For finance leaders

AI for finance leaders: ask your own numbers, get the answer before the meeting

You already know the questions: how much liquidity headroom, who's paying late, why you're behind budget. The problem is that the answer sits scattered across fourteen tabs and three exports, and the version that goes out under your name is the one you couldn't fully check. An AI grounded in your own files closes that gap.

Join the waitlist What an AI CFO is, and how it fits your week

Adoption has plateaued while the pressure hasn't: 59% of CFOs and senior finance leaders say their teams use AI in 2025, nearly flat versus 58% in 2024, and the top use case is knowledge management, not forecasting. Most tools stall in the same place: they can summarise a document, but they can't read your ledger, so they can't tell you the cause or the move.

The risk isn't that AI is too ambitious; it's that feeding it 'good enough' data produces confident, wrong answers. A generic chatbot has no access to your numbers and will invent them. A BI dashboard shows the number but not why it moved or what to do about it. Neither is safe to walk into a board meeting on.

AI Board is different because it is grounded in your own files and cites its source for every figure: the exact document and version, so you can check it in ten seconds instead of trusting it blind. This page is about how you actually work with it, day to day. For what an AI CFO is, see the AI CFO overview.

The week you recognize

The week-6 dip you only see once it's too late

You clear thirteen weeks on paper, but a receivable slips the same week a purchase commitment falls due and headroom quietly drops to €1.4M. You catch it only if you re-run the whole model by hand, and the version that goes out under your name is the one where you didn't. That's the miss that keeps you up: not the number you can't find, but the one buried in 13-week Cashflow Forecast v14.xlsx that nobody flagged.

DSO creeping eight days while nobody pulls the alarm

€2.1M overdue, four customers making up most of it, and DSO drifting from 48 to 56 days across a quarter without a single trigger. You have a monthly aged-debtors report, but it lists rows. It doesn't synthesise the concentration, name the account against its credit limit, or tell you which call to make today. By the time you spot the trend, the cash has been stuck for weeks.

"Why are we behind budget?" from the owner, every close

Every month-end the same question lands, and the honest answer takes an afternoon of pivoting Budget vs Actual FY26 YTD.xlsx before you can say whether a €320K revenue gap is one customer's lower offtake or a structural hole. The reassurance-with-evidence you want to give ('not structural, opex on plan, one account') is exactly what you can't produce fast enough to walk into the meeting calm.

Live demo

Your personal AI assistant, thinking

Why are we behind budget this month?

You're €320K under on revenue, but 70% (€224K) is lower offtake from one customer, Postma Retail, not structural. Opex is on plan. Call Postma: delayed or permanent, before next month repeats it.

Budget vs Actual FY26 YTD.xlsxupdated yesterday · v3

Ask AI Board…

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What changes

The cause and the move, not just the number

Ask where your liquidity really sits and you get the week it dips, the two positions that cause it, and the one payment to push, with the source document and version named so you can verify before you act. A dashboard stops at the number; a second brain grounded in your files carries it through to the decision.

Every figure cites its file and version

No invented numbers. Each answer points to the exact source ('13-week Cashflow Forecast v14.xlsx, updated today'), so you check the underlying cell in seconds instead of trusting a black box. That's what makes it safe to put your name under the forecast: the trail is right there.

The month-end answer, ready before the meeting

The variance decomposed to the one account that drives it, the aged-debtors concentration named, the slow-moving stock quantified, assembled the moment you ask, from the files already on your machine. You walk into the close with the synthesis you used to build by hand, and time to weigh the trade-off instead of racing the clock.

Answered on demand

Covenant headroom

Where do we stand against our covenants this quarter, and which line is closest to breaching?

Gross margin drift

Our gross margin slipped two points. Is it pricing, mix, or a supplier creeping up on us?

Cash conversion

How long is our cash tied up end to end right now, and where in the cycle is it stuck?

Board pack sanity check

Do the revenue and margin figures in this board pack reconcile with the ledger, or is a tab out of date?

Questions, answered

What is an AI CFO copilot?

It's an AI that works at a finance leader's level, grounded in your own cashflow, debtors, inventory and ledger files rather than the open internet. You ask it the questions you already ask yourself each week, and it answers from your actual numbers, naming the source document and version for every figure so you can check it. It doesn't replace judgement; it removes the hours of manual pivoting between you and the answer.

How is this different from ChatGPT or a BI dashboard?

A generic chatbot has no access to your ledger, so if you ask about your liquidity it will either refuse or invent a number. A BI dashboard shows you the figure but not why it moved or what to do next. AI Board reads your own files, explains the cause, suggests the move, and cites the exact source: the working layer neither of the others gives you.

Is my financial data safe?

AI Board is private by design: it runs grounded in the files on your own machine, so your cashflow forecasts, debtor reports and ledgers stay where they are. Nothing about your finances is published or turned into a marketing claim. You keep control of the documents the AI reads.

Can I trust the numbers it gives me?

That's the whole point of grounding. Every figure is traced to a specific file and version, so instead of trusting a confident answer blind, you verify it against the underlying cell in seconds. The failure mode of most finance AI is confident-but-wrong output from fragmented data; citing the source doc is how you avoid it. We're pre-launch, so treat it as a tool that shows its work, not an oracle.

The questions don't change from Monday to Monday: liquidity, debtors, stuck stock, the budget gap. What changes is whether the answer is ready when you need it, and whether you can stand behind it. An AI grounded in your own files gives you both: the cause and the move, with the source named, before the meeting starts.

Put your own numbers to work

See how a second brain grounded in your cashflow, debtors and ledger answers the questions you already ask each week, with the source cited every time.