AI for CFOs – 8 finance processes where artificial intelligence pays back fastest

The finance department is a good place for first AI projects, because most of the work consists of numbers and documents, and the results are easy to measure. Not every process is suitable as a starting point, though. Three questions determine the order.

First: how many times a month is the task repeated? Second: is the data you need already in the accounting system, ERP or KSeF, or does someone first have to gather it? Third: can you measure today how much time or money the process costs?

The table below ranks eight processes against these criteria. The first three usually meet all three conditions straight away. The others offer a large benefit, but need better data or longer preparation.

Until recently, the first AI project in finance was usually text recognition from invoices (OCR, which turns a scan or PDF into data a program can read). In 2026 the situation changed. Since 1 April 2026, most domestic purchase invoices from businesses have arrived through KSeF as structured data, so nobody has to retype them any more.

Text recognition is still useful, but only for some documents: foreign invoices, receipts, invoices from companies still using a transitional exemption, and documents that are not invoices. If a supplier offers you an “OCR for all purchase invoices” project today, ask how many such documents are actually left in your company.

The work has therefore shifted from data entry to decision-making. Someone still has to assign the ledger account, cost centre, project and the purchase order the invoice relates to. This is where AI delivers the biggest benefit today.

Coding means assigning an invoice to a ledger account and a cost centre. In a company that receives several hundred invoices a month, it takes accountants many hours. Most invoices from regular suppliers are coded the same way every month, so AI can suggest the coding based on earlier decisions.

The accountant stays in control: they see the suggestion and approve or correct it. The system sets aside unusual invoices, for example from a new supplier or with a new line item, for a manual decision. Over time, the share of suggestions accepted without correction shows how well the solution works.

Calculate the benefit using your own data with the formula below. The example is hypothetical, and you can replace all the assumptions with your own.

An invoice is rarely held up because someone does not want to approve it. It is held up because the request is stuck in an inbox, the manager is on holiday or nobody knows who should sign. An automated workflow solves these problems before AI even comes into it.

A tool such as Power Automate sends the approval request to the right person in Microsoft Teams, sends reminders about the deadline and passes the matter to a deputy. AI adds one thing: based on the invoice content and history, it suggests who should approve it when this is not clear from the purchase order.

The benefit shows in two places. Fewer invoices are paid late, so there is less interest and compensation to pay suppliers. And the accounts team stops wasting time chasing managers for signatures.

Days sales outstanding (DSO) tells you how many days, on average, customers take to pay their invoices. Every day means a specific amount of money tied up with customers. Take a hypothetical company with annual turnover of PLN 80 million: daily sales come to about PLN 219,000.

Cutting DSO by 5 days therefore frees up about PLN 1.1 million in cash. If the company is financed by a working capital loan at 7% a year, that means about PLN 77,000 less interest a year. This is not a promised result, just a way of calculating it – plug in your own turnover and interest rate.

AI helps here in three ways. It identifies the customers most likely to pay late, based on their payment history. It prepares reminders suited to the situation, from a polite email to a firm demand for payment. And it alerts a salesperson that their customer has overdue invoices before they accept the next order.

The other side of cash is liabilities. Some suppliers offer an early-payment discount, for example 2% for paying within 10 days instead of 30. A company that does not take it pays PLN 2,000 more on a PLN 100,000 invoice in exchange for keeping the cash for 20 days longer.

On an annual basis, that is a cost of about 36% (2% × 365 days / 20 days), usually well above the interest rate on a working capital loan. Other invoices can safely be paid on the last day of the payment term.

AI can prepare a proposed payment schedule every week: what to pay early to get the discount, and what to pay on the due date to preserve cash. A person still makes the decision, but no longer has to work everything out by hand in a spreadsheet.

It is also worth automating a check of bank accounts against the Polish VAT white list (the public register of VAT taxpayers and their accounts) before every transfer above PLN 15,000. It is a simple rule. A transfer to an account not on the list means losing the tax-deductible cost and joint liability for the supplier’s unpaid VAT, unless you notify the tax office within 7 days.

Traditional controls involve checking a selected sample of documents. AI can review every transaction and flag the ones that deviate from the norm. It does not decide whether fraud has occurred; it simply puts forward cases for checking.

Typical examples are listed below. The results are hard to predict in advance, because they depend on how many errors there are in your data today. That is why it is a good idea to start with a one-off review of the last 12 months, and only then decide on a permanent solution.

In many companies, the cash forecast is an Excel spreadsheet updated by hand once a week. An AI model can learn from customers’ payment history, seasonality and the purchasing plan, and then show several scenarios, for example what happens if the largest customer pays a month late.

This project is harder than the previous ones. You need at least a year of consistent payment data, and two years is better in strongly seasonal businesses. Before you start, record how far today’s forecast differs from actual results – without that figure, you cannot tell whether the new one is better.

Preparing the monthly reporting pack usually takes the finance team several days. When the board asks about a detail, such as why the margin fell in one region, someone has to go back to the data and prepare an answer for the next day.

Tools such as Copilot in Power BI let you ask that kind of question in plain language and get a chart or a short analysis. AI can also write a first draft of the commentary on variances, which the analyst simply checks.

There is one condition, and it is a strict one: the data must be well organised and clearly defined. If “margin” is calculated differently in two reports, AI will confidently give you the wrong answer. In many companies, most of the work in this project is organising the data, not the AI itself.

Month-end close involves dozens of small tasks carried out by several people in a set order. AI can keep track of the task list and send reminders about overdue steps. It can also flag accounts whose balances differ from previous months and prepare a draft explanation for the board.

This project only makes sense once the close procedure is written down. If every month looks different, first agree on a single sequence of steps. A task list in a work management tool on its own often shortens the close before AI even comes into it.

A safe sequence is three stages, with a decision after each one. First, invoice coding and approval, because they deliver quick, measurable results and tidy up the data. Then receivables, payments and irregularity checks. Finally, forecasts, reports and month-end close, which use the data organised earlier.

In fairness, we also have to say when such a project will not pay off. The situations listed below do not mean giving up on AI for good, only that you need to remove the obstacle before you start.