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AI Sensitivity Analysis Narrative: Turn a What-If Table Into an Exec One-Pager

Take a price / volume / cost what-if table and let AI write the one-pager that explains which lever matters most.

Published: Author: AI Productivity Guide Team 🌐 查看中文版本

The task

You ran a sensitivity table — price ±10%, volume ±10%, COGS ±5% — and need to turn it into a one-page narrative your CEO can actually read. The numbers are right; the story is missing.

When this is the right job for AI

  • The math is already done in Excel / Sheets / Looker — you only need narrative.
  • You can hand AI a clean table (no raw transactions, no PII).
  • The audience is non-finance and needs “what to do”, not the matrix itself.

If you are still building the model, do that first. AI cannot recover a broken sensitivity table.

What to feed the AI

  • Base-case revenue, gross margin, EBIT
  • The sensitivity table itself (rows = price scenarios, cols = volume scenarios — or vice versa)
  • The 1-2 assumptions you suspect are most fragile (e.g. “Q4 churn assumed at 4%”)
  • Audience: “CFO + 3 non-finance VPs”

Copy-ready prompt

You are writing a one-page sensitivity-analysis narrative for non-finance executives.

Base case: revenue $42M, GM 58%, EBIT $6.1M.
Most fragile assumptions: {paste 2}
Sensitivity table:
{paste table — price scenarios x volume scenarios -> EBIT}

Write:
1. One-sentence headline: which lever moves EBIT the most.
2. Three bullets: best case, worst case, most likely case — each with the lever that drives it.
3. One "watch this" — the assumption I should pressure-test before locking the plan.
4. One recommendation in plain English: which lever to defend in the plan.

Tone: direct. No "it depends." 300 words max.

Sample output structure

Headline: EBIT swings ±$2.4M on price, only ±$0.9M on volume — pricing is the lever to defend.

  • Best case ($46M rev / GM 60%): if we hold list price and volume comes in at +5%, EBIT lands at $7.8M.
  • Worst case ($38M rev / GM 55%): a 5% price concession plus flat volume drops EBIT to $3.7M.
  • Most likely ($42M rev / GM 58%): base case ships if we don’t discount.

Watch this: the Q4 churn = 4% assumption. At 6% churn the worst case becomes the most likely.

Recommendation: defend list price. Volume incentives are cheaper than discounts if growth slips.

How to refine

  • Output too generic? Paste 1-2 prior-quarter narratives from the same company so AI matches voice.
  • AI hedging (“could either way”)? Replace prompt line 1 with: “Pick the single most important lever; do not list two.”
  • Wrong audience tone? Add: “The CFO already knows the math — do not re-explain sensitivity. Focus on the call.”

Common mistakes

  • Pasting raw transactional data instead of the already-built sensitivity grid.
  • Letting AI invent numbers between the cells you provided — always tell it “do not interpolate, use only my values.”
  • Asking for “what if customers churn at 10%?” without giving the row for that scenario. AI will hallucinate.
  • Skipping the recommendation. A sensitivity report without a call is data, not analysis.

Practical depth notes

For AI Sensitivity Analysis Narrative: Turn a What-If Table Into an Exec One-Pager, the difference between a usable AI result and a generic one is the input packet. Give the model the audience, the current draft or raw material, the desired format, the decision you need to make, and two examples of what good and bad output look like. Ask it to preserve facts first, then improve structure or wording second.

After the first response, do a separate review pass. Look for missing constraints, invented details, weak calls to action, and language that sounds plausible but does not match the real situation. The best final output should be easy to use immediately: clear owner, clear next step, and no hidden assumption that someone else has to untangle. A stronger version of this workflow also defines the handoff. Decide who will use the output, what they should do next, and what information would make them reject it. If the deliverable is copy, test whether it has a single clear action. If it is analysis, test whether it separates observation from recommendation. If it is planning, test whether dates, owners, and tradeoffs are explicit enough for someone else to execute.

FAQ

  • Can I share the table with ChatGPT? Use ChatGPT Enterprise / Claude for Work / a corp-approved tenant for any internal numbers. Strip customer names and dollar absolutes first if your policy is strict — ratios are usually fine.
  • What if my sensitivity is 3-dimensional (price × volume × COGS)? Collapse to two 2D tables (price × volume at base COGS, then price × COGS at base volume). AI handles 2D much better than 3D.
  • Does this work for non-P&L sensitivities (e.g. churn × ARPU)? Yes — same template. Replace “EBIT” with the target metric.

Tags: #AI writing #Finance #Sensitivity analysis #Business analysis #Executive update

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