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.