AI for finance teams: close faster without losing control

Most finance functions now use AI: 59% of them in 2025. It speeds the monthly close, drafts the reporting and flags the odd transaction before it becomes a problem. But finance is a control function, so the rule is simple: AI assists the numbers, and a qualified person still owns them.

If you run finance for a growing business, the pressure is familiar. The close takes too long, the board pack eats a week, and every reconciliation competes with the analysis anyone actually wants. AI can take real work off that pile. What it cannot do is carry the responsibility for a figure being right, and in finance that distinction is everything.

This post is about where AI safely earns its place in a finance team, and where the human sign-off has to stay put.

How far this has already gone

Adoption is now the norm, not the edge. In the 2025 Gartner AI in Finance Survey of 183 CFOs and senior finance leaders, taken in May and June 2025, 59% of finance functions reported using AI, up slightly from 58% the year before. The growth has levelled off, but the base is high: most teams are already doing this.

What they use it for is telling. The most common use case is knowledge management at 49%, followed by accounts payable process automation at 37% and error and anomaly detection at 34%. None of those is AI writing the accounts. They are AI doing the finding, the sorting and the first draft, with a person deciding what it means.

The direction of travel is clear at the top, too. In Deloitte's Q4 2025 CFO Signals Survey of 200 North American CFOs at businesses with at least a billion dollars in revenue, 87% expected AI to be extremely or very important to their finance department's operations in 2026, and 54% named integrating AI agents in finance as a priority. The question for most teams is no longer whether to use AI, but how to use it without loosening control.

Where AI already helps finance

The safe wins in finance share a shape: AI does the legwork, a person keeps the judgement. Four stand out.

1. Process automation is the least glamorous, which is why it pays. Coding invoices to the right account, matching payments, routing approvals: these are repetitive, rule-shaped tasks, and modern finance tools now handle a large share of them from patterns they have learned on your own ledger. You review the exceptions rather than key every line.
2. Anomaly detection. AI is good at noticing the transaction that does not fit: the duplicate payment, the invoice that is oddly large for that supplier, the coding that breaks a pattern. It flags, and a person investigates. That is a genuine control improvement, because a tool that checks every line never gets tired at line four hundred.
3. Forecasting and analysis. AI can build a first-cut cash-flow forecast, model a scenario, or surface the driver behind a variance far faster than a spreadsheet rebuilt by hand. Ask it why gross margin moved and it will point you at the mix and the pricing in seconds. Treat that output as a draft to challenge, not a result to publish.
4. Reporting drafts. The board commentary, the variance narrative, the first version of the management pack: AI can produce a clean draft from your figures in minutes, which the finance lead then corrects and signs. The hours saved are on the writing, not on the deciding.

We covered the wider practice picture in our note on AI for accountants; this one is about the finance function's own controls.

The control line that cannot move

Everything above works because of one boundary. In finance, AI can assist a number but it can never own it. Hold three habits and the rest is safe.

Every figure that leaves the team is signed off by a named person. AI can propose the reconciliation, draft the commentary and flag the outlier, but the statement that the numbers are right stays with a qualified human, every close, with no exception for a hard month-end.

You keep an audit trail of where AI touched the work. Not a bureaucracy, just enough that if a figure is ever questioned you can show how it was produced and who checked it. This is the modern version of the working papers a finance team already keeps, and it matters most when an auditor or a board member asks.

And no unverified number goes out. AI can state a figure, a growth rate or an explanation with total confidence and be simply wrong, because it predicts plausible text rather than knowing your ledger. In a function where a wrong number is a liability rather than a typo, every AI-produced figure is checked against the source before it is used.

Data quality comes first

AI in finance is only as good as the ledger under it. Point a tool at messy, half-reconciled data and it will automate the mess faster. Before you turn features on, get the basics right: a clean chart of accounts, consistent supplier records, reconciled control accounts. The teams that get value from AI are usually the ones whose books were already in order, and the ones that struggle are usually trying to use AI to paper over a data problem it cannot fix.

This is also where the value case gets honest. Faster processing only helps if the saved time goes somewhere useful, so decide in advance what the analyst freed from reconciliations will actually do. Our note on AI, productivity and business value makes that argument in full: speed is not the same as value, and finance is where that gap shows up quickest.

A safe starter setup

You do not need new software to begin. Start with the AI features already built into the finance system you pay for, because they work on data that already sits in a system under contract not to train on it, and they are built for the task. Turn on automated coding suggestions and payment matching, and treat every suggestion as a first pass to review.

Add anomaly flagging next, pointed at the areas where an error is expensive: payments, expenses, journals. Keep a separate assistant, one that is under contract not to train on your data, for drafting commentary and answering internal queries, and never paste client or employee financial data into a free consumer chatbot open in another tab. A one-page AI policy that names the approved tools and the data that must never be entered is the cheapest protection a finance team can put in place.

The first 30 days

Pick one recurring task the team does at least monthly, and point AI at only that. Accounts payable coding is a good first target: switch on the suggestions, review every one for a full cycle, and note where the tool is reliable and where it needs a firm hand.

At the end of the month you will have three things: a measured sense of the hours saved, a short list of the transaction types AI keeps getting wrong, and the beginnings of the review habit that keeps the numbers right. That is a working practice, not an experiment. Add the next task only once the first one runs cleanly.

The prize is not closing the same books faster. It is freeing the hours that used to go into processing for the work a finance team is actually valued for: the analysis, the challenge, the number that changes a decision. AI makes that trade possible, but only for a team that keeps a firm hand on the sign-off.

If you want help choosing where AI fits your close and setting the controls around it, book a session and we will map it to your finance function.