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Working with AI SYNTHESIS · The Insight Statement Framework

The Insight Statement Framework with AI

AI helps turn observations into well-formed insight statements and — usefully — helps you check whether you've written a genuine insight or just restated the data. Recognising what's truly insightful still takes domain judgment AI lacks, but it's a sharp partner for phrasing and pressure-testing.

← Back to the The Insight Statement Framework tool
1How to use AI for this

A step-by-step way to work

1

Bring the observation and the tension

Give AI the finding and any surprise or contradiction in it — insights live in the tension, not the summary.

2

Ask for insight statements, not summaries

Request statements that capture the non-obvious truth beneath the data, in the framework's form.

3

Test for depth

Ask 'is this an insight or just a restatement of what users said?' — AI is decent at spotting shallow output.

4

Generate reframings

Get several framings of the same insight; the best one tends to suggest solutions.

5

Decide what's insightful

You judge which statements are genuinely non-obvious and change what you'd build.

2Worked examples

Real prompts, and what good output looks like

Insight vs. restatement
Your prompt

Observation: 'Users abandon the upgrade page after viewing it for a long time.' Is this an insight, and if not, make it one.

What good output looks like

Not yet — it restates a behaviour without explaining the tension. It tells you what, not why-it-matters.

Insight: 'Users spend a long time on the upgrade page not because they're convinced, but because they can't tell which plan fits them — deliberation reads as interest but is actually confusion.'

That reframes the problem from 'persuade them to upgrade' to 'help them choose', which points at different solutions.

This is the core move: AI distinguishes a data restatement from an insight and reframes toward action. You still verify the 'why' is true.
Pressure-testing depth
Your prompt

Is this a real insight? 'Mobile users convert less than desktop users.'

What good output looks like

No — it's a metric comparison, not an insight. It states a gap without a non-obvious reason or a 'so what'.

To become an insight it needs the why: what is it about the mobile context or flow that suppresses conversion? Pair it with qualitative evidence before stating it as an insight.

Guards against fake insights: AI flags the 'metric gap masquerading as insight' trap that fills a lot of research readouts.
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 research findings into genuine insight statements — and call out any that are just restated data.

Finding(s) and the tension/surprise in them:
[paste the observation, plus what's surprising or contradictory about it]

Please:
1. For each, tell me if it's a true insight or a restatement of the data, and why.
2. Rewrite the real ones as insight statements that capture the non-obvious truth and imply a 'so what'.
3. Offer 2 alternative framings of the strongest insight.
4. Flag any where the 'why' is assumed and needs evidence before I state it.
4Common pitfalls

What AI gets wrong here

Restatement dressed as insight

AI can produce fluent statements that just rephrase the observation.

Do this instead: Ask it to justify why each statement is non-obvious; reject anything that's just the data in nicer words.

Assumed causes

An insight needs a true 'why', and AI will supply a plausible one whether or not it's evidenced.

Do this instead: Treat any AI-supplied cause as a hypothesis; flag it for validation before acting.

Losing the surprise

AI smooths findings into reasonable-sounding statements, sanding off the surprising edge that makes an insight valuable.

Do this instead: Push it toward the tension or contradiction; the insight is usually in what doesn't fit.
The judgment that stays yours

An insight is a non-obvious, evidenced truth that changes what you build — and judging whether something clears that bar takes domain knowledge and a feel for your users that AI doesn't have. It's a strong partner for phrasing statements and for catching restatement-masquerading-as-insight, but you decide what is genuinely insightful, and you confirm the 'why' is real.