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Bookkeeping contains exactly the sort of work that automation handles well: repeated formats, high transaction volumes and rules that can be stated in advance. That makes it an excellent place to begin with AI. It also makes overconfidence dangerous, because a small coding error repeated hundreds of times can distort management information and tax records.
Where automation earns its keep
Modern tools can read supplier invoices, suggest ledger codes, identify duplicate documents, match bank transactions and flag entries that do not fit the usual pattern. In a clean, stable process, this can reduce manual keying and allow the bookkeeper to focus on exceptions.
An AAT member described a sensible division of labour: “Bank rules need to be absolutely right.” The system can suggest matches, but the practitioner checks and trains the process.
That is the practical model I favour. Let the system handle the first pass; let a trained person decide whether the evidence supports the accounting treatment.
The errors to expect
AI can confuse similar suppliers, infer the wrong VAT treatment, repeat an error in historical data, or match a payment to the wrong invoice. It may also produce a confident explanation that does not agree with the underlying document. These are not reasons to reject automation. They are reasons to design controls around predictable failure modes.
Five controls I would retain
- Approval thresholds: require human approval for new suppliers, unusual journals, high-value items and changes to standing rules.
- Exception queues: review low-confidence matches, duplicates, missing evidence, unusual VAT codes and transactions outside normal patterns.
- Reconciliations: reconcile bank, receivables, payables and control accounts independently of the automated posting process.
- Audit trail: retain the original document, proposed treatment, final decision and identity of the reviewer.
- Periodic sampling: test apparently successful transactions as well as exceptions; silent systematic errors may otherwise go unnoticed.
ACCA’s work on how AI is reshaping finance emphasises that automation remains constrained by operational, control, cost and data considerations. A gradual implementation is therefore good practice. A parallel run gives the business a baseline, reveals misclassifications and allows rules to mature before reliance increases.
Measure quality, not just speed
A time saving is only valuable if the records remain accurate and reviewable. I would track the proportion of transactions accepted without change, the type and value of corrections, time spent resolving exceptions and whether month-end closes sooner. If automation merely moves work from entry to investigation, the process needs redesign.
The aim is not bookkeeping without bookkeepers. It is bookkeeping in which routine handling is lighter and professional attention is directed towards the entries that carry judgement, risk or commercial significance.
Continue reading on The Perry
- AI in Accounting and Bookkeeping: A Practical Guide for UK Businesses
- From Bookkeeping to Better Decisions: AI Forecasting and Advisory Work
- Working from home when self-employed: simplified expenses or actual costs?
References and further reading
- AAT Comment, “How are accountants and bookkeepers using AI in their day-to-day practice?” (19 August 2026) (accessed 29 August 2026).
- ACCA, “AI Monitor: Exploring the trends, innovations and challenges of artificial intelligence” (accessed 29 August 2026).
- ACCA, “How is AI reshaping finance and accounting work?” (accessed 29 August 2026).
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