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SCR Storytelling with Data with AI

AI is a strong partner for shaping findings into a Situation–Complication–Resolution story — structuring the arc, cutting the data-dump, pressure-testing the logic. What it can't supply is the judgment of what the data means and which decision to advocate. And it will confidently narrate a conclusion the data doesn't support, so the analysis must be sound first.

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1How to use AI for this

A step-by-step way to work

1

Bring sound analysis, not raw data

SCR is for communicating a finding you've already validated — don't ask AI to find the story in unanalysed numbers.

2

Lay out S–C–R

Give AI your findings and ask it to arrange them as Situation (the context everyone agrees on), Complication (what changed/what's wrong), Resolution (your recommendation).

3

Cut to the story

Ask it to strip a dense draft to the few points that drive the decision.

4

Pressure-test the logic

'Does my resolution actually follow from my complication?' — AI is decent at catching logical gaps.

5

Own the recommendation

The resolution is a decision you're advocating; AI structures it, you stand behind it.

2Worked examples

Real prompts, and what good output looks like

Structuring findings as SCR
Your prompt

Turn these findings into an SCR story for a leadership review: trial-to-paid conversion is 12% (down from 18% last quarter); the drop is concentrated in self-serve signups; guided-onboarding signups still convert at 19%. I want to recommend adding guided onboarding to the self-serve flow.

What good output looks like

Situation: We rely on trials to drive new revenue; last quarter trials converted to paid at 18%.

Complication: This quarter conversion fell to 12%, and the drop is entirely in self-serve signups — guided-onboarding signups still convert at 19%.

Resolution: Bring guided onboarding into the self-serve flow; the 7-point gap suggests recovering much of the lost conversion without adding headcount.

The logic holds IF the guided vs self-serve difference is causal, not just a difference in who chooses each path — worth a caveat or a test.

Excellent catch at the end: AI flags the causation-vs-selection risk in your own argument. That's the logic pressure-test working — address it before you present.
3Copy-paste template

A prompt you can reuse

Fill in the highlighted parts and paste it into your AI tool of choice. Edit the output — it's a starting point, not a finished answer.

Reusable prompt
Help me turn validated findings into a Situation–Complication–Resolution data story. Don't invent data; do challenge my logic.

My findings (already analysed):
[paste the key numbers/findings]
The decision I want to advocate:
[your recommendation]
Audience:
[who's in the room]

Please:
1. Structure it as S–C–R — Situation everyone agrees on, Complication that creates tension, Resolution as my recommendation.
2. Cut anything that doesn't advance the decision.
3. Pressure-test: does the Resolution actually follow from the Complication? Flag any leap (e.g. correlation treated as cause).
4. Suggest where I need a caveat or a confirming test before I present.
4Common pitfalls

What AI gets wrong here

Narrating an unsupported conclusion

AI will build a clean story toward whatever resolution you give it, even if the data doesn't support it.

Do this instead: Validate the analysis first; ask AI to flag where the story outruns the evidence.

Persuasion over honesty

A slick SCR can make a weak case sound strong.

Do this instead: Use the structure to clarify a true finding, not to oversell one; keep the caveats in.

Losing the caveat in the polish

Tightening the story can quietly drop the 'this is correlational' honesty.

Do this instead: Keep the key limitation visible in the resolution; a story that hides its risk will unravel under questioning.
The judgment that stays yours

AI structures a data story well and — valuably — will catch logical leaps in your own argument if you ask it to. But it can't tell you what the data means or which decision is right; it will narrate whatever conclusion you hand it. The interpretation and the recommendation are yours, and the analysis behind them must be sound before you let AI make it sing.