘AI comfort rift’ within their organisations to foster a unified, high-performing team. If you would rather read than listen, here is the essay version of this episode. Duration: 13:52 · Listen on Spotify, Apple Podcasts, or watch the full episode on YouTube Podcast transcript It’s the beginning of the week and your calendar is already completely full. Back-to-back meetings all day long, all the way 9 to 5. Matter of fact, 8 o’clock in the morning. Matter of fact, some mornings you gotta even take your first meeting from home or from the train or the bus. We all know the drill. You as a top leader, as an executive, you are reacting to this calendar that’s completely full day after day, day after day. And yet you have this thing called AI happening all around you. You’re worried about how it’s gonna disrupt your business, how it’s gonna disrupt your revenue and the future of your company. Matter of fact, it’s disrupting you today, let’s be honest. Welcome to Clarity at the Top podcast, I’m Thomas Anglero. And let me give you some new perspectives, some insights, some more information you didn’t have before, calm you down and give you solutions to help you get out of the situation that you’re in now. Because number one, this is where we start: you’re not alone. All right, I’ll repeat, you’re not alone. Everyone’s overwhelmed. Your competitor’s overwhelmed. The guy in the cubicle next to you is overwhelmed. The woman over there is overwhelmed. Everyone’s calendar is completely full with back-to-back meetings. When it gets to 5 o’clock, so many people are going home, you’re sitting at your desk going, “I’m finally opening my inbox.” We’re all there, we’ve all been there. And where do you have the time to learn this AI stuff? Where do you have this time to see what all these people are doing, all these amazing things, and you say, “Oh it’s just hype, oh it’s so annoying, let me just get what I gotta do.” And you start doing what you’ve always done, grinding away, pounding at the keyboards. But in the back of your mind you get that little voice of, “Why is everybody going home so early? Why is… What about me?” Relax. As a top leader you’re in a difficult situation.   The 20x power of AI-enabled new hires Let me help you to get yourself out of that and also let me help you to expand your company and save your culture. Let’s start first with this: You’re responsible for all your employees and what’s going on in your company, and you’re gonna be hiring new people into your company. So that means that your company is refreshing itself with every new hire. You know this, you’ve been hiring people forever. Don’t have to be speaking to the choir. But what I want you to be aware of now, today, is that your next new hire will be able to launch their own company with AI. Let me explain to you what I’m talking about. In my other screen I have over here, which you can’t see, I actually have all these AI agents running in parallel. They’re completing a project that I have them working on while I’m talking to you. Each AI agent has a different experience level. They have an infinite number of years of experience ’cause they have access to all the information on the internet on doing every individual task that I want them to do. In your organisation today, you have a project manager, you have sales, you have customer support, you have marketing. You have all these individual positions. And with my AI agents sitting over here working off camera, they each are expert in customer support, marketing, sales. It’s all been digitalised. Your next new hire knows how to do this. So you’re thinking, let’s just give an example, product manager. You’re hiring for a product manager. In traditional interviews, you’ll ask them, “What have you done, what clients have you served, and how have you handled different difficult client situations?” You ask about KPIs, building pipeline, and handling unhappy clients. You know the traditional questions. Your new hire is gonna be a person enabled by AI, enlightened by AI. They will say to you, “I will be the project manager for this role, but I’ll be able to deploy an entire team of people to assist me. I’ll work with my colleagues, but I’ll be able to deploy an entire team of people.” They are able to deploy their own marketing team, their own sales team, their own accounting team, their own legal team. But that’s a big question mark, be careful with that one right; respect the lawyers and the law! Every new employee is able to have their own company in their role that they have for you. That new employee who is comfortable with AI and can build their own company within their position for you is 20X-ing their output for you, for your company, for your team, and for the colleagues. Every new hire is a 20X better performer in output, deliverables, and speed than any previous employee. This is the subject of my keynote, When Every Employee Becomes a Leader, Because of AI.   The growing cultural rift in your corporation I’m not saying get rid of your previous employees. That is the wrong way to look at it. The way you should look at your employees, you have a rift happening now in corporations, and this is something I really wanted to get into. The rift is those people who are comfortable with AI and those people who aren’t comfortable with AI. And the organisation, your culture, is beginning to split, and I’m seeing this in a lot of organisations, and as a leader, you’re responsible for it. Now you don’t need any more pressure. You don’t need me to be putting any pressure on you, right? Saying, “Thomas, why are you throwing this pressure on me? I don’t need this right now.” I’m not putting pressure on you. I’m helping you by saying you’re so bogged down in that calendar that’s full of back-to-back meetings, and then you’re bogged down in your inbox ’cause you’ve done all the back-to-back meetings, now you gotta do all the email replies and everything like that. The rift in the culture of your company is growing. What is the rift? There’s those people who are comfortable with AI, they’re producing more, doing more, and human nature, they may be a little bit cocky, a little bit rude in their bravado about knowing AI so well. And those people who aren’t comfortable with AI, who haven’t figured out AI for whatever reason, there are a million reasons, and there’s nothing wrong. These are normal people; these are you and me before we figured out AI. They’re feeling a little bit put to the side. It’s a bit like, remember in high school, where you have the cool kids, and if you’re not part of the cool kids, you felt like you’re outside the group and all that crap? Well, that is the culture that’s being produced inside of your professional corporation. You’re just reproducing the discomfort of high school. Who wants to go through that again? Never again. Your job is to get rid of the rift and make everybody one solid team, right? One solid unit. First thing you need to do is recognise that there is a rift in your culture that is splitting your company, and if that persists, you will not grow and AI will destroy you. The AI influence on cultures of corporations will destroy your corporation, will destroy your future, will destroy anything you’re trying to build, because everybody is in sort of high school mode. God, that in a corporation, what a horrible situation. And that’s the reality of so many people. And people going to work going, “I really am getting uncomfortable with work.”   Leading the AI transition as a CEO And that is ’cause it’s triggering stuff and memories from the past and they can’t associate it. But you’re the leader, you’re the boss, you’re the CEO, you’re the person they report to. They’re the person they look up to, who has to have the answers, now you understand. And you’re also the part-time psychologist as well. Part of the job is how do you address this and how do you fix this? You do that by calling an all-hands meeting or going around to different groups. So I would start off with an all-hands meeting. It can be online, but it’d be better if you have a big auditorium. But if you have people all over the world or in different locations, have an all-hands meeting and just say, “I see a rift in our corporation. We’re introducing AI. There are people who are comfortable with AI and there are people who are not comfortable with AI. It is my responsibility to make everybody comfortable in this organisation and I have not been able to do that.” “But my plan going forward is to make sure that we’re all comfortable with AI. Because those people who are not comfortable with AI, once you understand that it’s not just a tool, this is not just the introduction of a new dashboard that you gotta figure out how to use. This is a major change to how we work going forward for the rest of our lives. This is the equivalent to going from pen and paper to the PC.” And then I remember those days, dating myself, but I remember that transition. That was very weird, you gotta be pretty old to remember that, but that was a major shift. There were a lot of people who were uncomfortable with that and it took some time. But now today, can you imagine doing your job without a PC? Nothing digital. Doing your entire job with pen and paper, it’s impossible to see that. This shift from pen and paper to PC is the same as PC to AI. That’s how important this shift is. “And me as a leader, I have underestimated it. We have some people who are comfortable, we have some people who are uncomfortable, and it’s my job to make everyone comfortable. So going forward, I will spend more resources and more time to make people more comfortable, more one-on-ones to get them to understand the value of AI and where we are going.” “And then as a company, we’ll come back together. I apologize for putting not as much resources on this as I should, but that’s the plan going forward.” Excuse me, doing this in real time. That is one way you can handle it. As a leader, you take the blame because the responsibility is yours, but you also show humanity.   The true ROI of retaining your employees You also talk about the elephant in the room, right? The rift of the people. And once you talk about the elephant in the room, you talk about how the thing that makes them uncomfortable, as their leader will go, “Oh okay, it’s something we can talk about now. In our culture we can discuss this now, and this is gonna be fixed now. This is great, I’m very happy now.” Right, that’s where you start understanding that this thing has separated, understanding that your new hires are gonna be so comfortable on this side it’ll make these people uncomfortable. But these people are just a few days, a few weeks away from sliding over, and this becomes bigger and bigger and bigger, and this becomes smaller and smaller and smaller, and then you have an entire company of AI-enabled people. And each employee is 20X-ing what they were doing before. That’s your goal, that’s where you’re targeting, and that’s what the outcome’s gonna be, a 20X for each employee that you have. This whole AI thing has never, never been about layoffs. Every company who lays off an employee and says, “Because of AI we’re more efficient, I’m laying off 100, 1000, whatever, five, ten employees,” they don’t see that every employee that they laid off is 20X the output of what they’re gonna get from the AI. That’s madness. To lay off people when for every employee you get 20X, but when you lay off 500 people, 500 times 20X, that’s a humongous loss. You didn’t lay off 500 people, you laid off 10,000, what a stupid move. Your employees who are uncomfortable with AI are your most valuable resource because when they come to understand AI, fantastic. That is what you need to keep in mind going forward and becoming a better leader, ’cause you already are a good one. You’re just overwhelmed with everything that’s going on. You’re not alone, you’re okay. I also provide advisory for leaders navigating this. But here are some strategies, right? That’s a strategy going forward: understanding the power of AI, understanding the implication it is having to your culture, to your day-to-day, and let’s be honest, to your sanity. We gotta save your sanity. So smile, it’s not all doom and gloom, okay? I got you, this is the Clarity at the Top podcast, I’m Thomas Anglero. I look forward to sharing with you every week insights into how AI and being a top leader coming together, what happens when that magic comes together, right? And what can you do, give you some different things to think about and help you enjoy the quality of life even more. Spend more time with your family, friends, more time maybe just sitting in the grass, why not? I’m Thomas Anglero. Talk to you next time.   Frequently asked questions How much more can an AI-enabled new hire produce? Around twenty times the output of a traditional employee. A new hire comfortable with AI can deploy an entire team of AI specialists within a single role, their own marketing, sales, accounting and support capability, so they effectively run a small company inside their position, delivering far more in output, speed and deliverables than any previous employee. What is the “AI comfort rift” splitting company culture? It is the divide between the people who are comfortable with AI, producing more and sometimes carrying a little bravado about it, and the people who are not yet comfortable and feel pushed to the side. Left alone it reproduces the discomfort of high school inside a corporation, the culture splits, and the company cannot grow. Should companies lay off employees because AI makes them more efficient? No. This was never about layoffs. Every employee who becomes comfortable with AI is worth around twenty times their previous output, so laying off 500 people is really losing the equivalent of 10,000. Employees who are not yet comfortable with AI are the most valuable resource, because the moment they understand it, their output multiplies. How should a CEO close the rift? By naming it openly, usually in an all-hands meeting, and taking responsibility. The leader says plainly that there is a rift, that making everyone comfortable with AI is their job, that they have underestimated it, and that they will now put real time and one-on-ones behind it. Taking the blame while showing humanity turns the elephant in the room into something the culture can finally discuss and fix. Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. --- ## The Question Your Organisation is Asking About AI is Probably Wrong URL: https://anglero.com/2026/03/31/question-your-organisation-is-asking/ Published: 2026-03-31 The first question most organisations ask about AI, what do we do about this, is the wrong one. It frames everything around protecting what already exists instead of revealing what becomes possible. The leaders who navigate AI transformation well ask a different question: what can we build now that we never could before?   There is a pattern I have observed across hundreds of boardrooms and leadership teams over the past years. When AI enters the conversation, the first question is almost always some version of: what do we do about this? It sounds like the right question. It is not. It is the question of someone looking at today. At the current organisation. The current team. The current business model. The current competition. The current KPIs. Everything is framed around protecting what already exists. I have been watching this same dynamic play out on my Instagram this week. A post about robots and AI reached 36,000 people in the first 24 hours, and it is still climbing. Over 400 comments. The overwhelming theme, fear. People worried about their jobs. People asking whether they will be replaced. I understand that fear. It is human and it is real. But I noticed something in those comments. The people most afraid were asking the same question as the leaders I described above. They were looking at today and asking: what happens to what I have? That is the wrong question.   The right question changes everything   When a forest burns, it looks like destruction. From the ground, looking at the charred trees, it is difficult to see anything else. But beneath that ground, something is already happening. Seeds that needed heat to germinate are now cracking open. Space that was closed is now open. New species that could never survive in the old forest are beginning to grow. The leaders who navigate AI transformation well are not the ones who react fastest. They are the ones who look at the burned ground and ask: what can grow here that could never grow before? What new products become possible when your people are freed from routine work? What business models open up when your cost structure changes? What industries can you enter now that were previously inaccessible? What talent can you attract when your organisation operates differently? What acquisitions make sense now that valuations are shifting? These are the questions of a leader who has made a fundamental shift, from defending the present to designing the future.   This shift begins with you   I want to be direct about something that most leadership conversations avoid. Your organisation will not make this shift until you do. Culture does not change from a strategy document. It does not change from a consultant’s report. It changes when the people who lead begin to see the world differently, and make that visible to the people around them. If you react to AI news with anxiety, your team will too. If you treat every new development as a threat to manage, they will manage it defensively. If your body language in meetings signals that this is a problem rather than an opportunity, the whole organisation will feel it. The most important AI decision you will make this year is not a technology decision. It is a decision about how you carry yourself when uncertainty walks into the room. Leaders who thrive in transformation share one characteristic I have observed consistently. They are energised by adversity rather than diminished by it. Not because they are fearless, but because they have learned to read adversity as a signal that something is shifting, and that shifts create openings. Running towards the fire is not recklessness. It is the recognition that someone may need help, that the situation requires your presence, and that retreating does not make the fire smaller.   What I will share in this newsletter   Each edition of Clarity at the Top will offer one substantive perspective on AI strategy, governance, and leadership, written for senior executives who need to think clearly about these decisions without the noise. No hype. No predictions about which model is fastest. No vendor recommendations. Just the questions worth asking, and some frameworks for answering them, drawn from building, selling, and governing AI systems at the highest levels of global organisations. What I have learned is that the organisations navigating AI well are not necessarily the most technically advanced. They are the ones with leaders who have made the internal shift, from defending today to designing tomorrow. That is what this newsletter is about. I look forward to sharing more with you next week.   Frequently asked questions What is the wrong question to ask about AI? “What do we do about this?” It sounds like the right question, but it frames everything around protecting what already exists: the current team, business model, competition and KPIs. It is the question of someone looking only at today, and it is the same fearful question people ask when they worry about being replaced. What is the right question to ask about AI? What can we build now that we never could before? Instead of defending the present, the question designs the future: what new products become possible when people are freed from routine work, what business models open up when the cost structure changes, which industries and talent become reachable. It is the shift from defending today to designing tomorrow. Why does the AI shift begin with the leader? Because an organisation will not make the shift until its leaders do. Culture does not change from a strategy document or a consultant’s report; it changes when the people who lead begin to see the world differently and make that visible. If a leader reacts to AI with anxiety, the team manages it defensively. The most important AI decision a leader makes is how they carry themselves when uncertainty walks into the room. What separates leaders who thrive in AI transformation? They are energised by adversity rather than diminished by it. Not because they are fearless, but because they have learned to read adversity as a signal that something is shifting, and that shifts create openings. Like a forest after a fire, the burned ground is where new things can finally grow.   Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. --- ## Thomas Anglero | AI Keynote Speaker | EY Norway, AI As A Force Multiplier URL: https://anglero.com/2026/03/17/ey-norway-ai-keynote-speaker/ Published: 2026-03-17 AI Keynote Speaker   March 2026 | Oslo, Norway (AI Keynote Speaker) Thomas Anglero has been selected by EY Norway to deliver a one-hour keynote presentation to 400 of their business consultants on the topic of AI as a Force Multiplier, exploring how artificial intelligence is reshaping work, strategy, and competitive advantage across industries. The session, designed to inspire and equip EY’s consulting teams for the future of work, goes beyond conventional keynote presentations. Thomas will conduct a live AI coding demonstration on stage, building a real-world business case for Norwegian energy company Vår Energi in real time, showing the audience exactly how AI transforms strategic thinking into executable outcomes. This live-build approach reflects Thomas Anglero’s distinctive keynote methodology: making AI tangible, practical, and immediately applicable for business leaders and their teams.   Why EY Norway Selected Thomas Anglero EY Norway chose Thomas Anglero for this high-profile internal event because of his rare combination of deep executive experience and hands-on AI capability. Unlike speakers who present AI concepts from a theoretical distance, Thomas demonstrates AI in action, writing code, building solutions, and solving business problems live on stage. This ability to bridge AI strategy and real-world execution is what keeps Thomas Anglero in demand across Europe and internationally. His presentations do not end with “imagine the possibilities.” They show the possibilities being built, in real time, in front of the audience. Thomas Anglero’s Keynote Speaking Credentials Thomas Anglero is an experienced and sought-after keynote speaker on artificial intelligence, digital transformation, and executive leadership in the AI era. His credentials include: 450+ keynote presentations delivered at international conferences, corporate events, and government summits across Europe, the Middle East, Asia, and North America Clients and stages including the European Union (Brussels), the World Government Summit (Dubai), the World Health Organization, Medtronic, Cisco, Schenker, TechnipFMC, and hundreds of leading organisations worldwide Former Nordic CTO and Chief Innovation Officer at Cognizant, closing over $500 million in enterprise transformation deals Founder of the IBM Watson AI Lab for Cancer at Oslo Cancer Cluster, while serving as IBM’s Nordic Director of Innovation Former Head of Innovation at the Norwegian Tax Authority (Skatteetaten), driving public sector digital transformation Author of “Intro to Artificial Intelligence”, a practical guide for business leaders navigating AI adoption Serial entrepreneur and active AI practitioner, currently building AI-powered products and deploying AI solutions hands-on through his own technology venture, MerkabaPhi AS Chairman and board member across multiple organisations, bringing governance experience to AI strategy at the highest level   What Makes Thomas Anglero Different from Other AI Keynote Speakers Many keynote speakers talk about AI. Thomas Anglero builds with AI, live, on stage, in front of the audience. His keynote presentations are known for combining three elements that are rarely found in a single speaker: Executive credibility. With C-suite and senior technology leadership roles at global companies including IBM and Cognizant, Thomas speaks the language of boards, CEOs, and enterprise leadership teams. He understands the strategic decisions, organisational dynamics, and transformation challenges that executives face, because he has led them himself. Technical hands-on capability. Thomas does not rely on slides to explain AI. He demonstrates AI by building solutions in real time during his presentations. His live coding demonstrations, such as the Vår Energi business case being built on stage at EY Norway, give audiences a concrete, memorable experience of AI’s capabilities that no slide deck can replicate. Global perspective with Nordic precision. Having keynoted on every major continent and advised organisations from Fortune 500 companies to European governments, Thomas brings a global strategic lens combined with the clarity, directness, and trust-based communication style that Scandinavian and international audiences value.   Speaking Topics Available for Booking Thomas Anglero’s current keynote topics address the AI decisions facing executive leadership teams and boards: The AI Experiment Is Over and the Results Have Not Arrived — for executive teams and leadership conferences. Why AI Drains the Moat From the Inside: The Warning Signs Leaders Miss — for executive teams and boards. How to Make AI Stick and Change How Work Gets Done — for leadership teams whose first AI attempt has not produced the results they wanted. All keynote topics are customised to the specific audience, industry, and strategic objectives of each event. Thomas works directly with event organisers to tailor content, examples, and live demonstrations to maximise relevance and impact. Book Thomas Anglero for Your Next Event Thomas Anglero is available for keynote presentations, executive workshops, board briefings, and corporate AI strategy sessions worldwide. If you are an event manager, speaker bureau, or corporate organiser looking for an AI keynote speaker who combines world-class executive experience with live AI demonstrations and genuine audience impact, get in touch through the speaking enquiry page. Frequently asked questions What is Thomas Anglero’s EY Norway keynote about? AI as a Force Multiplier. In a one-hour keynote to 400 EY Norway business consultants, Thomas explores how artificial intelligence is reshaping work, strategy and competitive advantage across industries, and includes a live AI coding demonstration that builds a real business case on stage in real time. What makes Thomas Anglero’s keynotes different from other AI speakers? He builds with AI live, on stage, rather than only talking about it. His keynotes combine executive credibility from senior technology leadership roles at companies including IBM and Cognizant, hands-on technical capability shown through live coding demonstrations, and a global perspective delivered with Nordic clarity and directness. What topics does Thomas Anglero speak on? His current keynotes include “The AI Experiment Is Over and the Results Have Not Arrived,” “Why AI Drains the Moat From the Inside: The Warning Signs Leaders Miss,” and “How to Make AI Stick and Change How Work Gets Done.” Every topic is tailored to the audience, industry and strategic objectives of the event. How do you book Thomas Anglero for an event? Thomas is available worldwide for keynote presentations, executive workshops, board briefings and corporate AI strategy sessions. Event managers, speaker bureaus and corporate organisers can get in touch through the speaking enquiry page to discuss dates and tailoring.   Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. --- ## The Future of Warfare: How AI and Robotics Are Redefining Global Security URL: https://anglero.com/2025/03/19/future-of-warfare-how-ai-and-robotics/ Published: 2025-03-19 Future of Warfare   Roughly 40% of future conflicts will be fought as cyber wars, with AI systems engaging other AI systems under human strategic oversight. On the physical battlefield, dual-use robots that switch from civilian to military roles within hours will replace large standing forces. For nations with small populations, such as Germany and the Nordic countries, AI and robotic forces are a demographic necessity, not a choice.   The New Reality of Modern Conflict As we move deeper into the 21st century, the very nature of warfare is undergoing a profound transformation. The traditional image of human soldiers on battlefields is rapidly giving way to something far more complex: a multi-dimensional conflict space where cyber operations, AI systems, and robotics form the backbone of military power. This isn’t science fiction, it’s already unfolding before our eyes.   The 40% Reality: Cyber Warfare’s Dominance In this new paradigm, approximately 40% of all conflicts will be cyber wars. These digital battlegrounds require fundamentally different talent, skills, and leadership than conventional military operations. Cyber warfare teams will operate both defensively and offensively, becoming the first line of engagement in any conflict. The strategic value is clear: cyber operations can disrupt enemy infrastructure without risking human lives, destroying physical assets, or deploying costly weaponry. Consider that modern missiles often cost upwards of $1 million each, cyber warfare represents a more cost-effective and potentially more impactful approach to achieving strategic objectives. These cyber armies will increasingly be composed of AI systems battling other AI systems, overseen by human strategists working in concert with artificial intelligence. This cyber dimension won’t be isolated, it will integrate with and support every phase of conflict from initial engagement to a peace treaty.   The Robot Revolution in Physical Warfare On the physical battlefield, we’re witnessing the early stages of a revolutionary shift. Today’s industrial and consumer robots represent just the beginning of what’s coming: multipurpose robotic systems that can be rapidly repurposed for military applications. Imagine consumer robots designed with dual-use capabilities, performing household tasks in peacetime but capable of being modified for battlefield deployment within hours when national security demands it. This approach creates a distributed, rapidly scalable robot army that doesn’t require maintaining massive standing forces. This transformation creates entirely new industries around robot maintenance, recovery, and repair. Until robots can fully repair other robots (which is coming, but not immediate), human technicians, electricians, mechanics, technologists, will be in unprecedented demand. The skilled trades that support this robotic warfare ecosystem represent a massive economic opportunity.   The Leadership Challenge: A New Kind of Commander Perhaps most fascinating is how this new warfare paradigm demands entirely new leadership models. Future military leaders must understand how to: Command AI systems with their unique capabilities and limitations Direct robotic forces effectively Integrate human and machine elements into cohesive fighting forces Maintain the human element of strategy and ethics Redefine the Future of Warfare This is fundamentally different from corporate leadership. While CEOs optimise for profit, military leaders optimise for mission success and survival. The best leaders for this new era won’t necessarily come from traditional military academies or corporate boardrooms, they’ll emerge from diverse backgrounds, identified by their unique capacity to understand both human and machine psychology.   The Demographic Imperative This transition isn’t merely a technological preference, for many nations, it’s a demographic necessity. Consider Germany’s recent €500 billion military investment. Despite this massive financial commitment, Germany’s population demographics make it impossible to field a large conventional force of young soldiers. The same applies to Nordic countries with populations of 5-8 million. For these nations, AI and robotic forces aren’t optional, they’re essential to national security. This creates unprecedented demand for AI expertise, robotics capabilities, and the infrastructure to support these new military paradigms.   Where The Commercial Opportunities Are For forward-thinking leaders and organisations, this transformation creates extraordinary opportunities: Development of dual-use robotic systems that serve both civilian and potential military applications Advanced cyber warfare capabilities that can protect national infrastructure AI systems designed specifically for strategic military applications Training programmes for the new generation of human-machine military leaders Support services for the maintenance and deployment of robotic forces The business of war is transforming, and those who understand this shift will be positioned to contribute meaningfully to global security while capturing significant economic value.   Frequently asked questions How much of future warfare will be fought in cyberspace? Roughly 40% of future conflicts will be cyber wars, increasingly composed of AI systems engaging other AI systems under human strategic oversight. Cyber operations can disrupt enemy infrastructure without risking lives or costly weaponry, which makes them the likely first line of engagement in any conflict. What are dual-use robots and why do they matter in warfare? They are robotic systems that perform civilian tasks in peacetime but can be modified for battlefield deployment within hours when national security demands it. They create a distributed, rapidly scalable robot army that removes the need to maintain massive standing forces. Why are AI and robotic forces a demographic necessity for some nations? Because population size, not budget, is the binding constraint. Germany committed €500 billion to defence yet cannot field a large conventional force of young soldiers, and the same applies to Nordic countries of 5 to 8 million people. For them, AI and robotics are essential to national security rather than optional. What new kind of leader does this future of warfare require? One who can command AI systems, direct robotic forces, integrate human and machine elements into a cohesive force, and hold the human element of strategy and ethics. It differs fundamentally from corporate leadership, and the best such leaders will come from diverse backgrounds, marked by an ability to understand both human and machine psychology.   Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. --- ## Maximizing GenAI Benefits: Executive Strategies for Workplace Transformation URL: https://anglero.com/2025/03/10/maximizing-genai-benefits-executive/ Published: 2025-03-10   Generative AI is transforming workplaces faster than previous technologies. 23% of U.S. workers use GenAI tools weekly, leading to significant productivity gains. The result is time savings of at least four hours per week, primarily in tasks like writing and data analysis. Executives like the CIOs must balance innovation with workforce modernisation and ethical AI usage to maximise GenAI benefits.   The GenAI Revolution in the Workplace The rapid adoption of generative AI tools represents a fundamental shift in how work gets done. Unlike previous technological advances that often required years of integration, GenAI is being embraced at unprecedented speeds due to its accessibility and immediate impact. Companies that foster an environment where these tools are properly utilised gain a significant competitive advantage. Employees who leverage GenAI report not only completing routine tasks more efficiently but also having more time for creative and strategic thinking. This shift allows teams to focus on higher-value activities while automating the mundane. However, this transition requires thoughtful leadership to ensure the technology serves both organisational goals and employee development.   Five Executive Strategies for GenAI Adoption Showcase Employee AI Innovation: Highlight AI success stories during every town hall or employee newsletter. This visibility normalises AI usage and inspires others to explore similar applications. Stay Informed Through Regular Dialogue: Have employees explain their AI utilisation regularly. This keeps leadership current with evolving workplace practices and demonstrates genuine interest in employee innovation. Recognise Individual and Team Achievements: Praise specific employees or teams by name for their AI implementations. Focus on improved outcomes rather than cost savings to maintain positive associations with the technology. Enable Peer-to-Peer Learning: Encourage employees to conduct Masterclasses for colleagues interested in AI adoption. This creates an organic knowledge-sharing ecosystem that accelerates organisation-wide capabilities. Champion Cultural Evolution: Embrace the inevitable changes in company culture rather than resisting them. Leaders who position themselves as AI champions set the tone for positive adoption throughout the organisation.   By implementing these strategies, executives create an environment where generative AI becomes a catalyst for growth rather than a source of uncertainty. The most successful organisations will be those where leadership actively guides this technological transition while empowering employees to innovate within appropriate frameworks.   Frequently asked questions How much time does generative AI actually save employees? Research indicates at least four hours per week for workers using GenAI tools, concentrated in tasks like writing and data analysis. With 23% of U.S. workers already using these tools weekly, the aggregate productivity gain across an organisation is substantial. Why is generative AI being adopted faster than previous technologies? Because of its accessibility and immediate impact. Unlike earlier advances that required years of integration, GenAI delivers value almost at once and needs no specialist training to begin, so employees embrace it at unprecedented speed. What are the executive strategies for maximising GenAI benefits? Five: showcase employee AI innovation publicly, stay informed through regular dialogue about how staff use it, recognise individuals and teams by name for outcomes rather than cost savings, enable peer-to-peer learning through masterclasses, and champion the cultural evolution rather than resisting it. Why should recognition focus on outcomes rather than cost savings? Because framing AI around cost savings invites fear of job cuts, while framing it around improved outcomes keeps the association positive. Employees adopt the technology more readily when leadership celebrates what it helps them achieve, not what it helps the company cut.   Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. --- ## Cultural Innovation Before AI Investment | Thomas Anglero URL: https://anglero.com/2025/03/06/cultural-innovation-and-ai-adoption/ Published: 2025-03-06 Thomas Anglero advising on Cultural Innovation Before AI Investment   Most AI investments underdeliver because the organisation’s culture is not ready for them, not because the technology falls short. Nearly 97% of senior leaders report positive returns on AI investments, and one-third of companies plan to invest $10 million or more, yet many lack the experiment-friendly, low-hierarchy culture needed to capture that value. Cultural innovation belongs on the leadership agenda before the next AI budget is approved.   AI is Cultural Innovation The statistics are compelling: nearly 97% of senior business leaders report positive returns on their AI investments, with a significant percentage planning to double down in the coming year. One-third of companies are preparing to invest $10 million or more in AI technologies. Yet beneath these impressive numbers lies a growing concern, executives are increasingly sceptical about their leadership’s ability to keep pace with technological change and deliver on key performance indicators. I saw this directly when I worked on innovation inside Norway’s Tax Authority: the technology was never the constraint, the culture was. This disconnect reveals a critical insight that forward-thinking organisations are beginning to recognise: before making another substantial investment in AI infrastructure, companies must prioritise cultural innovation. Technology alone cannot drive transformation; it requires a corresponding evolution in organisational culture, mindset, and leadership approach.   What is the Cultural Readiness Gap? Many organisations face what might be called a “cultural readiness gap.” They have access to cutting-edge AI tools and substantial budgets but lack the organisational culture to fully leverage these resources. Traditional hierarchies, siloed departments, and risk-averse decision-making processes can significantly hamper AI adoption and impact. Leaders who recognise this gap understand that successful AI implementation isn’t merely about the technology itself but about creating an environment where innovation can flourish. This means fostering a culture of experimentation, embracing calculated risk-taking, and encouraging cross-functional collaboration. This is the subject of my keynote, When Every Employee Becomes a Leader, Because of AI.   Leadership’s New Mandate For executive teams, this cultural shift represents both a challenge and an opportunity. The challenge lies in acknowledging that yesterday’s leadership practices may be insufficient for tomorrow’s AI-driven landscape. The opportunity comes in reimagining leadership itself, moving from command-and-control models to approaches centred on empowerment, continuous learning, and adaptability. Progressive leaders are already placing cultural innovation at the top of their strategic agendas. They’re asking crucial questions: How can we build psychological safety that encourages experimentation? How might we restructure teams to better collaborate with AI systems? What skills and mindsets do our people need to develop? Closing that gap is the focus of my Executive Leadership Advisory.   Building an AI-Ready Culture Organisations that successfully bridge the cultural readiness gap typically focus on several key areas: Developing digital literacy across all levels of the organisation Creating safe spaces for experimentation and learning from failure Redefining roles and responsibilities to complement AI capabilities Establishing clear ethical guidelines for AI implementation Fostering cross-functional collaboration to break down silos   The Critical Question As leadership teams gather for strategic planning sessions, the question becomes unavoidable: Is cultural innovation on the agenda of your next leadership group meeting? If not, even the most substantial AI investments risk failing to deliver their full potential. This is the foundation beneath why an AI project is really a culture project. The most successful organisations recognise that cultural transformation isn’t a one-time initiative but an ongoing journey. It requires persistent attention, measurement, and refinement, much like the AI technologies themselves. The message is clear: before approving that next significant AI budget, ensure your organisation has the cultural foundation to support it. The technology may be revolutionary, but without corresponding cultural innovation, its impact will be merely incremental.   Frequently asked questions Why do most AI investments underdeliver? Because the organisation’s culture is not ready for them, not because the technology falls short. Nearly 97% of senior leaders report positive returns on AI, yet many organisations lack the experiment-friendly, low-hierarchy culture needed to capture that value, so the impact stays incremental. What is the “cultural readiness gap”? It is the gap between having cutting-edge AI tools and substantial budgets on one hand, and lacking the organisational culture to leverage them on the other. Traditional hierarchies, siloed departments and risk-averse decision-making all hamper AI adoption regardless of how much is spent. What should leaders do before approving the next AI budget? Put cultural innovation on the agenda first. That means building psychological safety for experimentation, restructuring teams to collaborate with AI, developing digital literacy at every level, and moving from command-and-control leadership to empowerment and continuous learning. Is cultural transformation a one-time project? No. The most successful organisations treat it as an ongoing journey requiring persistent attention, measurement and refinement, much like the AI technologies themselves. It is not a box to tick before deployment but a continuous leadership discipline.   Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. --- ## AI takes the job of human coders in 2025 URL: https://anglero.com/2025/01/22/ai-takes-the-job-of-human-coders/ Published: 2025-01-22 Photo by Ron Lach   AI agents are starting to take over coding work that human developers used to do. Replit grew revenue five times after going all in on AI agents, Klarna has stopped hiring developers for future IT roles, and Meta and Salesforce have announced hiring freezes for human coders. The future of IT development belongs to leaders who can run teams of people and AI together.   The companies that stopped hiring human coders first Replit CEO went all in on AI Agents to help leaders and increased their revenue 5x. The future of IT development is not about developers, it is about AI coders and great leaders. Amazingly, IT companies are not the first to announce this but companies like Replit and KLARNA in Sweden whose visionary CEOs have seen the light that AI code services code just as good if not better than human coders. For this reason, these companies are the first to not hire human beings for future IT development jobs. Meta and Salesforce have also announced that they will begin a hiring freeze on human coders. The future is being a great human who can lead a team of people and AI to work seamlessly together. AI Agents are what every leader and employee has been wanting. The use of AI agents will be scary and intimidating in the beginning but soon after employees will realise that AI agents make their work day better. It helps them to reach their KPIs quicker and make the corporation shine during difficult times. AI agents are more friend than foe when used properly. Let your people use AI Agents as they come to market and watch your business grow!   Frequently asked questions Is AI really taking over jobs that human coders used to do? Yes, and it began earlier than most expected. Replit grew revenue five times over after going all in on AI agents, Klarna stopped hiring developers for future roles, and Meta and Salesforce announced hiring freezes for human coders. The shift from human-written code to agent-written code is already underway. If AI writes the code, what is left for people in IT development? Leadership. The future of IT development is not the individual developer but the person who can run a team of people and AI agents working seamlessly together. The valuable skill moves from writing code to directing the humans and agents who produce it. Should employees be afraid of AI agents? It is natural to find them intimidating at first, but they are more friend than foe when used properly. Employees quickly discover that AI agents make the working day better, help them reach their targets faster, and make the organisation more resilient in difficult times. What should a leader do about AI agents right now? Let people use them as they come to market rather than restricting them. The organisations pulling ahead are the ones whose leaders encourage adoption and build the skill of leading humans and agents together, rather than waiting for the shift to settle.   Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. --- ## 2025: The Critical Year for AI Leadership and Cultural Transformation URL: https://anglero.com/2025/01/13/ai-leadership-cultural-transformation/ Published: 2025-01-13   Generative AI has already spread through your organisation, whether you planned for it or not, and leaders can no longer control adoption. The job now is to train people to use AI well, because most employees barely scratch the surface of what these tools can do. Organisations that lead this shift keep their best talent; those that try to restrict it lose theirs.   AI Requires Leadership Changes Now AI leadership and cultural transformation are critical business skills in 2025. The landscape of artificial intelligence has undergone a seismic shift. Gone are the days when AI discussions evoked playful references to Arnold Schwarzenegger’s Terminator or Stanley Kubrick’s Space Odyssey. Today’s AI isn’t science fiction, it’s a tangible force transforming every aspect of our business operations and organisational culture.   The Democratisation of AI: A Double-Edged Sword Generative AI has accomplished what decades of traditional AI development couldn’t: it has made artificial intelligence accessible to everyone. From ChatGPT to Claude to Google’s Gemini, accessing AI is now as natural as opening Microsoft Word or checking email. This accessibility isn’t just about convenience, it’s triggering a fundamental transformation in how we work, think, and innovate. However, this democratisation presents a critical challenge for organisational leaders. While previous technological transitions allowed for careful, managed implementation, generative AI has already permeated your organisation, whether you planned for it or not. The reality is stark: if you’re a C-suite executive wondering how to control AI adoption, you’re already behind.   The Leadership Imperative: From Control to Empowerment The traditional paradigm of protective leadership, being the guiding parent who prevents employees from “sticking their fingers in the socket”, has been upended. Your workforce is already deeply engaged with AI tools, using them in ways they personally deem appropriate, often without guidelines or strategic direction. But this isn’t a cause for alarm, it’s an unprecedented opportunity for transformative leadership.   The Strategic Advantage: Training for Excellence As a leader in 2025, your critical role isn’t to restrict AI usage but to optimise it. Most employees are barely scratching the surface of AI’s capabilities, using these powerful tools in rudimentary ways. This is where your leadership becomes crucial: Show them how tasks that once consumed hours can be accomplished in minutes Guide them in understanding that AI-driven efficiency isn’t about doing less, it’s about achieving more Help them redirect their newly available time towards innovation and strategic thinking   The Cultural Transformation Opportunity The real power lies in what happens next. When employees master efficient AI usage, they gain something invaluable: time. But time itself isn’t the asset, it’s how that time is used. As a leader, your role is to cultivate an environment where this surplus time becomes fuel for: Career advancement Innovation initiatives Competitive advantage Personal and professional growth Strategic thinking and planning   The 2025 Ultimatum: Lead or Lose The stakes couldn’t be higher. Organisations that successfully navigate this transformation will find themselves with an energised workforce of future-seekers and innovators. Those that fail risk losing their best talent and being left with a workforce resistant to change and technological advancement.   Your Call to Action As a leader in 2025, your mandate is clear: Step up and lead proactively Guide your team through this transformation Become the role model for effective AI integration Create a culture that embraces technological advancement Foster an environment where innovation thrives   While it’s not too late to start, the cost of inaction grows daily. Your people are watching, waiting, and ready to follow strong leadership into this new era. The question isn’t whether your organisation will be transformed by AI, it’s whether you’ll be the one leading that transformation. The path to 2026 and beyond will be defined by the actions you take now. The future belongs to leaders who embrace this moment and guide their organisations through this unprecedented transformation. The time to act is now.   Frequently asked questions Can leaders still control AI adoption in their organisation? No. Generative AI has already spread through most organisations whether leadership planned for it or not. A C-suite executive still wondering how to control adoption is already behind. The role has shifted from controlling use to guiding it well. If employees are already using AI, what is left for leaders to do? Train them to use it well. Most employees barely scratch the surface of what these tools can do, using them in rudimentary ways. Leadership’s job is to show what is possible, then help people redirect the time AI frees towards innovation and strategic thinking. What happens to organisations that try to restrict AI rather than lead it? They lose their best people. Organisations that lead the shift keep their top talent and gain an energised workforce of innovators. Those that restrict it are left with a workforce resistant to change, at a growing competitive disadvantage. Why is time the real asset AI creates, rather than efficiency itself? Because efficiency only matters by what it frees you to do next. When employees accomplish in minutes what once took hours, the surplus time becomes fuel for career growth, innovation and strategic thinking, provided leadership cultivates an environment that directs it there.   Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. --- ## Great AI will Design the Office of the Future URL: https://anglero.com/2024/12/07/ai-will-design-the-office-of-the-future/ Published: 2024-12-07   AI will design the office of the future because it can weigh hundreds of millions of details at once: parameters, colours, shapes, textures and climate considerations. In an interview with Worklife, I explain why design is still mostly human only because designers are protecting their jobs. One company that redesigned its office using usage data saw voluntary attendance rise 200%.   In a recent interview I had with Worklife, I shared how AI will design the office of the future. This AI influence has already begun but the majority of design today is still done by humans because designers are desperately holding on to their jobs. If they allow AI to take over the art of design, then what jobs will designers, painters, artisans, and other creative people have in the future? Change is coming but many people don’t want it to happen in their life time. Here is the link to my interview. I hope you enjoy it! Here is a short abstract to pique your interest…   AI will design the office of the future   Why will AI design the office of the future? Thomas F. Anglero, former CTO and innovation officer at IT services and consulting firm Cognizant and an authority on AI, stresses that the true power of AI-enabled design tech, as with AI’s benefit in general, lies in its ability to simultaneously process hundreds of millions of different details, including parameters, colors, shapes, textures and climate considerations, far beyond human capabilities.   The shift toward data-informed office design comes at a critical moment, as HR departments contend with hybrid work arrangements and the wider adoption of RTO mandates. How to get employees to fall in love with the office again is a persistent problem to which AI could hold the answer. Deng reports that when his company redesigned its own office based on usage data, it notched a 200% increase in voluntary office attendance.   Frequently asked questions Why will AI design the office of the future? Because it can weigh hundreds of millions of details at once, parameters, colours, shapes, textures and climate considerations, far beyond what a human designer can hold in mind. That capacity to optimise across every variable simultaneously is what makes AI suited to designing space. If AI can design offices, why is most design still done by humans? Largely because designers are protecting their profession. The technology is already capable, but widespread adoption threatens the work of designers, artists and artisans, so the shift is being slowed by people who do not want it to arrive in their working lifetime. Does data-driven office design actually change employee behaviour? Yes. When one company redesigned its office based on usage data rather than assumption, voluntary office attendance rose by 200%. In an era of hybrid work and return-to-office mandates, designing space around how people actually use it is a real answer to a persistent problem. What can AI-driven design consider that human design cannot? It can process parameters, colour, shape, texture and climate together, in combinations too numerous for a person to weigh at once. Human designers work from experience and intuition; AI works from the full space of possibilities measured against real usage data.   Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. --- ## Gartner says IT spending hits $6.31 trillion in 2026. Most of it will be wasted. URL: https://anglero.com/2024/11/09/gartner-it-spend-forecast-for-2025/ Published: 2024-11-09 Gartner IT Spend Forecast   Gartner now expects worldwide IT spending to reach $6.31 trillion in 2026, a rise of 13.5% on 2025, raised from $6.15 trillion earlier in the year. Almost all of the new money is going into AI infrastructure. If there is an AI line in your budget this year, you are part of that $6.31 trillion. Here is the uncomfortable part. Most of it buys infrastructure that changes nothing about how your business actually works, and the leaders who confuse the two will spend a fortune and have nothing to show their people for it. A forecast that keeps climbing When I wrote about Gartner’s outlook in late 2024, the open question was whether enterprises would pull back. Through 2025 many did, in what Gartner described as an uncertainty pause, a deliberate hold on net-new initiatives. Spending rose anyway, and the 2026 number has since been revised upward twice. The growth is concentrated where the AI build-out is loudest: data centre systems, where Gartner expects spending to grow by more than 55% as hyperscalers race to stand up AI-optimised compute, and software and services, where generative AI is quietly lifting the cost of tools companies already run. On Gartner’s figures, IT services alone will pass $1.87 trillion and software will approach $1.44 trillion. Spending is up while expectations have come down The detail most leaders skip past is the split Gartner keeps naming. Expectations for generative AI have slid into what it calls the trough of disillusionment, even as spending on it rises. The hard numbers explain the mood: MIT’s 2025 report, The GenAI Divide: State of AI in Business, found that about 95% of enterprise generative AI pilots had delivered no measurable return. Read that again. Organisations are putting more money into AI in the same year they have quietly lowered what they expect it to do. That is not a contradiction. It is exactly what a real technology shift looks like from the inside: after the launch excitement, before the compounding returns. Buying infrastructure is not the same as getting value The overwhelming share of this $6.31 trillion is infrastructure: servers, data centres, memory, the plumbing of AI. I have led enterprise transformation from inside this market, at IBM and as Cognizant’s Nordic CTO and Innovation Officer, and the pattern never changes. The organisations that see a return are not the ones that bought the most technology. They are the ones that did the human work alongside it. AI is a culture project before it is a procurement line. The hardware arrives in weeks; the change in how people decide, trust and work takes leadership, and that is the line item that never shows up in Gartner’s table. What this means for you If you sit on a board or lead a team, three things follow from this forecast. First, separate the infrastructure decision from the value decision. Knowing the market will spend more than $6 trillion tells you nothing about what your people should do on Monday morning. Second, treat lowered AI expectations as the buying signal, not the warning. The noise is leaving and the real work is starting, and this is the window in which moving first compounds. Third, budget for the human side as deliberately as the technical side, because that is where the return on all this spending is won or lost. The number will keep rising, and the next wave, the move to embodied AI and robotics, will raise the stakes again. The leaders who benefit will be the ones who invest in capability and culture, not only in compute. If the human side is the part you are wrestling with, that is the conversation I have with leadership teams, and it is worth starting before the budget is spent, not after. Frequently asked questions How much will global IT spending reach in 2026? Gartner expects worldwide IT spending to reach $6.31 trillion in 2026, up 13.5% on 2025, and the figure has been revised upward twice. Almost all of the new money is going into AI infrastructure, with data centre systems alone forecast to grow by more than 55%. If spending is rising, why does Gartner say expectations have fallen? Because the two move independently. Expectations for generative AI have slipped into what Gartner calls the trough of disillusionment even as spending climbs. MIT’s 2025 report found about 95% of enterprise generative AI pilots delivered no measurable return, which explains the mood, and it is exactly what a real technology shift looks like after the launch excitement and before the compounding returns. Why will most of the $6.31 trillion be wasted? Because the overwhelming share buys infrastructure, servers, data centres, memory, that changes nothing about how a business actually works. The organisations that see a return are not the ones that bought the most technology, but the ones that did the human work alongside it. AI is a culture project before it is a procurement line. What should a leader actually do in response to this forecast? Three things: separate the infrastructure decision from the value decision, treat lowered AI expectations as the buying signal rather than the warning, and budget for the human side as deliberately as the technical side. That human line item never appears in Gartner’s table, but it is where the return is won or lost. Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. --- ## AI Bubble: Navigating the Future of AI URL: https://anglero.com/2024/10/17/the-ai-bubble-navigating-the-future/ Published: 2024-10-17 AI Bubble by Thomas Anglero   The AI industry shows classic bubble signals: overvalued startups, hype outpacing capability and little differentiation between products. Open-source models are the most likely trigger for a correction, because they erode the advantage of proprietary systems. AI itself is not a bubble; the way it is currently valued and monetised is.   In recent years, the tech world has been abuzz with excitement over artificial intelligence (AI), with companies large and small rushing to integrate AI into their products and services. However, as with any rapidly growing technology sector, questions about sustainability and long-term viability inevitably arise. Is the current AI boom sustainable, or are we witnessing an AI bubble that’s bound to burst? I shared my opinion in this recent interview. The Current AI Landscape The AI industry has seen unprecedented growth, with billions of dollars pouring into startups and established tech giants alike. Large Language Models (LLMs) like GPT-3 and BERT have captured the public imagination, demonstrating capabilities that seemed like science fiction just a few years ago. This has led to a gold rush mentality, with investors and companies scrambling to stake their claim in the AI frontier. Signs of an Impending Bubble Despite the enthusiasm, there are several indicators that suggest we might be heading towards an AI bubble: Overvaluation of AI Companies: Many AI startups are receiving astronomical valuations based on potential rather than proven results or sustainable business models. Hype Outpacing Reality: While AI has made significant strides, the gap between public expectations and current capabilities remains substantial. Lack of Differentiation: As AI tools become more commonplace, many companies struggle to differentiate their offerings in a crowded market. Regulatory Uncertainties: Increasing scrutiny from regulators regarding AI ethics, bias, and data privacy could impact the industry’s growth trajectory. The Open-Source Revolution One of the most significant factors that could contribute to the bursting of the AI bubble is the rise of open-source LLMs. Companies like Meta (formerly Facebook) are leading the charge in this area, releasing powerful models to the public domain. This trend has several implications: Democratisation of AI: Open-source models make advanced AI capabilities accessible to a wider range of developers and organisations, potentially levelling the playing field. Reduced Barriers to Entry: As powerful AI tools become freely available, the competitive advantage of proprietary models may diminish. Acceleration of Innovation: Open collaboration could lead to faster advancements in AI technology, potentially outpacing closed, proprietary development. The OpenAI Pivot: A Sign of the Times? OpenAI’s recent shift from a non-profit to a for-profit model and discussions about a potential IPO can be seen as a strategic response to the changing landscape. This move suggests that even leading AI companies are feeling the pressure to capitalise on their current market position before open-source alternatives gain more ground. The race to monetise may indicate a recognition that the window of opportunity for proprietary AI models could be closing. As open-source alternatives improve, the unique value proposition of companies like OpenAI may diminish, unless they can continually stay ahead of the curve. The Future of AI: Open Source Dominance? While it’s too early to definitively predict the future of the AI industry, the trend towards open-source solutions is undeniable. This shift could have several long-term effects: Commoditisation of Basic AI Capabilities: As open-source models improve, basic AI functionalities may become commoditised, forcing companies to find new ways to add value. Focus on Specialised Applications: To remain competitive, AI companies may need to focus on developing specialised, industry-specific solutions rather than general-purpose AI. Emphasis on Data and Implementation: With the algorithms becoming more accessible, the true value may lie in data quality and effective implementation rather than the AI models themselves. Collaborative Ecosystem: An open-source dominated landscape could foster a more collaborative AI ecosystem, potentially accelerating overall progress in the field. Navigating the AI Bubble For businesses and investors looking to navigate the potential AI bubble, consider the following strategies: Focus on Sustainable Value: Prioritise AI applications that solve real-world problems and deliver measurable value. Embrace Open Source: Consider how open-source AI tools can be leveraged to create unique solutions without reinventing the wheel. Invest in Data and Expertise: High-quality data and AI implementation expertise will likely remain valuable even if basic AI capabilities become commoditised. Stay Agile: Be prepared to pivot strategies as the AI landscape evolves, keeping an eye on emerging trends and technologies. Conclusion While the AI industry is undoubtedly experiencing a period of hype and potentially unsustainable growth, it’s important to recognise that AI itself is not a bubble. The technology will continue to play a crucial role in shaping our future. However, the way we develop, deploy, and monetise AI is likely to undergo significant changes. The rise of open-source AI models may indeed lead to a recalibration of the industry, potentially bursting the bubble of overvalued proprietary AI companies. However, this shift could also usher in a new era of innovation and accessibility in AI technology. As we move forward, it will be crucial for businesses, investors, and technologists to stay informed and adaptable. The AI bubble may burst, but from its aftermath, a more sustainable and impactful AI ecosystem is likely to emerge. What are your thoughts on the future of AI? Do you see open-source models as the key to long-term progress in the field? Frequently asked questions Is AI a bubble that is about to burst? AI itself is not a bubble, but the way it is currently valued and monetised is. The industry shows classic bubble signals, overvalued startups, hype outpacing capability and little differentiation between products, so a correction is plausible even though the technology remains genuinely important. What is most likely to trigger a correction in the AI market? The rise of open-source large language models. As powerful models become freely available, the competitive advantage of proprietary systems erodes, basic AI capabilities commoditise, and the premium valuations attached to closed models become harder to justify. If AI capabilities become commoditised, where does the value go? Into data quality and implementation. Once the algorithms are widely accessible, the durable advantage lies less in the models themselves and more in proprietary data, specialised industry-specific applications, and the expertise to deploy AI effectively. How should a business or investor navigate a possible AI bubble? Focus on AI applications that solve real problems and deliver measurable value, leverage open-source tools rather than reinventing them, invest in data quality and implementation expertise, and stay agile enough to pivot as the landscape shifts. Work with Thomas as a Strategic AI Advisor   Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. --- ## The AI Leader Dilemma: From Show Projects to Strategic Implementation URL: https://anglero.com/2024/10/09/ai_leaders_take_risk_and_build_culture/ Published: 2024-10-09 AI Leaders   According to a Dice.com survey, 36% of AI professionals say most AI projects they support exist primarily to show investors and boards that the company is doing something with AI. These show projects waste budget, demoralise staff and widen the gap behind competitors who implement AI seriously. The fix starts with leaders tying their own KPIs to real AI outcomes. The Current State of AI Projects: A Reality Check A recent survey by Dice.com revealed a startling statistic: 36% of AI professionals believe that the majority of AI projects they’ve been asked to support are “primarily used to show investors, board members, or outside stakeholders that the company is doing something with AI.” This finding underscores a significant problem in the corporate approach to AI adoption. While approximately half of all AI projects are considered strategically important, many companies are still caught in the trap of superficial AI engagement. This begs the question: why are leaders hesitant to fully commit to AI implementation? The Root Causes of “For Show” AI Projects Several factors contribute to the prevalence of superficial AI initiatives: Risk Aversion: Many leaders are unwilling to take substantial risks, especially with emerging technologies. Lack of Understanding: There’s a widespread knowledge gap among executives regarding AI’s potential and practical applications. Time Constraints: Leaders often lack the time to invest in learning about AI technologies and their strategic implications. Misaligned Incentives: Key Performance Indicators (KPIs) for most leaders are not tied to AI implementation, reducing motivation for meaningful engagement. The Consequences of Inaction Maintaining a façade of AI involvement without genuine commitment comes at a significant cost: Wasted Resources: “For show” projects consume valuable time, budget, and human resources without delivering tangible benefits. Missed Opportunities: While companies dabble in superficial AI projects, competitors are making strides in meaningful implementation. Demoralised Workforce: Employees who are enthusiastic about AI’s potential become disillusioned when projects lack substance. Widening Competitive Gap: A great AI divide is emerging between companies that commit to real AI integration and those that don’t. Becoming a True AI Leader For those ready to transition from superficial engagement to strategic AI leadership, consider the following steps: Educate Yourself: Invest time in understanding AI’s potential impact on your industry and company. Develop an AI Strategy: Create a comprehensive plan that aligns AI initiatives with your business objectives. Set AI-Related KPIs: Establish measurable goals tied to AI implementation to drive meaningful progress. Foster a Culture of Innovation: Encourage experimentation and learning around AI technologies. Allocate Resources Wisely: Direct funds and talent towards projects with potential for significant impact. Seek Expert Guidance: Don’t hesitate to reach out to AI specialists or consultants for support and direction. Conclusion: It’s Not Too Late to Lead The AI revolution is still in its early stages, and there’s ample opportunity for leaders to make a significant impact. If you believe in the potential of AI to transform your organisation, now is the time to act. Surround yourself with knowledgeable, motivated individuals who share your vision. Remember, true AI leadership isn’t about having all the answers, it’s about asking the right questions and being committed to finding solutions. Whether you’re well on your way or just starting your AI journey, the key is to move beyond “for show” projects and embrace strategic, impactful AI implementation.   Frequently asked questions What is a “show” AI project? It is an AI project that exists mainly to signal activity to investors, boards or outside stakeholders rather than to deliver real value. A Dice.com survey found 36% of AI professionals say most projects they support are of this kind, doing something with AI for appearances rather than outcomes. Why do so many leaders settle for show projects instead of real AI implementation? Four causes recur: risk aversion around emerging technology, a genuine knowledge gap about what AI can do, lack of time to learn its strategic implications, and misaligned incentives, because most leaders’ KPIs are not tied to AI implementation, so there is little motivation for meaningful engagement. What does a show project actually cost a company? More than wasted budget. It consumes time and talent without benefit, lets competitors pull ahead with serious implementation, demoralises employees who were enthusiastic about AI, and widens the competitive gap between companies that commit to real integration and those that do not. How does a leader move from show projects to genuine AI leadership? Start by educating yourself on AI’s impact on your industry, then build a strategy aligned to business objectives, set AI-related KPIs, foster a culture of experimentation, allocate resources to high-impact projects, and seek expert guidance. True AI leadership is less about having all the answers than about asking the right questions.   Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. --- ## My Leadership Team Is Now Half AI URL: https://anglero.com/2024/09/16/ai-replaces-my-entire-leadership-team/ Published: 2024-09-16 Thomas Anglero on building a leadership team that is half AI   More than half of my leadership team will be AI services, working around the clock for less than $100 per month. This is not an experiment or a stunt; it is the operating model of my AI-first venture, run with AI agents. Here is what the AI seats actually do, what stays human, and why the economics are impossible to ignore.   The model: a leadership team that is half AI A traditional leadership team is a set of standing functions: someone who challenges your strategy, someone who checks your numbers, someone who reviews what leaves the building, someone who watches the market. Each seat costs a salary, works office hours, and takes holidays. My plan is for 50%+ of those seats to be AI services. The AI seats work 24/7, providing feedback, reports and analysis at any hour without hesitation, for a combined cost of less than USD $100 per month. They never tire of a question, never need the context re-explained, and never let a document leave unreviewed because it arrived at midnight. This is the foundation of my AI-first venture, run with AI agents as the operating team.   What the AI seats actually do This is not theory; the seats are already working. My venture’s SEO is 100% done by AI, and the results are steady traffic growth with our most important keywords ranking high in Google, achieved at a speed no human team could match. The AI seats draft and pressure-test documents, produce analysis on demand, prepare positions before negotiations, and act as a standing counsel that challenges my thinking before decisions are made, which is precisely the discipline a real AI initiative demands from its leader.   What stays human The seats that remain human are the ones the model cannot hold: final judgement, accountability, and relationships. An AI can prepare the negotiation; it cannot sit across the table and build trust. It can flag the risk; it cannot own the consequence. The point of the model is not to remove people, it is to stop paying leadership-team prices for work that no longer requires a leadership-team salary, and to redirect the human seats to the work only humans can do.   Why most companies cannot copy this yet The barrier is not the technology or the $100. It is culture and mindset. A business only benefits from an AI leadership seat when its leader actually trusts the seat, uses it daily, and leads the cultural change rather than delegating it. Organisations that invest in cultural innovation before AI investment gain the advantage; those that buy the tools without the mindset get an expensive subscription and no leverage. It is also why I speak on When Every Employee Becomes a Leader, Because of AI: the same model that transforms a leadership team eventually reaches every desk in the company.   Frequently asked questions Can AI really hold seats on a leadership team? Yes, for the standing functions that are work products rather than relationships: feedback, reports, analysis, document review and strategic challenge. The AI seats work around the clock and never need context re-explained. Final judgement, accountability and relationships remain human seats. What does an AI leadership team cost? Less than USD $100 per month for the AI services in my model, against the salary cost of the equivalent human functions. The economics are not an incremental saving; they are a different category of operating model. What results has this model actually produced? My venture’s SEO is run entirely by AI and has delivered steady traffic growth with top keyword rankings in Google, at a speed no human team could match. The AI seats also draft, pressure-test and analyse on demand, functioning as a standing counsel before every significant decision. Why can’t most companies just copy this model? Because the barrier is culture, not technology. The model only works when the leader genuinely uses and trusts the AI seats daily and leads the cultural change personally. Companies that buy the tools without changing the mindset get a subscription, not leverage.   Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. --- ## AI Voice Will Drastically Change Your Life URL: https://anglero.com/2024/08/20/ai-voice-will-drastically-change-your-life/ Published: 2024-08-20   Always-on AI voice assistants will work like a personal secretary that follows you everywhere: recording commitments, noting dates and handling the routine of daily life. The trade is privacy, and it will split people into those who embrace the technology and those who refuse it. In this interview I answer three questions on what AI voice means for people and business.   Recently, I was interviewed and asked several great questions about AI Voice and its impact on people and business. The topic is important because people that embrace this technology will be far ahead of those who do not. There will be new business models and revenue models coming from the increased efficiency we all have and the increased speed we will work. I am quite excited even though there will be some negative drawbacks to privacy as well. Here are the interview questions and my answers:   In what ways might Gemini Live’s voice features differ from those of current AI assistants like Siri, Alexa, or ChatGPT’s Voice Mode, especially concerning naturalness and overall functionality? Siri and Alexa are an older generation of voice capabilities that does not allow for a fluid conversation that includes interruptions which are sometimes a natural part of human speech. I have used ChatGPT voice mode and it is quite close to being natural speech. The hesitation it has in answering a question comes from it thinking (or actually processing) about an answer. The delay before answering is something a user gets used to because the answers are so good that the delay becomes ignored. This is true too of human speech. What privacy concerns could arise with an AI assistant operating both in the background and while a phone is locked? Assuming a person gave the AI assistant permission to always be listening like Amazon Alexa does, then the same concerns are relevant but more so. We carry our mobile phones with us all the time. The phone would be listening all the time to every conversation, voice, noise, and sounds we naturally experience. This feature can be extremely beneficial for vloggers who can now document their entire day at a level they could never have achieved before with just video but there might be a violation of an individual’s privacy involved. This is a new situation which will be debated when more people begin “Voice Vlogging” their life in public spaces and more so, in private situations. Not everything needs to be recorded. How could introducing various voice options for AI assistants affect user engagement and the general perception of AI technology? Once we become used to the new norm that many people have their voice assistant always on, things will feel normal because of the incredible advantages having it on listening, recording, noting down important parts of conversations, commitments we have made on specific dates and times, etc. It will feel like we have a personal secretary following us around all the time taking care of every facet of our personal and business life. People will feel empowered and more efficient. The downside is that we lose some privacy and create a segment of people who refuse to use this technology and then we have a new segment of the haves and have nots. This technology will be abused by certain people but that is true of all new technologies. The flaw is not the technology, it’s that we are human.   Frequently asked questions What is an always-on AI voice assistant? A voice assistant that listens continuously in the background, like a personal secretary that follows you everywhere, recording commitments, noting dates and times, and handling the routine of daily personal and business life. People who adopt it tend to feel more empowered and efficient. How is the new generation of AI voice different from Siri and Alexa? Older assistants like Siri and Alexa cannot hold a fluid conversation that allows interruptions, which are a natural part of human speech. Newer voice modes are close to natural speech; the small delay before answering is the model processing, and users stop noticing it because the answers are so good. What are the privacy concerns with an always-listening AI assistant? Because we carry our phones everywhere, an always-listening assistant would capture every conversation, voice and sound around us. That is useful for documenting a day, but it risks violating the privacy of others, especially in private situations. Not everything needs to be recorded, and this will be debated as more people record their lives. Will AI voice assistants divide people? Yes. As always-on assistants become normal, a divide forms between those who embrace the technology and gain efficiency and those who refuse it, creating a new split of the haves and have-nots. Like every technology it will be abused by some, but the flaw is human, not technological.   Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. --- ## The Future of Tech is Human: Insights from Innovation Expert Thomas Anglero | Unmasking Humanity URL: https://anglero.com/2024/08/07/thomas-anglero-future-of-tech-podcast/ Published: 2024-08-07   In this podcast interview with Joshua T. Berglan, I discuss AI, robotics and why the future of technology is human. The conversation covers my path from New York’s Lower East Side to leading technology organisations, and why AI and robotics may free us to be more human rather than less.   On a hot summer Sunday night, I responded to Joshua T. Berglan‘s request for speakers on his podcast. He instantly connected with my story, values and how I go through life. 18 hours later we were recording! Honestly, this is the best interview I have ever given. Joshua asked me 21 questions that he asks every person he interviews but the questions ignited something within me that made me answer from deep within my soul. About the conversation In this thought-provoking conversation, I share my perspective on the future of technology, the importance of authenticity in innovation, and how to maintain our humanity in an increasingly digital world. We cover the journey from New York’s Lower East Side to becoming a global tech leader, embracing change, finding purpose, and the surprising ways AI and robotics might actually free us to be more human. Whether you are a tech enthusiast or simply curious about our digital future, it offers a fresh, inspiring take on where we are headed and how to thrive in a rapidly evolving world. What Thomas and Joshua discuss Overcoming challenges, resilience, and the role of love and family Early internet innovations and serving the underserved through technology The hard truth about innovation, and debunking the myths around it Breaking from the herd mentality and a healthy caution on social media A tech initiative for youth empowerment and advocating for diversity in tech Concerns about the rapid advance of robotics, and why robots are an opportunity Maintaining well-being in the tech industry The role of AI in global communication Simplifying complex technology concepts so anyone can connect with them A future where the best technology becomes invisible The full timestamped chapters are available on the YouTube video above. It is a personal counterpart to my writing on why AI is a culture project. Frequently asked questions What is the Unmasking Humanity interview with Thomas Anglero about? It is a wide-ranging conversation with Joshua T. Berglan on AI, robotics and why the future of technology is human. Thomas traces his path from New York’s Lower East Side to leading global technology organisations, and argues that AI and robotics may free us to be more human rather than less. Why does Thomas Anglero say the future of technology is human? Because as AI and robotics take on more of the mechanical work, what becomes scarce and valuable is human authenticity, connection and purpose. Handled well, the technology removes drudgery and gives people more room to be human, rather than replacing what makes them human. What does Thomas say about robots? He treats them as an opportunity rather than a threat. His concern is the pace of advancement, but the framing he returns to is that robots, like AI, can lift people when leaders and society choose to use them to serve human needs. How long is the interview and where can I watch it? It runs a little over an hour and is embedded on this page. The full timestamped chapters are available directly on the YouTube video.   Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. --- ## The Best Use Case for Generative AI: Business Continuity Under Deadline URL: https://anglero.com/2024/07/23/best-use-case-for-generative-ai/ Published: 2024-07-23   The best use case for generative AI in the enterprise is business continuity: covering for expert capacity you suddenly do not have. Holidays, sick leave, resignations and deadline collisions no longer have to cost you the deal. Here is the principle, and how to apply it before you need it.   The capacity problem every business has Every organisation, from a sole founder to a multinational, runs on a thin layer of expert capacity. When that layer is unavailable, the business does not slow down politely. Opportunities arrive on their own schedule, and they do not wait for your specialists to return. Most companies simply absorb the loss: the proposal not sent, the contract not reviewed, the deadline missed. It never appears on a profit and loss statement, but it is one of the most expensive line items a business carries. Generative AI changes that equation. For the first time, expert-level drafting, analysis and review capacity is available on demand, at any hour, for a fraction of the cost of a single consultant day. The companies that treat GenAI as deadline insurance, rather than a toy or a threat, are the ones whose best people are already quietly proving the point.   The principle: coverage, not replacement The principle is not that AI replaces your experts. It is that AI covers for them when they cannot be reached, and supplements them when they can. The distinction matters. A business that fires its specialists and hands everything to AI has misunderstood the tool. A business that lets deals die because its specialists are on holiday has misunderstood the moment. I learned this directly one Nordic summer: my entire team was on holiday when a new client opportunity arrived with a 24-hour deadline, and generative AI carried the specialist workload that would otherwise have cost us the contract. The story is less important than the lesson, which is that the coverage was only possible because the working method already existed.   How to apply it before you need it Deadline coverage fails if the first time you try it is during the emergency. Three preparations make it work: Choose and learn the tool now. Whichever frontier model you standardise on, someone senior must already know how it behaves on your real documents before the crisis, not during it. This is part of preparing your culture for AI, not an IT decision. Work in sections, keep the whole in view. Give the AI the complete context of the document or task, then delegate section by section. It will hold the overview, understand why each part exists, and volunteer improvements you had not considered. Keep human judgement as the final gate. The AI produces the options and the speed; you decide what represents the company. That division of labour is what makes the output trustworthy under pressure, and it mirrors the discipline of any real AI project. Executives who build this muscle report the same pattern: routine expert tasks that consumed days now complete in a fraction of the time, and the emergency coverage capability comes free with the practice.   Frequently asked questions What is the best use case for generative AI in the enterprise? Business continuity: covering for expert capacity you suddenly do not have. Deadlines and opportunities do not wait for holidays, sick leave or resignations, and generative AI is the first tool that provides expert-level drafting and analysis on demand when your people cannot be reached. Is this about replacing experts with AI? No. The principle is coverage, not replacement. AI supplements your specialists when they are available and stands in when they are not. Firing experts misunderstands the tool; losing deals because experts are on holiday misunderstands the moment. How should a company prepare for AI deadline coverage? Three steps: standardise on a frontier model and learn its behaviour on real documents before any crisis, practise delegating work in sections while the AI holds the full context, and keep human judgement as the final gate on everything that leaves the building. What does deadline coverage cost compared to the alternative? A fraction of a single consultant day, against the invisible cost of missed proposals, unreviewed contracts and lost deals. The expense never appears on a profit and loss statement, which is exactly why most companies never realise how much unavailable expertise costs them.   Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. --- ## Artificial Intelligence Induces Anxiety and Shifts Skill Requirements URL: https://anglero.com/2024/07/01/artificial-intelligence-induces-anxiety/ Published: 2024-07-01   AI adoption changes how employees see their job security in three ways: it raises anxiety among staff whose tasks AI can perform, it shifts skill demands towards creativity and complex problem-solving, and it creates ethical concerns about AI making decisions on hiring and performance. I was interviewed by Webuters on exactly this question; here is my full answer.   I was interviewed by Webuters on the question, “Does Artificial Intelligence induces anxiety and shifts in skills and requirements?” How does AI adoption affect how employees feel about their jobs?   The increasing adoption of AI in business environments has a significant impact on employee perceptions of job security and potential technology-related job displacement. There are a few areas which are the most affected: 1) Heightened anxiety and uncertainty: Many employees feel anxious about the potential for AI to replace their jobs, leading to increased stress and job insecurity. This is very relevant for older employees whose tasks can be performed by an AI with no possibility for error and who are not willing to reskill themselves. 2) Shifting skill requirements: Younger employees recognise the need to adapt and acquire new skills to remain relevant. They place an emphasis on developing AI-complementary skills. In the future, tasks that require human creativity, emotional intelligence, and complex problem-solving will be in the highest demand. 3) Ethical concerns: Many employees worry about the ethical implications of AI decision-making in areas like hiring, performance evaluation, and resource allocation, which can affect their sense of fairness and job security. There are also extreme concerns when AI is used in healthcare. Who is liable, and can we afford for it to make any mistakes? There are many more aspects to this issue, and this discussion is heating up as people realise that the benefits they have reaped from using AI in their jobs, so their work is not as difficult, will also be the reason why they will struggle to keep their existing jobs.   Frequently asked questions Does AI adoption really make employees anxious? Yes. Many employees feel genuine anxiety about AI replacing their jobs, leading to increased stress and a sense of insecurity. It is felt most acutely by employees whose tasks an AI can perform reliably and who are reluctant to reskill. Which skills become more valuable as AI takes over routine tasks? The human ones. As AI absorbs routine and repeatable work, demand rises for creativity, emotional intelligence and complex problem-solving. Younger employees in particular recognise the need to acquire AI-complementary skills to stay relevant. What ethical concerns do employees have about AI at work? Chiefly fairness in decision-making. Employees worry about AI influencing hiring, performance evaluation and resource allocation, which touches their sense of fairness and job security. Concerns are sharpest in high-stakes settings like healthcare, where liability and the cost of error are serious questions. Why is it a paradox that AI both helps and threatens the same employees? Because the very thing that makes work easier is also what puts the job at risk. The efficiency employees gain from using AI is the same capability that lets an organisation question whether the role is still needed, which is why the discussion is intensifying.   Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. --- ## Artificial Intelligence and Robots will change everything URL: https://anglero.com/2024/06/29/artificial-intelligence-and-robots/ Published: 2024-06-29 Artificial Intelligence and Robots   AI and robots are already changing how work gets done, and the gap between people using the latest tools and those relying on Microsoft Copilot alone is widening fast. In my keynote at Sphere24 in Helsinki, covered as a front-page story by Sweden’s IT publication Voister, I presented Google research showing how much more efficient workers become once generative AI enters their workflow.   Thank you to Voister, Sweden’s leading publication for IT Leaders, for making my keynote at Sphere24 in Helsinki on “How Leaders can Benefit from AI” today’s front page story. Link to Voister’s article YouTube link to the full presentation Contact me to present at your event   What the Google efficiency data shows During my presentation, I discussed how artificial intelligence and robots are already changing the work world by providing statistics reported by Google. Google performed a study that looked at ten different industries and evaluated the efficiency of a worker prior to artificial intelligence and what it was after GenAI came to market. The difference was drastic and the difference is only increasing. Why using Copilot alone leaves you behind The scary reality is that so many workers and leaders who use Microsoft Copilot think that they now have used GenAI to its fullest. Microsoft Copilot is a good solution but it is an infant as compared to the latest version of Anthropic’s Claude and OpenAI’s ChatGPT. If you have only used Copilot, it’s like saying you can be a professional football player by only watching tv. You’re not! The greatest innovations and innovation risks are being taken by these startups who do not have to worry about upsetting other revenue models within the existing business like Microsoft does. Use Claude or ChatGPT to assist you in your day-to-day work. I keep them both open in a browser tab next to each other so I can compare and contrast answers as well as keeping a feeling that I have a team of people working with me to solve a problem. Leaders and employees from all industries should have downloaded both Claude and ChatGPT into their mobiles so they can use them 24/7. The leaders and employees who are not incorporating these tools into their work and personal life are being left behind at an accelerating rate. Watch my “How leaders can benefit from AI and Robots now” presentation on YouTube and get inspired to be a leader that is leading your colleagues and others in your industry. AI-driven humanoid robots are already here Lastly, I discussed the state of AI driven humanoid robots. They are here and Tesla is one of the first to incorporate them into day-to-day work at their battery factory in the US. Look out for BYD to make major announcements in the near future about their humanoid robot and how it too is being utilised in their facilities and being trialled in other industries. Contact me if you want me to present at your event the latest on AI and Robots and what you should be doing to benefit now!   Frequently asked questions How much more efficient does generative AI make workers? In the Google research I presented, across ten industries the efficiency of a worker rose drastically once generative AI entered the workflow, and the gap is only widening. The point is not the exact number but the direction: the distance between people using the latest tools and everyone else is growing fast. Is Microsoft Copilot enough to say you are using AI? No. Copilot is a good solution but an infant compared to the latest versions of Anthropic’s Claude and OpenAI’s ChatGPT. Relying on Copilot alone and thinking you have used generative AI to its fullest is like believing you can be a professional footballer by only watching it on television. Which AI tools should leaders and employees actually use? Use Claude and ChatGPT for day-to-day work, and keep them on your phone so they are available around the clock. A simple habit that works well is keeping both open side by side to compare answers, which feels like having a small team helping you solve a problem. Are humanoid robots already in real workplaces? Yes. Tesla is among the first to put AI-driven humanoid robots into day-to-day work at its battery factory in the US, and BYD is expected to make major announcements about its own humanoid robot being used in its facilities and trialled in other industries.   Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. --- ## Artificial Intelligence & World Government Summit (Dubai 2023) URL: https://anglero.com/2024/02/05/artificial-intelligence-wgs-2023/ Published: 2024-02-05   I spoke on a panel at the World Government Summit in Dubai on value-driven organisations and disrupted business models, in front of an audience of senior government officials from around the world. The opening question cut to the heart of it: how does innovation in the public sector differ from the private sector?   What we discussed on the panel I was invited to speak on a panel at the World Government Summit (Dubai 2023) to discuss value-driven organisations and disrupted business models. The first question I received was about the difference between innovation in the public sector versus the private sector. The panel members were all incredible to be with and shared ideas so that the audience of the world’s most powerful government officials were, I hope, provided with new information. I was most impressed with H.E. from Kazakhstan, Minister of Innovation, who spoke about all the incredible innovation projects they have provided to the people of Kazakhstan. I was truly impressed with his passion and love for his people. The world can learn much from what Kazakhstan has done to improve and innovate their country so their people live better lives. Truly impressive!   Wonderful event and I look forward to speaking at more WGS events and other important events globally.   Frequently asked questions What did Thomas Anglero speak about at the World Government Summit in Dubai? He spoke on a panel on value-driven organisations and disrupted business models, in front of an audience of senior government officials from around the world. The opening question was how innovation in the public sector differs from the private sector. How does innovation in the public sector differ from the private sector? That was the panel’s opening question. The core difference is that public-sector innovation is measured by the value it delivers to citizens rather than by revenue or competitive advantage, which changes both what gets built and how success is judged. What is the World Government Summit? The World Government Summit is an annual gathering in Dubai that brings together heads of state, ministers and senior government officials from around the world to discuss the future of government, innovation and public policy. Thomas has been an invited panellist there. What stood out to Thomas at the 2023 summit? He was most impressed by Kazakhstan’s Minister of Innovation, who spoke about the country’s innovation projects and his evident passion for improving the lives of his people. Thomas noted the world can learn from how Kazakhstan has worked to innovate for its citizens.   Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. --- ## Incredible Embodied AI, the merger of AI and Humanoid Robotics in 2024 URL: https://anglero.com/2024/01/30/embodied_ai_begins_in_2024/ Published: 2024-01-30   Embodied AI is the merger of large language models with humanoid robots, and it is happening now. The two industries have improved in parallel at a frantic pace, and their convergence will change health, food production and quality of life worldwide. In the video below I explain how to prepare yourself and your company.   Embodied AI is happening now. The rapid speed and improvements made with AI-based Large Language Models (LLMs) by companies all over the world have created this perfect convergence with humanoid robots. They in parallel have been improving at a frantic pace. Combining these two disruptive industries will lead to a global change of improvements in health, food, quality of life, and more. In the video below, I explain why you must prepare yourself and your company for this permanent change to our way of life.   Want to know more? Contact me and let’s discuss how to help you.   Frequently asked questions What is embodied AI? Embodied AI is the merger of large language models with humanoid robots. The intelligence that has advanced so quickly in software is now being placed into physical machines that can act in the world, and that convergence is happening now rather than in some distant future. Why is embodied AI happening now? Because two disruptive industries have improved in parallel at a frantic pace: large language models on the software side and humanoid robots on the hardware side. Each reached the point where combining them became practical, creating a convergence that was not possible even a short time ago. What will embodied AI change? It will drive global improvements in areas like health, food production and quality of life, because capable machines can take on physical work at scale. It is a permanent change to the way we live and work, not a passing trend. How should a company prepare for embodied AI? Start treating it as a permanent shift rather than a novelty, and prepare both yourself and your organisation for it. The leaders who benefit are the ones who understand the change early and position their people and processes around it before it arrives in force.   Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. --- ## ChatGPT Voice: Powerful Use Case for an AI Budgeting and Investment Tool URL: https://anglero.com/2024/01/05/chatgpt-voice-use-case-for-ai/ Published: 2024-01-05   ChatGPT Voice is now a realistic alternative to a human financial advisor for the average person. It is conversational, available at any hour, remembers every previous conversation and costs $20 per month. A stock analysis that would take a human advisor several days is done in seconds, with the findings explained aloud as many times as needed.   With the new release of ChatGPT Voice, yes, ChatGPT Voice is now an alternative to human advisors to help the average person with financial questions. The reason is that ChatGPT Voice is conversational. To use AI all the average person has to do is talk to it just like they would to a human financial advisor. The difference is that the AI is available 24 hours per day, 7 days per week, with unlimited patience to answer any question as many times as needed without being annoyed or tired, and will explain the answer in an infinite number of different ways and over 30 languages until the average person is satisfied. One of the key benefits is that it remembers the previous conversation forever, so if a person has a new question on the same topic the next day or week or month, the AI will continue the conversation where you and the AI left off the last time. There is no need to spend time trying to re-establish a foundation of knowledge before you can ask your question. The AI is always ready for the average person when they need help, not only when a human is awake and available. Technically, most people work 8 hours per day but AI is available 24 hours per day. For $20 per month, the average person gets better help, more often, with infinite patience and memory.   AI budgeting and investment tools Lastly, you can ask AI to do a comparative analysis of a pair of stocks or the whole stock market going back months or years, and it will complete the analysis in seconds and verbally explain its findings, and also provide a coloured graph of its findings to help you visually understand the results. This would take a typical financial advisor several days of work, and the margin of error increases with the number of days spent, as do the costs. With AI, it is done quickly and accurately, and can be explained as many times as needed using different references until the average person understands.   Why ChatGPT Voice can act as a financial advisor Yes, the average person will benefit, and so will the typical human advisor who will start using the AI to provide themselves with more insight so that they appear smarter than they were.   Frequently asked questions Can ChatGPT Voice act as a financial advisor? It can act as a conversational alternative for everyday budgeting and analysis questions. It is available around the clock for about $20 per month, answers with unlimited patience in many different ways and over thirty languages, and remembers previous conversations, so a person can pick up a money question exactly where they left off. What can an AI budgeting and investment tool actually do? You can ask it to compare a pair of stocks, or analyse the whole market over months or years, and it returns the analysis in seconds, explains the findings aloud, and can produce a coloured graph to make the results easier to understand. The same work would take a human advisor days. Why is 24/7 availability such a big deal? Most people work about eight hours a day, but questions about money arrive at any hour. An always-on tool is ready when the person needs help rather than when a human advisor happens to be awake and available, and it never tires of explaining the same point again. Does this replace human financial advisors? It changes their role rather than simply removing it. The average person gets more help, more often, for a low monthly cost, and advisors themselves can use the same AI to sharpen their own insight. For regulated, personalised financial decisions, a qualified professional still matters.   Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant. ---