AI in company finance: accounting, financial analysis and a ChatGPT invoice without errors

What AI in company finance is

Artificial intelligence in finance is not a single program. It is a set of tasks in which the computer takes the load off people: it reads documents, organises data, flags deviations and writes the first draft of a commentary on the results. In a medium-sized company, it usually starts with accounting and cost control and moves on to forecasts and analysis later. How finance fits into the whole topic, from law to sales, is shown in the guide to artificial intelligence in a Polish company.

According to a Gartner survey from November 2025, 59% of finance departments use AI. The most common applications are knowledge management (for example, quickly finding policies and procedures), automating the processing of cost invoices, and detecting errors and unusual items, i.e. exactly the areas where the finance department loses the most time on work whose results nobody outside it sees. For a Polish company, this is a good signpost. Repetitive tasks first, analytics later.

Where AI really helps in finance and accounting

Artificial intelligence in accounting has gained new ground in Poland thanks to KSeF, the National e-Invoicing System (Krajowy System e-Faktur). Since 1 February 2026 the largest businesses have been required to use it, and since 1 April 2026 everyone else. Invoices therefore arrive in a structured format. This makes automatic description and posting much easier. Below are the applications, ordered by risk.

Who AI in finance is for and when it pays off

Those who gain most are companies whose finance department is drowning in documents: several hundred cost invoices a month, several companies in the group, many locations, project accounting. Every hour saved on describing documents is an hour for analysis. In manufacturing companies, where finance ties in with planning and the warehouse, also take a look at the article on AI in manufacturing and ERP systems.

The second group are companies where the board waits weeks for a report because the data has to be gathered from several spreadsheets. AI won’t fix scattered data, but it will help combine and describe it. We describe how to build a tool that shows the board the key figures in one place on the page decision dashboards.

A small company with a dozen or so invoices a month and an external accounting firm stands to gain less. Instead, ask the firm which AI tools it already uses and whether it can share the results with you in a more convenient form, because at that scale building something of your own costs more time than it will ever save.

Financial analysis with ChatGPT: how to use it and what not to do

How can ChatGPT be used in financial analysis? Most safely as an assistant that helps you ask the right questions and describe the results, not as a source of figures. It works well when you need to explain where a budget variance might come from, structure a report for the board, write a spreadsheet formula or review a contract for payment terms. We show how to do this in the spreadsheet itself in the guide on AI in Excel.

What not to do? Don’t ask the model to calculate ratios from memory or to give “current” interest or exchange rates without a source. A language model can state an incorrect figure in a confident tone; we explain why this happens in the article on AI hallucinations. The spreadsheet or the system does the calculating. AI only describes.

English-language guides on AI for financial analysis often show models analysing entire financial statements. For a first review, this works well. Every figure in such a summary still has to be checked against the source document, though, because the model can confuse periods, mix up consolidated and standalone data, or quietly round an amount that matters in the statements.

AI assistants and AI agents in finance: what is realistic in 2026

An AI assistant in finance answers questions and helps with the work, but does nothing on its own. It suggests a posting, explains a procedure, writes an email to a counterparty reminding them about a payment. An AI agent in finance goes a step further and carries out tasks: it retrieves an invoice from KSeF, suggests a description, checks it against the order and creates a task for the approver.

An AI agent for invoices is currently the most mature example, because the process is repetitive and the documents are structured. Even so, start in a mode where the agent prepares and a person approves. We explain how an assistant differs from an agent in the articles on AI assistants in business i o AI agents. Simpler automations, such as passing an invoice from email into the system, can be built without a developer. We show this in the article on automation with AI and n8n.

ChatGPT invoices: how to download an invoice and how to book it

This question comes up in search hundreds of times a month, so we answer it directly. As of September 2026, based on a PłatnikVAT.pl guide from July 2026. This is not tax advice. Confirm the details with your accountant.

Who issues the invoice? When buying directly from OpenAI (website, API, business plan), the issuer is an OpenAI group entity, for customers in the European Economic Area usually OpenAI Ireland Ltd. Check this on the document. When buying through the App Store or Google Play, the invoice is issued by Apple or Google, not OpenAI.

How do you get an invoice in the company’s name? In the account’s billing settings, mark the account as a business account and enter the company details and EU VAT number (the Polish NIP tax number with the PL prefix, registered for intra-Community transactions). You can download the documents in the same tab.

VAT. Buying a service from a foreign supplier is usually an import of services. Under the general rule (Article 28b of the Polish VAT Act), the service is taxed in the country where the buyer company is established, and the tax is accounted for by the buyer itself (Article 17(1)(4)), i.e. the so-called reverse charge applies. An active VAT payer then reports both output tax and, if entitled to deduct, input tax in the same JPK file. A VAT-exempt company must also account for such a purchase, having first registered for EU VAT. If the invoice shows Irish VAT because the account had no EU VAT number, you cannot deduct that tax in the Polish JPK, and the details in the settings need to be completed. Confirm the date the tax obligation arises and the exchange rate with your accountant.

Cost. A subscription used in the business may be a tax-deductible expense. The currency and exchange rate are a matter for your accountant.

1. Buy the plan on a company account, not a private one.
2. Before the first payment, enter the company details and EU VAT number in the billing settings.
3. Download the document from the billing tab every month and pass it on to your accountant.
4. Check the invoice for the issuer, the absence of foreign VAT and a reverse charge note.

Implementing AI in the finance department, step by step

Introducing AI in finance should look like any good controlling project. First measurement, then change, and finally checking the result. Here are five steps for the first quarter:

1. STEP 01Choose one process

   Ideally a repetitive, well-documented one, for example describing cost invoices from KSeF or matching payments. Don’t start with forecasts.

2. STEP 02Measure your starting point

   How many documents a month, how long each one takes, how many corrections and errors? Without these figures you can’t assess the effect.

3. STEP 03Set control rules

   What AI suggests, who approves, and what always requires a person, for example every item above a set amount, a new supplier or a correction.

4. STEP 04Run a pilot on part of the documents

   For example, on one company or one cost category. Compare the AI’s suggestions with the accountant’s decisions and record the discrepancies.

5. STEP 05Evaluate after 60–90 days

   Compare the time and the number of errors with the baseline period. The fairest comparison is with the same month of the previous year, because year-end and quarter-end closes have their own rhythm. Only then expand.

How much AI in finance costs and what the budget depends on

Licences are usually the smallest part of the budget. What costs more is connecting the tool to the accounting and ERP systems, plus the team’s time spent checking results in the first months, when the accountant does their usual work in parallel and compares it with the AI’s suggestions, so for a few weeks the department’s workload tends to rise rather than fall. We compare general-purpose tools in the round-up ChatGPT, Gemini or Copilot for business.

When to introduce AI in finance and when to wait

A good moment is a quieter month after the quarter close. Not December and January, when the finance department is closing the year. Many companies also have a natural reason to act right now: since the switch to KSeF, documents arrive in a single format, and automation is simpler than ever before.

Wait if you are in the middle of changing your accounting or ERP system, if your chart of accounts and reference lists are in disarray, or if nobody in the company can find time to check the results during the pilot. In that case, get things in order first. We describe how to break such problems down into parts before reaching for technology in the article on problem-solving methods in organisations.

How to check whether AI in finance is delivering results

Three indicators are enough to start with: the time taken to process one document, the percentage of AI suggestions accepted without corrections, and the number of errors found only at month-end close. Time falling but errors rising? The savings are illusory.

A faster month-end close after implementation is not necessarily down to AI. There may simply have been fewer documents. So run the pilot on one company and compare it with the others, or compare the result with the same period last year. If you’d like someone from outside to help design such measurement and implementation, visit the page Commplace® AI agency or AI analytics.

Where finance departments most often stumble with AI

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