Is AI bookkeeping accurate? The question sounds like it wants a yes or a no, and that is the trap. Accuracy in bookkeeping is not one number. It is several different jobs with very different error rates, and a tool that is excellent at one can be quietly dreadful at another.
So the useful answer is conditional. For transaction categorisation against a clean chart of accounts, modern tools are accurate enough that a human reviews exceptions rather than every line. For anything involving judgement about how a transaction should be treated, the tool is a suggestion engine and nothing more. This note explains where the line sits.
Where the accuracy is real
Bank-feed categorisation is the strong case. Once a tool has seen a few months of a business's transactions, it categorises recurring items with high reliability. The corner shop's card-processing fee lands in the right account every time. The recognition gets better the longer it runs, because the firm's own corrections train it.
Reconciliation matching is the other strong case. Matching a bank line to an invoice is a pattern-recognition task, and pattern recognition is exactly what these tools are good at. The accountants we spoke to who use this well treat the matched items as done and spend their attention only on the unmatched residue.
In both cases the accuracy comes with a condition attached. The chart of accounts has to be clean, and someone has to review the exceptions. The tool shrinks the pile. It does not remove the need for a person who understands what they are looking at.
Where it quietly fails
Anything that requires knowing the client's intent is where the trouble starts. Was that €4,000 payment a capital purchase or a repair? The tool will guess from the description, and the description is whatever a tired bookkeeper typed at the time. A wrong guess here is not a rounding error. It changes the tax treatment.
VAT treatment on mixed or unusual supplies is a second soft spot. The rules have edge cases that a general model handles by reaching for the most common answer, which is the wrong answer precisely in the cases that matter.
The failure mode that worries us most is the confident one. The tool does not flag these as uncertain. It files them with the same calm certainty as the easy items, which means the error is invisible unless a human goes looking. That is the argument for review, and it is why "accurate enough to fire the bookkeeper" is the wrong goal. The right goal is accurate enough to change what the bookkeeper spends their day on.
How to use the tools without getting burned
Keep a person on the exceptions and on the judgement calls. Let the tool own the high-volume, low-ambiguity work where it is genuinely strong. Review a sample of the categorised transactions each month rather than assuming the run was clean.
The firms getting the most from this are not the ones chasing full automation. They are the ones who moved their best bookkeeper off data entry and onto the review-and-judgement work the tool cannot do. The headcount stayed. The work got more interesting and the throughput went up.