AI for Accountants

Most accountants are already using AI somewhere in the week, even if the firm has not decided anything. A partner asks a chatbot to tidy a client email. A junior pastes a table into a free tool to find a formula. Nobody is being reckless, and nobody has written down what is allowed.

This guide is for the person who has to decide. It sets out what AI is good for in an accountancy firm, which tasks to hand over first, where client information must stay out, and how to check the work before it reaches a client. It is written for small and mid-sized practices, where there is no IT department to take the decision for you.

What can accountants use AI for?

AI is good at work that is made of language and patterns, and poor at work that depends on being exactly right from memory. That split explains most of what is worth doing.

The tasks that tend to work well:

• Drafting and rewording. Client emails, reminder letters, engagement-letter wording, newsletter paragraphs and explanations of a rule for someone who does not speak accounting.
• Summarising. Long documents, meeting recordings, lease agreements, a stack of correspondence on one matter.
• Spreadsheet help. Writing and explaining formulas, cleaning inconsistent columns, turning a messy export into a tidy table.
• Structuring. Turning a rough list into a checklist, a month-end close routine or a set of questions for a client onboarding call.
• First-pass analysis. Pointing out unusual items in a ledger extract for a person to look at, or comparing two versions of a document.

The tasks to be careful with are those where a wrong answer is expensive and the tool cannot show its working: rates and thresholds from memory, a filing position, a figure it has calculated itself. A language model produces text that sounds right. When it is wrong, the sentence still reads smoothly, and that is the danger.

A useful rule is to ask whether you could check the result in under a minute. If you can, it is a good candidate. If checking takes as long as doing the job, the tool has saved nothing, and our guide to what an AI workflow actually costs to run shows how to count that honestly.

Which AI Tools do accountants use?

Three kinds of tool cover most of what a practice does, and the difference between them is mainly about where your data goes.

General-purpose assistants. Products such as ChatGPT and Claude answer questions, draft text and explain documents you give them. They are flexible and quick to start with. Their risk is that people use free personal accounts, where the supplier’s terms on storage and training may not suit client work.

Assistants inside software you already pay for. Microsoft 365 Copilot, for example, works across the documents, email and spreadsheets in your existing licence, inside the firm’s own environment. For many practices this is the easiest to defend, because the data does not leave the system you have already assessed. It is only as safe as the permissions on your files, so tidy those first.

AI built into accounting and tax software. Many platforms now add features for categorising transactions, extracting data from invoices or drafting commentary. These work on structured data you already hold. They deserve the same questions as any supplier: where the data is stored, for how long, and whether it is used to train models.

Do not choose by what is popular. Choose by the task, then by what the supplier will say in writing about your data. The accountancy body ACCA suggests asking exactly this before contracting: what data the system collects, where it is stored, how long it is kept and whether customer data is used to train models. If a supplier cannot answer clearly, think carefully before buying.

If you are weighing a general assistant against one embedded in your office software, our comparison of ChatGPT vs Microsoft Copilot for business covers how they differ on data and cost.

Is it Safe for accountants to use ChatGPT with client Data?

Not by default. This is the question that matters most, and the answer is that it depends on the account, the settings and what you type in.

Accountants owe their clients confidentiality, and that duty does not change because the recipient is software. The ICAEW has warned that entering client information into a public chatbot is likely to breach confidentiality, because once data is in the model you have limited visibility of who it is shared with and how long it is kept. ACCA calls compromising confidential client data one of the major risks of unchecked AI use. In the EU and UK, data protection law adds its own layer. Under the GDPR, personal data must be processed securely, and a supplier that handles it for you is a processor that needs a proper contract.

Four habits keep you on safe ground:

1. Keep identifying details out. Replace the client name with “Client A”, round or remove figures that identify the business, and strip reference numbers. Often the tool needs the shape of the problem, not the identity.
2. Use accounts the firm controls. A business account with clear terms on storage and training is a different thing from a free personal login. If the firm has no approved account, that is the gap to close first.
3. Do not paste what you could not email to a stranger. Bank details, national insurance or tax identifiers, payroll data and anything covered by a client’s own restrictions stay out.
4. Write the rule down. A rule people have not read is no rule. A simple red, amber and green sort works for a practice of any size: green for generic questions, amber for anonymised figures, red for anything identifiable.

Banning the tools is not the answer. Staff who find them useful will carry on using them, privately, on devices you cannot see. That is shadow AI, and the case against banning AI tools at work is that a ban removes your visibility without removing the behaviour.

Can AI help with month-end Reconciliation?

Partly, and the split matters. AI can help you prepare for reconciliation. It should not be what decides whether the books agree.

Where it helps: spotting that two descriptions refer to the same supplier, suggesting a category for an unfamiliar transaction, drafting the query to send to a client about an unexplained payment, and writing the formula that matches two lists. These are patterns in text, and a person checks the result at a glance.

Where it should not be trusted: the arithmetic itself. A chat tool can add a column of figures wrongly and present the answer with full confidence. Keep calculation in the spreadsheet or the accounting software, where the formula can be inspected, and use the AI to write or explain the formula rather than to produce the total.

The same logic applies to anything with a deterministic answer. Matching rules in your software are more predictable than a language model. Use fixed automation for the fixed steps, and a language model for the one step that needs to read untidy text. Our article on the difference between automating a task and automating a decision explains why that line is worth drawing before you build anything.

Can accountants use AI for tax Research?

Yes as a starting point, no as a source. A good use is asking a tool to explain a concept, list the questions to ask about a situation or summarise a long guidance note you have supplied. A poor use is asking it to state a current rate, threshold or deadline and relying on the answer.

The reason is hallucination: a language model can state a rule that does not exist, or a real rule that was changed, in the same confident tone as a correct one. Tax rules change often, by jurisdiction and by year, which is exactly where memory-based answers fail.

A safer method:

• Provide the source. Paste the relevant guidance or give the tool the document, and ask it to answer only from that text.
• Ask it to quote. Require the passage it relied on, so you can read the original in seconds.
• Check the date and jurisdiction. Confirm the rule applies to the right year and the right country before it goes anywhere near advice.
• Record where you checked. A short note of the authoritative source you confirmed against is worth more than the draft.

This is what accountancy guidance asks for. ACCA’s guidance says professional scepticism and human judgement remain crucial when evaluating AI output, and that accountants cannot outsource them to technology. A US professional journal makes the same point from the liability side: the lack of an AI-specific standard is unlikely to defend a negligence claim if the work was not properly reviewed.

How do you check AI output before it reaches a client?

Build the check into the process so it happens every time, not only when someone remembers. A sound routine has four parts.

1. Source check. Every figure, date and rule is traced to the document or authority it came from. If the tool cannot point to one, the claim is a lead, not a fact.
2. Arithmetic check. Totals and ratios are recalculated in the spreadsheet, not accepted from the chat.
3. Scope check. Does the answer deal with this client’s situation, or a generic one? Tools often answer the question they expected rather than the one asked.
4. Sign-off. A named, qualified person approves anything that leaves the firm. This is the human in the loop in practice: the tool drafts, the accountant decides.

Match the depth of checking to the risk. A reworded reminder email needs a glance. A figure in a client’s accounts needs a full trace. Our guide to how to check AI output before it goes to a customer sets out a checklist you can adapt, and it is worth printing one version for the whole team.

It also helps to write a proper brief for the tool, which cuts down on errors before you start. The method in our guide to how to brief an AI tool like a new hire applies directly: say who the tool is acting as, what it may use, what format you want and what to do when it is unsure.

Do accountants have to tell clients they use AI?

The rules vary by country and body, so there is no single answer, and you should not guess. A professional journal for US accountants reports that no specific federal law or professional standard requires disclosing generative AI use, but that a patchwork of state laws does in places, and that tax return information can trigger separate consent requirements. It recommends taking legal advice on what applies to you. In the EU and UK, check your professional body’s current guidance, and your obligations under data protection law where personal data is involved.

Even where nothing requires it, being open tends to help. The same source suggests disclosure and consent should be specific, naming the tools, how they are used, the quality-control steps and any third-party sharing, and that boilerplate in an engagement letter is not enough.

A sensible position for a small practice is a short paragraph, in plain language, that says which tools you use, that client information is handled under your confidentiality obligations and that a qualified person reviews all work. It costs little, and it answers the question before a client asks it.

What should an accountancy firm’s AI Policy say?

Keep it to one page. Long policies go unread. The accountancy bodies recommend firm-wide policies that cover responsible use of AI tools, including when AI can and cannot be used, bias, inaccurate outputs, privacy and what to do if something goes wrong. For a practice, that translates to six short headings:

• Approved tools. The list, and who may add to it.
• Client information. What may and may not be entered, with examples.
• Checking. Who reviews output, and how deep the check goes for each type of work.
• Disclosure. What you tell clients, and where it is recorded.
• Incidents. What to do if client data goes somewhere it should not, and who to tell, including any regulator or client you must notify.
• Ownership. One named person responsible for keeping the policy current.

That last item is the one most firms skip, and it is the one that decides whether the policy survives. Our overview of AI governance for small organisations covers how to keep this light, and the longer piece on governance for smaller firms walks through the same steps. For a practice that wants an outside view of what staff are already doing, an AI audit maps current use and shows where the real risks sit.

Firms in the EU should also note the EU AI Act, which includes a duty on organisations that use AI to make sure their staff have a sufficient level of AI literacy. That is a reason to train, not just to write rules.

Which AI tasks should an accounting firm start with first?

Start with one internal task that is frequent, easy to check and harmless if the first attempt is wrong. Avoid anything client-facing or anything that touches advice until you have evidence of how well the checking works. Good first candidates for a practice:

• Drafting the standard reminder and chaser emails, with a person sending them.
• Summarising long email threads on a matter before a partner meeting.
• Writing and explaining spreadsheet formulas for recurring workpapers.
• Turning meeting notes into an action list and a follow-up email.

Before you begin, write down how long the task takes now, so you can see whether anything improved. After two weeks, time the whole cycle, including checking. If it saved time, keep it or move to a neighbouring task. If it did not, stop and say so. Our guide to which AI tasks a small business should start with first walks through the choice step by step, and the delegation ladder is a useful way to decide how much of a task to hand over.

How do you train an accounting team to use AI?

Training works best when it uses your firm’s own work. Generic sessions on prompts are quickly forgotten, whereas an hour spent on your own reminder emails and your own workpaper formulas sticks.

Cover four things: what the tools are good and bad at, what must never be pasted, how to write a clear instruction, and how to check the result. Pair the rules with a few real examples from your own files, anonymised. Then let people try it on a low-risk task and share what they found.

For hands-on help there are courses on responsible and compliant AI use and on AI in Excel and databases, which suits the spreadsheet-heavy parts of a practice. If you want an outside view of where AI would help and where it would not, an outside check looks at the work already running in the firm.

What follows

Pick one internal task, anonymise what goes in, name the person who signs off the output and write a one-page rule for the firm. Everything else, including tax research, client-facing drafts and any change to advice, can wait until you have a month of evidence about how your own team checks and corrects what the tools produce.

Sources

• ACCA, “Key areas of risk when adopting AI” (March 2026): the accountancy body names confidentiality as a major risk of unchecked AI use, recommends asking suppliers where data is stored, how long it is kept and whether it trains models, calls for firm-wide AI policies and says professional scepticism and human judgement cannot be outsourced to technology.
• Journal of Accountancy, “Should I disclose my use of gen AI to clients?” (April 2025): reports that no specific US federal law or professional standard requires disclosure, that some state laws do, that disclosure should be specific rather than boilerplate, and that the lack of an AI-specific standard is unlikely to defend a negligence claim if work was not properly reviewed.
• ICAEW, “Don’t let AI tools land you in hot water” (February 2024): the professional body for chartered accountants in England and Wales warns about confidentiality risks of entering client data into public AI tools; check its current guidance, as the article dates from 2024.
• EUR-Lex, Regulation (EU) 2016/679 (GDPR): the legal text on secure processing of personal data and on the contract needed with a supplier that processes it for you.
• EUR-Lex, Regulation (EU) 2024/1689 (EU AI Act): the legal text, including the duty on organisations using AI to ensure a sufficient level of AI literacy among their staff.