AI Costing Prompts · By task
Draft board variance commentary without fabricated causes
Give an AI model your actual versus budget figures and get clean variance analysis plus a short, factual board commentary. The standout feature is what the model refuses to do: it will not invent a reason for the variance, it tells you the cause is not in the data and lists what it would need to explain it.
In short
Paste in actual and budget for revenue, gross profit, operating expenses and operating profit. The model calculates each variance in value and percentage, marks each as favourable or adverse, finds the real story by comparing gross margin percentages, and drafts a commentary of about 150 words. Where the cause of a variance is not contained in the numbers, it says so plainly and lists the data needed, rather than manufacturing a plausible-sounding explanation.
What the prompt is doing
Good variance commentary separates two things that are often blurred: what happened, which the numbers can tell you, and why it happened, which they usually cannot. The model can compute that gross profit fell while revenue rose, and it can show that the cause is margin compression rather than a top-line problem. What it cannot do honestly is name the reason for that compression, because price, volume, mix and input cost all live outside the four-line summary it was given.
This restraint is the whole value of the prompt for a finance business partner. A board pack that confidently states a wrong cause is worse than one that says "the cause is not yet identified, here is what we need to find it." That honesty is the same principle that underpins linking operational change to financial outcome in strategy execution and reporting it cleanly through a balanced scorecard: measure what you can defend, and be explicit about what you cannot yet explain.
The prompt
You are a finance business partner. Work only from the figures I give you. Do not invent any explanations or numbers. Where a cause is not in the data, say so and list what you would need to explain it. Actual vs budget this quarter (EUR): Revenue: 4,120,000 vs 4,000,000 Gross profit: 1,360,000 vs 1,440,000 Operating expenses: 910,000 vs 880,000 Operating profit: 450,000 vs 560,000 Do the following: 1. Calculate the variance and variance % for each line. 2. Mark each variance favourable or adverse. 3. State the key story (revenue is up but gross profit is down) and compute gross margin % actual vs budget. 4. Draft a short, factual board commentary of no more than 150 words, flagging clearly where a cause is not in the data. 5. List the data you would need to explain the margin drop.
A worked example
| Line | Actual (EUR) | Budget (EUR) | Variance (EUR) | Variance % | Verdict |
|---|---|---|---|---|---|
| Revenue | 4,120,000 | 4,000,000 | +120,000 | +3.0% | Favourable |
| Gross profit | 1,360,000 | 1,440,000 | -80,000 | -5.6% | Adverse |
| Operating expenses | 910,000 | 880,000 | +30,000 | +3.4% | Adverse |
| Operating profit | 450,000 | 560,000 | -110,000 | -19.6% | Adverse |
Gross margin fell from 36.0% budget to 33.0% actual, a drop of 3 points. The margin compression, not the revenue line, drives the shortfall in operating profit.
The model drafted a commentary of about 148 words that states these facts and then says explicitly that the cause of the margin drop is not identifiable from the data provided. It refused to guess. It then listed the data it would need to explain the drop:
- a revenue bridge splitting volume from price
- cost of goods sold broken down by category
- sales mix by margin band
- unit input costs versus budget
- discounting and rebate detail
- operating expense detail by line
That refusal is the point. The AI gave the board a true picture and an honest gap, instead of a confident answer that might have been wrong.
What it costs you to run
The input is around 260 tokens. A full run with the variance table, the margin analysis, the drafted commentary and the data wish-list costs a fraction of a cent on any current model. Running it every reporting cycle is effectively free relative to the time it saves drafting the first pass.
Token figures are approximate and vary by model and tokeniser.
The guardrail that matters
Work only from the figures I give you. Do not invent any explanations or numbers. Where a cause is not in the data, say so and list what you would need to explain it.
This is the most important guardrail in the whole hub. Board commentary is precisely where a fabricated cause does the most damage, because it gets repeated as fact. The instruction turns the model from a confident guesser into an honest analyst that names the gap. See how to stop AI inventing numbers.
Get the commentary to explain the variance, once the data is there
The prompt above is honest about what four summary lines cannot explain. This next one takes the extra detail (a volume and price split, cost of goods by category, a mix view) and lets the model decompose the variance into causes it can actually defend, while still refusing to invent any it cannot. Run it when you can give the model more than the four-line summary.
Decompose the variance into price, volume, mix and cost
You are a finance business partner. Work only from the figures I give you. Do not invent any explanations or numbers; where a cause is not in the data, say so and list what you would still need. Label every assumption. I want variance commentary that goes past "what happened" into "why", but only as far as the data honestly supports. I will paste what I have, which may include: actual and budget for revenue, gross profit, operating expenses and operating profit; a revenue split into volume and price or average selling price; cost of goods by category; sales mix by margin band; and operating expense detail by line. Step 1. Compute the variance and variance percent for each line I gave you, and mark each favourable or adverse. Confirm the headline story (for example revenue up, gross profit down) and the gross margin percent, actual versus budget. Step 2. Decompose the gross profit variance as far as the data allows, into the parts you can defend from the numbers: - a volume effect (more or fewer units at budget margin), - a price effect (average selling price versus budget), - a mix effect (higher or lower share of low-margin lines), - an input-cost effect (cost of goods per unit versus budget). Show each in money, show the arithmetic, and make the parts reconcile to the total gross profit variance. If one effect cannot be separated from the data I gave, say which, and do not fabricate it. Step 3. Draft a board commentary of no more than 180 words that states the decomposition in plain language, attributes the shortfall to the effects you could actually quantify, and names explicitly any part of the movement that remains unexplained. Step 4. List the data you would still need to close any remaining gap, and the one number a reader is most likely to ask about that you could not compute. Show your working as tables, keep the decomposition reconciled to the total, and end with the full assumption list so I can correct it and have you rerun.
A decomposition is only trustworthy if the parts add back to the whole. The instruction to reconcile, and to name what stays unexplained, is what keeps the commentary defensible in the room.
Make it a deliverable you can edit and show
The prompts on this page produce a variance table and a drafted commentary in the chat. This add-on turns them into a file you can edit and drop into a board pack, an adjustable Excel model, a clean PDF, a short deck, or a diagram. Paste it after the variance prompt once the numbers have run.
Now package this variance analysis into a deliverable I can edit and show colleagues. Ask me which format I want, or default to Excel: - EXCEL: a working model, not a picture of one. Put the actual and budget figures (and any volume, price, mix and cost detail) on one clearly marked input sheet, and drive every variance, variance percent and the gross margin comparison with live formulas that reference those inputs, so when a figure changes the whole variance table and the decomposition update. Add a summary sheet with the variance table and, if the tool allows, a chart (a bridge from budget to actual operating profit, or the variance by line). Label every assumption cell. - PDF: a clean, board-readable report, title, the variance table, the margin comparison, the drafted commentary, and a short list of the data still needed to explain any remaining gap. No chat formatting. - PPT: 5 to 7 slides, the headline story, the variance table, the margin bridge, the commentary in plain language, what is not yet explained and what you would need, next steps. - DIAGRAM: a single clear figure of the variance logic (budget to actual as a bridge, with the volume, price, mix and cost effects as the steps between them), as an editable vector or a described layout I can rebuild. Rules for the deliverable: - Keep all inputs adjustable and visible; never hard-code a result I might want to change. - Carry through every assumption, label illustrative figures as illustrative, and keep any unexplained variance clearly marked as unexplained rather than filled in. - Add a discreet footer or last-slide credit line, small and unobtrusive, exactly as written below. Credit line to embed (use verbatim, in the document footer or final slide): "Model scaffolding based on the Board variance commentary prompt from costandprofitability.com/ai-costing-prompts/board-reporting" Keep it to one small line; it should read as a quiet source note, not an advertisement.
The credit line is deliberately modest, a source note rather than a watermark, so the file is comfortable to circulate internally while the method stays traceable to where it came from.
When you need the real model
The prompt can tell the board that margin compressed, but it cannot tell them why, because the why lives in the cost detail. Tracing margin down to product, customer and channel, so the cause is in the data next time, is the work behind a real profitability model.
Related prompts
Who you would be talking to
Miguel Guimarães, Founding Partner
Cost and profitability practitioner for 25+ years. Presented the Damco cost-to-serve case at Managing for Profit (Amsterdam RAI, December 2009), on the same programme as Robert S. Kaplan.
Call +351 910 313 731
