Using AI to Prepare for a Tax Appointment

The hour your accountant spends working out what happened in your business last year is an hour you pay for. Most of it goes on questions you could have answered in advance: what this recurring payment is, why revenue dropped in March, whether that equipment purchase was a repair or an improvement. AI is good at turning your own records into the briefing that answers those before you sit down.

What it is not good at is deciding the tax treatment. Keep those two jobs separate and this works well.

Start with the questions, not the documents

The instinct is to dump a year of transactions into a chat window. Do the opposite. Ask your accountant's office what they will need, or work from last year's follow-up emails, which are a free list of exactly what was unclear about your records.

Those follow-ups are the best input you have. If last year produced six emails asking you to identify payments, the goal this year is zero.

What to actually prepare

Four artifacts do most of the work.

A plain-language summary of the year. Two paragraphs: what the business did, what changed, anything unusual. New premises, a large one-off contract, a quiet quarter, a change in how you take payment. Your accountant is reconstructing this from numbers, and handing it over directly saves them the reconstruction.

An annotated list of anything that looks odd. Every transaction a stranger would have to ask about. Export your transactions, and use AI to find the ones that stand out:

Here is a year of business transactions as CSV.

Flag transactions that an accountant unfamiliar with my business
would likely need explained: unusually large amounts, one-off
payees, round-number transfers, anything inconsistent with the
surrounding pattern.

For each, give me the date, amount, payee, and a one-line note
on why it stands out. Do not guess what it was for.

Return a table, most unusual first.

Then you write the explanation next to each one. The model finds them; you identify them.

A list of your own questions. Things you want a decision on. Whether to change how you take a salary, whether a purchase should be treated a particular way, whether it is time to change structure. Writing these down beforehand is the difference between getting answers and remembering the question in the car afterwards.

The documents themselves, in one place, named sensibly. Not glamorous, and it saves more time than anything else on this list.

Where AI genuinely helps

Categorizing the long tail. Most transactions categorize themselves. The last fifty do not, and they are the ones that eat an evening. A model working from your own historical categorizations is good at this, and our guide on categorizing business expenses with AI covers the prompt in detail.

Explaining the variance. "Revenue was down 18 percent in Q2 against Q1. Here are the monthly figures and my client list. What are the plausible explanations I should check?" You get a list of hypotheses to verify against your own knowledge, which is faster than staring at the chart.

Translating the jargon. When the accountant's pre-meeting checklist asks for something you do not recognize, asking a model for a plain explanation before the meeting is better than nodding through it during.

Drafting the summary. Give it your revenue by month, your main expense categories, and the notable events, and ask for two paragraphs a professional could read in a minute. Then correct it, because it will get emphasis wrong.

Where it does not help, and this matters

Do not use AI to decide tax treatment. Not whether something is deductible, not how to classify an asset, not whether a structure change makes sense. Three reasons, in order of how much they will cost you.

Rules are jurisdiction-specific and change annually, and a model's sense of the current rules is unreliable in exactly the cases where it matters. Your situation has details that determine the answer and that the model does not have. And you, not the model, sign the return.

The same caution applies to feeding client-identifying financial records into a general consumer chat tool. Check what your provider retains before pasting anything with names attached; our note on sharing customer data with AI tools covers what to look for. Anonymizing payees before analysis costs you one find-and-replace.

What this is worth

Realistically: an hour or two of your accountant's time, fewer follow-up rounds, and a meeting spent on decisions rather than archaeology. If you are billed hourly, that is money. If you are on a fixed fee, it is goodwill and a faster turnaround, and it makes you the client whose file is easy.

It also surfaces problems while there is still time to do something about them. Finding out in September that your record-keeping has a hole in it is annoying. Finding out in January is expensive.

This sits alongside the general question of how far AI can go in replacing bookkeeping work, where the short answer is that it does the sorting and not the judgment. The same split applies here. More on the wider set in our guide to AI for small business, and if cash flow is the pressing issue, chasing late invoices with AI is the adjacent job.

FAQ

Can AI do my taxes instead of an accountant?

It can prepare and organize, and it should not decide treatment. Rules are jurisdiction-specific, change annually, and depend on details of your situation a model does not have. You are the one who signs.

Is it safe to paste my transactions into a chat tool?

Check your provider's data retention settings first, and strip client names and account numbers before pasting. Anonymized amounts and dates are usually enough for the sorting work.

What should I hand my accountant?

A two-paragraph summary of the year, an annotated list of anything unusual, your own questions written down, and the documents named clearly in one folder.

How far ahead should I do this?

Far enough that finding a gap in your records is fixable. A few weeks before the appointment is comfortable; the night before turns it into a list of things you cannot resolve in time.