About this article This article is personal, general commentary. It does not represent or carry the endorsement of any employer, ICAEW, AAT or another professional body; it is not accounting, tax, legal, investment, employment or data-protection advice; and it creates no adviser–client relationship. No client or employer information has been used. Verify current rules and obtain advice appropriate to your circumstances before acting.
The most promising use of AI in accounting is not faster data entry. It is the opportunity to turn well-kept records into earlier, more useful conversations. If technology can identify a weakening margin, a cash-flow pinch or an unusual spending pattern before the month-end meeting, the accountant can spend more time helping the owner decide what to do.
What AI adds to forecasting
AI-assisted tools can analyse historic sales, payment behaviour, seasonality and cost patterns. They can refresh forecasts more frequently and model alternative assumptions. For a growing business, that may expose a funding gap earlier; for a retailer, it may highlight the cash effect of stock decisions; for a service firm, it may show the impact of delayed billing or recruitment.
ACCA says the direction of travel includes “an expansion in strategic and advisory decision-making.” That is an opportunity, but it also raises the standard of judgement expected from the accountant.
A forecast is not a fact
Every forecast is conditional. Historic patterns may be a poor guide after a price increase, the loss of a major customer or a change in regulation. An AI model can also hide assumptions behind a simple chart. Before presenting the result, I would ask what data was used, what period it covers, which variables matter, how outliers were treated and what happens under a downside case.
This is where professional experience adds value. A business owner may know that a contract is at risk, a supplier is changing terms or a one-off order will not recur. The ledger does not automatically contain that context.
A practical advisory routine
- Start with reconciled and timely bookkeeping; sophisticated analysis cannot repair unreliable source records.
- Agree the decision the forecast is intended to support, such as hiring, borrowing or dividend planning.
- Show a base case and at least one downside case, with the assumptions written in plain English.
- Compare each forecast with actual results and investigate material variances.
- Record where AI assisted the analysis and ensure the final commentary is reviewed by the accountant.
ICAEW’s global research emphasises critical thinking alongside privacy, data security, ethics and client relationships. That combination is telling. Better software does not reduce the need for an accountant who can challenge an output and explain uncertainty without hiding behind technical language.
The human advantage
Clients rarely need another dashboard. They need a conversation that connects the figures to choices: collect debts sooner, change pricing, delay expenditure, obtain finance or accept a period of lower margin. AI can help find signals and test scenarios. The accountant must decide which signals deserve attention, communicate the limitations and relate the numbers to the client’s objectives.
Used on that basis, AI can strengthen advisory work. It does not replace the trusted adviser; it gives that adviser a faster route from transactions to questions worth asking.
Continue reading on The Perry
- AI in Accounting and Bookkeeping: A Practical Guide for UK Businesses
- AI Bookkeeping Automation: Saving Time Without Losing Control
- Accounting, tax and technology
References and further reading
- ACCA, “How is AI reshaping finance and accounting work?” (accessed 29 August 2026).
- Chartered Accountants Worldwide / Ipsos, “AI and the Future of the Global Chartered Accountancy Profession” (April 2025) (accessed 29 August 2026).
- AAT Comment, “How are accountants and bookkeepers using AI in their day-to-day practice?” (19 August 2026) (accessed 29 August 2026).
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