Prompting Copilot for Accounting Work
Reconciliations, exception handling, and making sense of large data. Sixty minutes, two live demos.
Copilot is not the reconciliation engine.
The tools that find differences and the tool that explains them are not the same tool. Everything else on this page follows from that split.
- Power Query merges
- INDEX/MATCH
- SUMIFS
- Pivot tables
- Conditional formatting
- Classifying an exception list: timing, coding, accrual, credit
- Searching email and Teams for why an item is open
- Writing the matching logic you then run yourself
- Drafting the narrative and the follow-up to the item owner
- Reviewing a finished rec for what is unexplained or unsupported
Never ask Copilot to be the matching engine. "It found 12 unmatched items when there were 15" is an audit finding, not a productivity win.
Structure beats cleverness: R-STAR-QC
You will not use all seven every time. Role, Task, Result, and Constraints carry most of the weight.
- R
- Role
- Who should it act as? A staff accountant reviewing a sales tax rec.
- S
- Situation
- What is the context? Month-end close, period ending 7/31, two extracts.
- T
- Task
- What is the single deliverable? Categorize these 6 variances.
- A
- Action
- What steps, in what order? Group by state, then by root cause.
- R
- Result
- What format? A table with columns State, Amount, Category, Next step.
- Q
- Question
- What should it ask you? List anything you need from me to be certain.
- C
- Constraints
- What are the rules? Do not estimate. Flag anything you cannot source.
Act as [role]. Here is [context]. Do [one task]. Return [exact format]. Do not [the thing that would ruin it].
Source: Adebayo, Akpan & Dell, "Prompt Engineering for Accountants: Automating Financial Tasks with AI," CPA Practice Advisor, September 2025.
The five prompts from the live demos.
Every prompt ends with a boundary, and that sentence is the accuracy control, not politeness. Copy them, then change the period, the account, and the categories to match your own work.
You are a staff accountant reviewing a sales tax reconciliation for the period ending 7/31/2026. Below are the state-level variances I calculated in Excel from two extracts: the GL sales tax payable detail and the tax engine export. For each variance, assign one root-cause category from this list: timing, coding error, missing accrual, unrecorded credit, rate error, jurisdiction mapping. Return a table with columns: State, Variance, Most likely category, Second possibility, What I should pull to confirm. Do not calculate or invent amounts. Use only the variances I pasted. If a variance could fit more than one category, say so explicitly.
Search my email, Teams messages, and files from the last 90 days for anything about Minnesota sales tax, marketplace facilitator rules, or the Kestrel Nutrition account. Summarize what you find in three bullets, name who said it and when, and link each source. If you find nothing relevant, say so rather than guessing.
Below is corporate card spend by category by month for May, June, and July 2026. Identify the three categories with the largest change and state the direction and dollar amount of each. Then tell me which changes look like normal seasonality versus something worth asking about, and explain your reasoning. Use only the figures I pasted.
Here is spend by cardholder by month. Flag any cardholder whose monthly spend in any month is more than double their own average for the other two months. Also flag any category that appears for only one cardholder. For each flag, give the cardholder, the month, the amount, and one sentence on what I should check. Do not flag anything that does not meet those two definitions.
Draft an email to the Controller summarizing the four verified findings below. Under 200 words. Professional and factual, not alarmed. Lead with the total dollar impact, then the findings as a short list, then one clear ask. Do not add findings that are not in my list.
Eight ways people get burned.
None of these are exotic. Every one of them shows up in the first week of using the tool on real work.
01 Asking it to do the math
Treating Copilot like a calculator over a large range.
02 Leaving the format open
"Summarize this" returns whatever shape it feels like.
03 Accepting the first answer
The first draft is a starting point, not a deliverable.
04 Dumping in a whole workbook
Context gets truncated and it quietly drops rows.
05 Asking a leading question
"Confirm this variance is timing" gets you agreement, not analysis.
06 Starting a new chat every time
You re-explain context and lose the thread's accumulated setup.
07 Trusting a cited source blindly
It can attach a real-looking citation to a claim the source never made.
08 Pasting confidential data outside Copilot
Your organization's data protection agreement covers Microsoft 365 Copilot. It does not cover a personal ChatGPT account.
- Every number that reaches a workpaper ties back to a control total before anyone signs it. Copilot drafts. The accountant ties out and signs.
- Every categorized exception needs a source document, not just a plausible story.
- If Copilot names a dollar amount you did not give it, that is a red flag, not a finding.
- Note in the workpaper where AI assisted. Reviewers should know what to scrutinize.
Company financial data stays in M365 Copilot.
Everything from the session.
The prompt library above is the whole of it in text form. These are the files.