AI in accounting
AI in accounting can classify documents, suggest transaction categories, match records, and draft commentary for human review. It is less dependable when source data is incomplete, accounting judgments are ambiguous, or nobody checks the output.
What AI does well
Start with repeatable inputs and a clear review path. Invoice extraction, suggested coding, matching candidates, and first drafts of variance commentary are practical candidates. Keep supporting evidence available to the reviewer.
What AI does badly
AI can produce plausible answers without evidence. Complex revenue recognition, tax positions, unusual transactions, and final accounting judgments need qualified review. An AI answer should not serve as its own audit trail.
Where to start
Choose one recurring process with an owner and observable exceptions. Document what happens today, which systems hold the inputs, and who approves the result. Avoid a project that changes the whole finance function at once.
How to scope the project
Define the input, proposed output, system boundaries, access restrictions, and acceptance criteria. Ask the builder to describe a pilot, exception handling, rollback, and handoff. Use volume and budget bands in your brief, not confidential records.
Measure before expanding
Record the current review effort and error types. Compare pilot outputs with approved records, including difficult cases. Expand only after the finance owner has reviewed the results and agreed on controls.
Put the project into words.
Your systems, your process, your constraints. Bands only.