AI for accountants: what can a UK practice safely hand to it?

AI for accountants is most useful on the work around the numbers, not the judgements in them. In a UK practice it can draft client queries, chase missing records, check how transactions have been categorised, summarise correspondence and prepare first drafts that a qualified person then reviews. It should not sign anything off, give tax advice or make a decision a client relies on. This guide is for practice owners and partners who want to start sensibly. It is written from the operations side: I don’t run an accounting practice, but I have spent years putting AI into finance and document work inside a regulated firm.

Jobs AI handles well in a practice

These are the tasks where AI saves time without moving professional judgement out of the practice:

• Drafting client queries. Turn a reviewer’s rough notes into a clear, polite question for the client, in plain English, naming the transaction or document it refers to.
• Chasing missing records. Compare what a job needs against what has arrived, list the gaps and draft a chaser. Someone checks the list before it goes.
• Reviewing bank and receipt categorisation. Flag transactions that look miscoded, duplicated or out of pattern, so a bookkeeper works through a short list instead of every line.
• Summarising correspondence. Condense a long email thread or a letter from HMRC into the key points, deadlines and actions for the file.
• First drafts of management-account commentary. Given the figures and your notes, draft the narrative that explains the movements, for a person to correct and approve.
• Engagement letters and onboarding packs. Assemble the right template sections for a new client’s services and draft the welcome pack, checked against your current standard terms.
• Internal knowledge questions. Answer staff questions from your own procedures, checklists and technical notes, pointing to the source each time.

The pattern is the same in every case. AI gathers, sorts and drafts. A person decides.

What must stay with a qualified person

Some work should never be handed to a tool, however good the draft looks. Keep these with a qualified, accountable person:

• Signing off accounts, returns and anything that is filed.
• Tax advice, and any judgement about how the rules apply to a client’s position.
• Estimates, provisions and the other judgements inside the numbers.
• Anti-money laundering decisions, including client due diligence and whether to report a suspicion.
• Anything that reaches a client as advice rather than an admin message.
• Deciding whether an exception the AI has flagged actually matters.

AI tools sound just as confident when they are wrong. They can produce a plausible figure, describe a tax rule that does not exist, or write a summary that drops the one sentence that mattered. Treat every output as a junior’s first draft: useful, and always reviewed.

Client confidentiality and data handling

Clients trust you with bank statements, payroll, tax records and personal details. That trust does not change because the processing happens inside an AI tool, and UK GDPR still applies. ICAEW’s guidance on generative AI and ethics applies the fundamental principles of its Code of Ethics to AI use, confidentiality among them. It advises against loading confidential information into public generative AI tools, and asks members to apply professional scepticism to what the tools produce.

In practice that means:

• Use AI tools on business plans with written data processing terms, never on personal accounts.
• Check where data is stored, how long it is kept and whether it is used to train the provider’s models.
• Share only what the task needs, and remove names and identifiers where you can.
• Keep bank details, payroll files, tax references and identity documents out of general chat tools.
• Tell clients how you use AI tools, in your privacy notice and, where it helps, your engagement letter.
• Write the rules down. A short staff policy beats good intentions, and the free AI acceptable use policy template gives you one to edit.

This is practical guidance, not legal advice. Check your own obligations with your professional body and, where you need to, a qualified adviser.

AI tools for accountants: use what you have, or build?

Start with the software you already pay for. Many bookkeeping, practice management and office packages now include AI features. Check what is already in your bookkeeping software and in Microsoft 365 or Google Workspace, read the supplier’s terms on client data, and learn to use those features well before buying anything new. It is the cheapest and lowest-risk first step, because the data is already there.

A custom build makes sense when the task is specific to how your practice works, when it has to connect several systems, or when a built-in feature can’t handle client data the way your policy requires. Examples are a records-chasing workflow that reads your practice management system and drafts chasers for approval, or an internal assistant that answers staff questions from your own procedures. Much of the effort in builds like these goes into reading documents reliably, and the guide to intelligent document processing explains how that part works.

An internal assistant for staff questions is a good first build, and it is work I have done. As Head of AI & Automation at Alter Domus, a global fund administrator, my team built knowledge agents that answered support questions from internal documentation. A fund administrator is a different business from a high street practice, but one lesson carries over: the model is rarely the hard part. The work is in deciding which documents are current, who keeps them that way and who checks an answer before anyone relies on it. More about that work is on the AI for financial services page.

A first 30-day plan

You don’t need a programme. One task, one month, measured honestly:

1. Week one: pick the task and set the rules. Choose one job that repeats every week, such as chasing missing records or drafting client queries. Approve one AI tool on a business plan, adopt a short staff policy and agree what must never be pasted in.
2. Week two: try it by hand. A named person runs the task with the approved tool on real examples, identifying details removed, and keeps the before and after. No automation yet.
3. Week three: tighten and measure. Improve the instructions and templates from what went wrong. Record how long the task takes and how many corrections the reviewer had to make.
4. Week four: decide. Keep it, widen it to a second person, or stop. If it works reliably by hand, that is the time to consider connecting it to your systems, with a person still approving the output.

Don’t skip the option to stop. A pilot that shows a task is not worth automating has still saved you money.

Getting help

Many small practices don’t need a project at all. They need someone alongside them while they tidy one process at a time and turn it into AI tools in their own accounts. That is what the monthly AI partner service is for: from £500 a month, with a minimum of six months and the exact fee agreed in writing. Larger or more connected builds are scoped separately, in a written proposal.

If you want to talk through which job to start with, book a free 30-minute AI consultation. We look at how your practice runs and where AI could save time, with no obligation.