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AI Board

For CPOs and heads of product

AI for product leaders: portfolio decisions grounded in your own product data

This is the product-executive seat, not the backlog: for CPOs, product directors and heads of product who have to say in the MT which bets are working, why the quarter slipped and what build capacity is actually left. The evidence exists (an adoption export, a committed-versus-actual deck, a retention dashboard), it just never gets cross-read before the meeting starts. An AI grounded in those files closes that gap, and tells you where the file stops.

Join the waitlistTurn your product files into a company brain

Two altitudes get confused under the label "AI for product". One is the product-manager craft: grooming, specs, discovery notes, prioritisation frameworks. That work matters and it has its own page. This page is the other one, the conversation you have with the MT and the board: is this bet returning anything, why did P-2043 move twice, and what do we cut so the strategic work gets built at all.

That conversation is hard for an honest reason. Flat is the base rate. In Microsoft's own experimentation platform work, Kohavi, Crook and Longbotham reported that of well-designed and executed experiments intended to improve a key metric, only about one third actually did (2009, measured on metric movement in controlled experiments, not on shipped features generally). Most of what a good product organisation ships will not move the number. The executive skill is not avoiding that, it is finding out fast, from your own evidence, before the next quarter is committed to the same idea.

The vendor answer is a portfolio platform: buy the suite, migrate your roadmap and telemetry into it, wait for the integration. Your question is due Thursday and the files are already on your drive. AI Board reads them where they are, names the exact document and version behind every figure, and stays private by design: unreleased roadmaps and named-customer churn analyses never need to leave your machine. For the mechanics of asking your own files, see chat with your data.

The week you recognize

Is the bet working, or are we about to double down on nothing?

Adoption on last release reads 6% of accounts in 30 days, and you cannot tell from the number whether that is a slow start or a dead feature. The thing that decides it is the trend, not the level: if week 4 trails week 1, that is decay, not adoption, and if a single segment (say Zorg-NL) is the only one that sticks, you have a discovery problem, not a fit problem. Those are opposite decisions. One means fix the onboarding step where people drop; the other means stop building on this line. You are usually asked to choose between them in a meeting, with a percentage and no trend.

Why the roadmap slipped, with the receipts

Every quarter you have to explain the same slip, and the true explanation is political rather than technical: scope grew mid-quarter through change requests from sales, not because engineering got slower. You know it. You cannot prove it in the room, so it lands as a symptom instead of a decision. PMI's Pulse of the Profession 2018 put scope creep or uncontrolled change on 52% of projects completed in the prior 12 months, up from 43% five years earlier, which tells you it is structural, not personal. What you need in the MT is the change log: which item, which date, how much scope it gained, and who asked.

The reason column nobody distrusts

"Missing functionality" sits on roughly 40% of the quarter's cancellations, and the reflex it triggers costs a quarter of build capacity. Cross that column with ARR and usage depth and it often dissolves: small accounts that never onboarded, plus one large loss (Van Leeuwen) that left on price. Same column, opposite conclusion. Fix onboarding, do not build. The uncomfortable part is that the wrong reading is the easy one, because the reason column is already tabulated and nobody has time to argue with it before the roadmap review.

Live demo

Your personal AI assistant, thinking

Is missing functionality really why these accounts are churning?

'Missing functionality' tags ~40% of Q2 cancels, but distrust that column: crossed with ARR it's small accounts that never onboarded. Van Leeuwen, the big loss, left on price. Fix onboarding, not features.

Retention dashboard - June 2026.pdfQ2 cancels by reason x ARR
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What changes

Level and trend, so the decision is actually made

Ask whether the last release landed and you get adoption by segment with week 1 against week 4, the one segment that stuck, the previous launch's number for comparison, and the specific onboarding step where people drop out. That is enough to say discovery or fit out loud and defend it. The judgement stays yours. What changes is that you make it on the trend from your own adoption export instead of on a single percentage someone read aloud.

A change log you can put on the table

Ask why the initiative slipped and you get the committed items that moved, which one moved twice, the scope each gained mid-quarter and the dated requests that caused it, read from the roadmap review deck and the change log on your drive. That converts the slip from a confession into ammunition: freeze scope at commit, or accept the trade openly. It also gives you the forward version, which quarter goes the same way unless something changes, while there is still time to change it.

An answer that argues with its own source data

Ask whether missing functionality is really driving churn and you get the reason column crossed with ARR and usage, the accounts that never onboarded separated from the one large loss, and a clear statement that the tagged reason and the evidence disagree. Where the file cannot settle it (no usage depth logged, no exit notes) it says so rather than filling the gap. That is the whole point: it will contradict the convenient reading, which is exactly what makes it safe to quote in the roadmap review.

Answered on demand

Capacity versus commitments

How much of next quarter's build capacity is already spoken for by commitments we made to customers, and what is genuinely left for the strategic bets?

Promises made in deals

What has been promised to accounts during renewals or sales cycles that is still not shipped, and which renewals depend on it?

Portfolio maintenance cost

Which parts of the portfolio still earn their maintenance and support cost, and what would sunsetting the weakest line actually free up?

Signal versus one loud account

Which feature requests recur across multiple accounts and segments, and which are one large customer repeated by three different people?

Questions, answered

What does AI do for a CPO or head of product, specifically?
It answers the questions the product-executive seat gets asked, from your own product evidence rather than the open internet: is this bet working, why did the quarter slip, is the reason column telling the truth, what capacity is left. You point it at the adoption export, the roadmap review deck and the retention dashboard you already maintain, and it reads across them and answers with the exact document and version named for every figure. Most AI content written for CPOs describes how the role is evolving. This is aimed at the specific answers you need before Thursday, and it does not run product or replace your judgement.
How is this different from AI for product managers?
Different altitude, deliberately. The product-manager version helps with the craft: discovery notes, specs, prioritisation, keeping a backlog honest. See [AI for product managers](/en/ai-for-product-managers) for that seat. This page is the portfolio conversation above it: which bets to continue or kill, why commitments slipped and what to tell the MT, where build capacity really goes. Same assistant, different questions and different files. If you hold both roles in a smaller company, read the two together.
Everyone wants an AI feature on our roadmap. Can this help decide?
It can gate the decision on evidence instead of enthusiasm, which is where most of these bets go wrong. [MIT's NANDA report on the state of AI in business found that only about 5% of enterprise generative-AI pilots reached rapid revenue acceleration, while the vast majority stalled with little to no measurable impact on P&L](https://fortune.com/2025/08/18/mit-report-95-percent-generative-ai-pilots-at-companies-failing-cfo/); the figure has been publicly contested on methodology and sample, so treat it as a warning about pilot design, not a law. [Gartner predicts that through 2026 organisations will abandon 60% of AI projects unsupported by AI-ready data, and its Q3 2024 survey of 248 data-management leaders found 63% either lack the right data-management practices for AI or are unsure whether they have them](https://www.gartner.com/en/newsroom/press-releases/2025-02-26-lack-of-ai-ready-data-puts-ai-projects-at-risk) (a prediction, not a measurement). Both point the same way: before you spend capacity, ask what recurring evidence in your own files says customers would actually use, and what data the feature would need to work.
Does our unreleased roadmap stay private?
That is the right worry for this role, because the sensitive material is exactly what you would want to ask about: unreleased roadmaps, pricing thinking, named-customer churn analyses. The realistic risk is habit, not policy. [MIT's NANDA research found that employees in over 90% of companies regularly use personal AI tools for work, while only 40% of companies have bought an official LLM subscription](https://fortune.com/2025/08/19/shadow-ai-economy-mit-study-genai-divide-llm-chatbots/), which is how a roadmap deck ends up pasted into a personal chatbot. AI Board is private by design: it runs on your own machine, grounded in the files that are already there. See [security](/en/security) for how that works.

The portfolio questions do not change from quarter to quarter: is the bet working, why did it slip, what do we cut, what do we build. What changes is whether the evidence is assembled when you need it and whether you can stand behind it in the room. 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 shows you the trend, the change log and the contradiction in your own data. The bet is still yours to call.

Put your own product evidence to work

See how an assistant grounded in your adoption exports, roadmap decks and retention data answers the portfolio questions you get asked every quarter, private by design, with the source named every time.

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