AI in Accounting: Less Sorting, More Time for Judgment

Accounting involves repeated reading, sorting and asking for context. What was purchased? Does it fit the client’s activity? Is there enough information to handle it correctly? AI can assist with these language-heavy tasks. Calculations, exact matches and established accounting rules belong in dependable software. A useful system connects those two kinds of work so an accountant can spend more attention on exceptions and decisions.

MechaAnetka is an accounting workflow project with a connection to Poland’s national e-invoicing system, KSeF. As the Polish Ministry of Finance explains, KSeF invoices use a structured XML format. The project reads those fields directly and prepares the seller, buyer, total and line items for review. That data preparation is ordinary software automation; AI comes into play when interpreting the purchase descriptions. For businesses receiving scans or PDFs, document extraction could be an earlier step, with the extracted values checked against the original.

The MechaAnetka examples here describe the implementation reviewed for this article. They do not establish production usage, measured time savings or accuracy rates. The purchase scenarios are illustrative and contain no client records.

The same item can mean different things for different clients. Children’s toys could be stock for a toy shop or supplies for a nursery. For an unrelated business, they might prompt a question. MechaAnetka supplies the AI with an accountant-written description of the client’s activity alongside the invoice details. Its review asks two narrow questions: does this purchase involve a vehicle, and does it show signs of personal rather than business use? The output is a review flag, not a ruling on deductibility.

Missing context is a reason to seek more information. It does not establish that an expense is private, and an unflagged invoice is not proof that its accounting treatment is correct.

Imagine a repair invoice describing tyres and an oil change, with no vehicle registration number. A text model can recognise the vehicle connection even when an exact match is unavailable. In MechaAnetka, software first checks available registration numbers and vehicle identification numbers against the client’s vehicle records. A match follows the existing rule-based path and skips the AI review. Unmatched purchases can reach AI, which may flag a vehicle expense or a possible personal purchase for inspection.

• Clear vehicle wording: Fuel, tyres or a named vehicle repair give the reviewer something concrete to work with.

• Ambiguous wording: ‘Maintenance services’ alone could describe IT support or equipment servicing. The model’s instructions explicitly distinguish these cases.

• Human follow-up: An accountant can inspect the invoice, confirm which vehicle was involved and apply the appropriate treatment using the full facts.

A flag helps only if someone can find and resolve it. MechaAnetka stores the review result against the invoice and provides an action to mark the issue resolved. It also records failed AI checks separately and lets the team retry them. That distinction matters: a failed check must remain visible rather than look like a completed review with no concerns. This turns AI output into a manageable piece of the accounting workflow.

The current classifier returns two yes/no signals. It does not provide a written explanation or a confidence score. The accountant still needs to inspect the supporting invoice details.

The same approach can extend to other accounting tasks. These are possible extensions, rather than features verified in MechaAnetka’s invoice review implementation:

• Client questions: Draft a concise request for a missing receipt or the business purpose of a purchase. An accountant checks it before sending.

• Reconciliation assistance: Suggest possible matches when payment descriptions differ from invoice references. Confirm amounts, dates, currency and evidence before accepting a match.

• Month-end commentary: Turn verified report figures into a first draft of an explanation for the client. Check every number and distinguish an observed change from an assumed cause.

• Document intake: Suggest document types and extract fields from incoming files, while keeping the original available for verification.

ICAEW’s guidance on AI skills highlights the continuing need for close human review of AI-assisted financial models. The same discipline is useful for invoice workflows: keep the original document accessible, make unresolved cases visible and define who can approve changes. Decide which client data the model actually needs, where it is processed and who can access it. Review flags should support professional judgment; they should not independently trigger tax filings, payments or final accounting decisions.

Choose a bounded job, such as finding purchase invoices that need clarification about a vehicle. Compare the AI-assisted process with an accountant-reviewed sample before widening its scope. Include clear examples, ambiguous descriptions and documents with missing information. Check both the flagged invoices and a sample of the unflagged ones, so missed issues are visible too.

• Time spent: Measure total review and correction time, including follow-up work.

• Useful flags: Track which alerts lead to a meaningful question or correction, and which create unnecessary work.

• Missed issues and failures: Record problems found in unflagged invoices and checks that never completed.

A worthwhile pilot should show that the team reaches sound decisions with less effort. The number of invoices sent to an AI model tells you very little on its own.