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