Skip to content
AI Board

For CEOs and managing directors

AI for CEOs: walk into the MT with numbers you can defend

Sunday evening you turn over the cash question. Monday the MT hands you a forecast you cannot fully check. Thursday you learn margin has leaked for six months and nobody flagged it. The answers were in your own files the whole time, spread across a cash forecast, a budget sheet and a margin analysis that nobody had a reason to cross-read. An AI grounded in those files closes that gap, names the document behind every figure, and tells you how hard the advice is.

AI stopped being an IT topic and landed on your desk. BCG's AI Radar 2026 found 72% of CEOs now say they are the main decision maker on AI, roughly double last year's share, and half say their own job is on the line if it does not pay off. That is an uncomfortable position, because you cannot properly evaluate a decision you have never made yourself, and nothing on your calendar is going to teach you what this technology is actually good for.

The case for caution is strong. Gartner reports that by the end of 2025 at least half of GenAI projects were abandoned after proof of concept, with poor data quality and unclear business value named as causes, and RAND puts the failure rate for AI projects above 80%, about twice that of comparable non-AI IT projects. The figure someone has probably quoted at you is MIT NANDA's claim that around 95% of enterprise GenAI pilots deliver no measurable P&L impact, which is contested; we would rather flag that than lean on it. The more useful read is PwC's, from 4,454 CEOs: 56% say AI delivered neither revenue nor cost benefit in the past twelve months, while CEOs with strong AI foundations are three times more likely to report meaningful financial returns. Read that as a foundations problem rather than a technology problem, and the way forward gets narrow and practical.

Foundations, for a managing director of a 200-person firm, means one thing: the assistant has read your ledger, your cash forecast and your customer margins, and it cites the file and version for every number it gives you. That is the difference between an answer you can put in a board pack and one you have to check for an hour first. This page is about your week, question by question. If you want the definition of the role itself, what an AI CEO is and is not covers that. For the act of asking your own files and getting a sourced answer, see chat with your data.

The week you recognize

The cash question you turn over on Sunday

You know roughly what the bank balance is. What you do not know, at 21:00 on a Sunday, is what happens if Q3 collections slip two weeks and the September payroll lands before the receivables do. Your controller can answer it, on Wednesday, in percentages. You decide in euros, and the moment that matters is not the month you run short, it is the week you should have called the bank. The file that holds the answer, 13-Week_Cash_Forecast.xlsx, is usually one version behind and buried in a mail attachment.

A forecast you have to defend but cannot fully check

Monday the MT presents the forecast. Sales talks pipeline, operations talks the order book, finance talks budget, and the three do not reconcile. You sign the board pack anyway, because there is no version of the afternoon in which you personally cross-read Budget_vs_Actuals_2026.xlsx against the order list. Then a shareholder or a bank asks the one question underneath the number, and you are defending a figure you did not build. That exposure, not the gap itself, is the part that costs you sleep.

The margin leak nobody reported

Revenue is up and the bottom line is flat. Nothing in the management report says anything is wrong, because nothing is wrong at the level the report is written: the erosion sits inside individual accounts, in maintenance contracts that were never indexed, a few points at a time. Nobody produces that view without being asked, and you only think to ask in month six. It is money already given away, and the reason it hurts is that it was avoidable housekeeping, not a market shift.

Live demo

Your personal AI assistant, thinking

Revenue is up but the bottom line is flat. Where are margins leaking?

Three accounts (De Groot, Jansen Techniek, Meijer) lost 4-6 margin points since January on maintenance contracts never indexed. Together nearly half the erosion. Index them before the Q4 renewals.

Gross_Margin_by_Customer_H1_2026.xlsxupdated yesterday · v4
Ask AI Board…

What changes

The cash answer in euros, with the week it bites

Ask how many months of liquidity you really have if collections slip, and you get the base case and the downside together, the specific week the pinch lands, the amount you would be short, and what to do about it while you still have room to do it. Months and percentages are paired with an amount, because that is how the decision is actually made. Every figure names the file and version it came from, so you can check it in seconds rather than trusting it blind.

One reconciled version of the number, before the MT sees it

Ask whether you are going to hit budget, and you get the gap located rather than just reported: how much of it is underlying and how much is two named orders sliding into the next quarter, with the customer and order number attached. The distinction between a timing problem and a revenue problem decides whether you intervene, so it is stated plainly, along with how firm the conclusion is and what the file cannot tell you.

The leak found in month one, with the renewal date attached

Ask where margin is going and you get the accounts it concentrates in, how many points each has lost and since when, the annual amount involved, and when each contract comes up for renewal. That last detail turns a finding into a Monday action: which contracts to index before they roll over. It is a signal to act on, presented as one, and never dressed up as a diagnosed cause when the file only shows the pattern.

Answered on demand

Three versions of one number

The pipeline, the order book and the finance forecast tell three different stories. Where exactly do they diverge, and which one belongs in the board pack?

Customer concentration

How much of our revenue sits with our top five customers, how has that concentration moved over the past two years, and which of them have we grown too dependent on?

Cost base drift

Our costs grew faster than revenue this year. Which lines drove it, and which of those are commitments I can still unwind this quarter?

Decisions that never moved

What did we actually decide in the last three MT meetings, and which of those actions has moved since?

Questions, answered

What can AI actually do for a CEO?
It answers the questions you already carry into the weekend, from your own files rather than the open internet: how much liquidity you really have under a downside case, whether the forecast is drifting and why, where margin is leaking and in which accounts. You point it at the cash forecast, the budget file and the margin analysis you already have, and it reads across them and answers with the document and version named for every figure. It does not run the company or make the call. It removes the two days between your question and a defensible answer, which is usually the difference between acting early and explaining late.
Isn't this what my CFO or controller is for?
It is, and this does not replace either of them. The gap it fills is timing and altitude: your controller produces the monthly report, and the questions that matter to you arrive on a Sunday, or ten minutes before the MT, or in the form of a follow-up nobody prepared a slide for. Being able to ask directly, and get an answer that names the file it came from, means you turn up to the conversation with your finance people already knowing what to probe. In practice it makes that conversation shorter and sharper, not redundant.
Why not just use ChatGPT, or look at our BI dashboard?
A general chatbot has never seen your ledger, so it will answer your cash question fluently and with no idea whether it is right, and it has no reason to tell you the difference. A BI dashboard shows you the number, accurately, but not the cause underneath it or the move it implies, and it only covers what somebody built a report for. Waiting for the controller gets you both, three days later. A company-grounded assistant sits in the space those three leave open: your own files, read across, answered now, with the source attached.
Is it safe to put the board pack and the cash forecast on it?
Those files are exactly the ones you would never paste into a public chatbot, which is the reason the product works the way it does. AI Board is private by design: it runs on your own laptop, grounded in the files already on your machine, so your cash position and customer margins do not go somewhere you cannot see. That also matters because the alternative is happening anyway, quietly, wherever people reach for a personal chatbot to get their work done. [Security](/en/security) sets out how that holds up, and [company brain](/en/company-brain) explains what it is reading.

You do not need an opinion on AI. You need to know whether the numbers you are about to defend hold up, and you need it before the meeting rather than after. AI Board is your personal AI assistant that makes you AI-native: it runs on your own laptop, grounded in your company's data, and gets sharper as your knowledge grows. It reasons at CEO, CFO and CTO level on your own files, names its source every time, and is honest about where the file stops and your judgement starts. One week of asking it your own questions will tell you more about what this technology is worth than any strategy deck will.

Ask it the question you carry into the weekend

See what a second brain grounded in your cash forecast, budget and margin files gives back: the answer in euros, the week it bites, and the file it came from. Private by design, on your own laptop.

Runs on your own laptop. Your data never leaves it.