The typing-and-translating parts of accounting are now fast. Variance commentary, engagement letters, client explanations, Excel formulas, a summary of a new ASU or a lease: a chat assistant gives you a credible first draft in a minute. The judgment stays with you: is the revenue recognized correctly, is the reconciliation actually reconciled.
These tools are language models, not calculators. They will misfoot a column, quote last year's standard deduction, and cite a Treasury regulation that does not exist, all in the same confident tone. Treat every output as staff work that needs review before it reaches a client, a workpaper, or a return.
The confidentiality rules did not change either. Client names, EINs, tax return data, and payroll files do not belong in a consumer chat tool unless your firm has approved the tool and its data terms. Most of what follows works on de-identified data.
Quick wins this week
- Paste one department's budget vs actual columns (no client name needed) and ask for draft explanations of every line over your threshold. Edit, then send.
- Describe a spreadsheet in words (column A is invoice date, column F is amount, totals by month and customer) and ask for the SUMIFS. Test on ten rows first.
- Upload a new ASU, a state nexus notice, or a 20-page lease to NotebookLM and ask questions answered only from that document, with page references.
- Turn your engagement letter template into a fill-in prompt: give entity type, services, and fee structure, get a draft for partner review.
- Ask for a plain-English email explaining why a client owes estimated payments or what a K-1 line means. You have said it a hundred times; let the tool write the hundred-and-first.
What AI can do for accountants, task by task
Month-end close and bank reconciliations
Export the bank feed and GL detail for one account, strip customer names, and upload both to a tool that runs code (ChatGPT's data analysis, Julius, or Copilot in Excel). Ask it to match on amount and date within three days and list exceptions. Confirm the exceptions foot to the unreconciled difference.
Variance and flux commentary
Give the comparative numbers, account descriptions, and what you already know (a March price increase, a one-time accrual). Ask for two-sentence explanations per line in your controller's format. Read each against the ledger: the model invents a plausible driver for any variance it cannot explain.
Tax research and client questions
Ask for the framework first: which code section, which form, the general rule and common exceptions. Then verify every threshold, date, and citation in the IRS publication or your research platform. Tax figures change yearly and models have a training cutoff, so treat any dollar limit as a placeholder.
Excel formulas, Power Query, and cleanup
Describe your columns, the result you want, and your Excel version. Ask for the formula with one line per argument; for Power Query ask for the M code or the click path. When it fails, paste the exact error back. Test on a range you can check by hand.
Technical accounting memos
Give the fact pattern (parties, term, payments, options, dates) and ask for a memo skeleton: issue, facts, analysis under the five-step model in ASC 606 or the classification tests in ASC 842, conclusion, open questions. Then open the codification and check every paragraph cited. Paragraph numbers are a frequent hallucination.
Reading leases, loan agreements, and prior-year workpapers
Upload the document to NotebookLM or Claude and ask targeted questions with page references: covenant ratios, renewal options, escalation clauses, related-party terms. Then open the cited page and read the clause yourself. The summary tells you where to look, not what it says.
Prompts for accountants
Replace the bracketed placeholders, paste into any chat assistant, and iterate on the result.
Draft flux commentary from a budget vs actual table
You are a senior accountant preparing month-end variance commentary for a controller. Below is a budget vs actual table for [MONTH] for [ENTITY OR DEPARTMENT], amounts in [CURRENCY AND UNITS]. Context: [KNOWN DRIVERS, E.G. MARCH PRICE INCREASE, ONE-TIME ACCRUAL]. For every line with a variance over [THRESHOLD], write two sentences: direction and amount, then the most likely driver based only on the context above. With no basis for a driver, write DRIVER UNKNOWN: needs review. Do not guess. Output a table: Line, Variance, Commentary. [PASTE THE TABLE]
Tip: The DRIVER UNKNOWN flags are your review list.
Explain a tax concept to a client in plain English
You are a CPA writing to a small-business client with no accounting background. Explain [TAX CONCEPT, E.G. WHY ESTIMATED PAYMENTS ARE REQUIRED] in an email under 180 words. Use one concrete example with round numbers. Do not cite code sections. Do not state dollar thresholds, rates, or deadlines; leave a placeholder such as [DEADLINE] so I can fill it from current guidance. End with one clear next step. Tone: calm, direct, no jargon. Client context: [CLIENT'S SITUATION IN ONE OR TWO SENTENCES]
Tip: Forcing placeholders for figures keeps stale numbers out of client emails.
Build or fix an Excel formula
You are an Excel expert. I use [EXCEL VERSION]. My sheet has these columns: [LIST COLUMN LETTERS AND CONTENTS]. I need: [DESCRIBE THE RESULT, E.G. TOTAL BY CUSTOMER FOR INVOICES THIS QUARTER, IGNORING VOIDED ROWS]. Give me the formula, one line per argument, the two most likely ways it could return wrong results with my data, and a three-row test. If my description is ambiguous, ask me a question before answering.
Tip: Paste the exact error text back when it fails; the second answer is usually right.
Outline a technical accounting memo
You are a technical accounting manager. Draft a memo outline for this fact pattern under [STANDARD, E.G. ASC 606 OR ASC 842]: [DESCRIBE THE CONTRACT: PARTIES, TERM, PAYMENTS, OPTIONS, KEY DATES]. Structure: Purpose, Background, Issues, Analysis (apply each required step or test in order), Conclusion, Open Questions. Wherever you reference a codification paragraph, mark it [VERIFY]. List facts still needed for a firm conclusion. Do not invent contract terms.
Tip: Every [VERIFY] tag is a real to-do before the memo goes in the file.
Triage reconciliation exceptions
You are a staff accountant working a bank reconciliation. I will paste bank transactions and ledger entries for [ACCOUNT NAME] for [PERIOD]. Match on amount and a date within [NUMBER] days; one bank item may match several ledger entries that sum to it. Use code and show your work. Return: matched count and total, unmatched bank items, unmatched ledger items, duplicates, and whether the unmatched totals explain a difference of [RECONCILING DIFFERENCE]. Bank: [PASTE BANK LIST] Ledger: [PASTE LEDGER LIST]
Tip: Use a tool that executes code; plain chat miscounts anything longer than a screen.
Want a prompt for something else? Use the Prompt Builder or browse data and spreadsheets prompts, finance and analysis prompts.
Skills to build
Describing data precisely
Why: Most bad output starts with an underspecified input: which columns, what units, which period, what a correct answer looks like.
How: Before pasting anything, write three lines: what the data is, what you want back, and how you will check it.
Verification as a reflex
Why: The output looks finished, which is exactly why errors slip through. A wrong threshold or invented citation ends up on a return.
How: Fixed rule: every figure re-footed, every citation opened, every date checked against this year's calendar. Still faster than drafting from scratch.
Using code-execution tools for anything numeric
Why: A model predicting text approximates arithmetic; one that writes and runs Python computes it. Know which mode you are in before trusting a total.
How: Find the data-analysis features in ChatGPT, Claude, Julius, and Copilot in Excel. Practice on a bank export you already reconciled by hand.
Saved prompts for recurring work
Why: You do the same dozen things every month. A saved prompt with placeholders makes each a two-minute task with consistent quality.
How: Save your five best prompts as a Claude Project, custom GPT, or Gemini Gem, with your firm's style rules and a verification checklist in the instructions.
Reading the standard, not the summary
Why: AI summaries of the codification, IRS guidance, and state rules are convenient and occasionally wrong in ways only the primary text reveals.
How: Use NotebookLM or Claude with the actual document uploaded, ask for paragraph references, and open each one before relying on it.
Tools worth knowing
ChatGPT
The general-purpose AI assistant most of your coworkers already use.
Claude
A careful writing and analysis assistant that shines on long documents.
Microsoft Copilot
AI inside Word, Excel, Outlook, and Teams, with your company's data protections.
Julius AI
Chat with your spreadsheets and data files and get charts, stats, and answers back
NotebookLM
A research notebook that only answers from the sources you give it, with citations.
Zapier
Connect thousands of apps with no-code workflows, now with AI steps, agents, and chatbots
Cautions for accountants
Tax return information is protected by IRC Section 7216 and the AICPA confidentiality rule, and tax preparers fall under the FTC Safeguards Rule, so your written information security plan should name the AI tools allowed. Never paste client identifying data into a consumer tool your firm has not approved. De-identify first: Client A, no EINs, no account numbers.
If your work feeds a public company's financial statements, an AI-drafted entry, reconciliation, or disclosure is subject to the same preparer and reviewer controls, and the reviewer needs to know AI was involved. Note the tool, the input, and the review in the workpaper. Auditors are already asking.
A chat model miscounts rows, misfoots totals, and quotes thresholds from training data even though limits and rates change every year. It also invents code sections, revenue rulings, and ASC paragraph numbers that sound right. Use code-execution tools for arithmetic and open every source before a citation reaches a client.
In public accounting, running an attest client's data through a tool your firm's quality management system has not approved creates documentation problems and, depending on the tool's terms, can raise independence questions. Route new tools through the firm's approval process first.
Your 30-day plan
- Week 1: Read your firm's AI policy, or draft a one-page version. Pick one approved tool and build a de-identified practice set: one bank export, one trial balance.
- Week 1: Do three quick wins on non-confidential work: an Excel formula, a client explainer email, a summary of a document you had to read anyway.
- Week 2: Run last month's flux commentary through the variance prompt, compare with what you wrote by hand, and list where the model was wrong.
- Week 2: Reconcile one account with a code-execution tool alongside your normal process. Time both and note which exceptions each caught.
- Week 3: Save your five most useful prompts as a Project, custom GPT, or Gem with your style rules and a verification checklist.
- Week 4: Standardize one recurring task (engagement letters, invoice intake) with a saved prompt or Zapier flow, and share results with your team, including the errors you caught.
Frequently asked questions
Will AI replace accountants?
Is it safe to put client data into ChatGPT?
Can ChatGPT do bookkeeping?
Do I have to tell clients I use AI?
Terms used on this page
Related roles
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