Structuring a case study for AI search and human skim: a checklist

Structuring a case study for AI search and human skim: a checklist

The question: how do you structure a B2B case study so both a skimming buyer and a citing AI model can extract the proof point in seconds?

Generative engines and busy buyers share a behavior: they extract, they don't read linearly. Passage-retrieval research shows self-contained, claim-first paragraphs get pulled and cited; buried context does not.

The structure checklist:
— Lead with the outcome number in the first sentence, with the unit ('cut sales-cycle length by 23 days').
— State the before-state explicitly — a result without a baseline is uncitable.
— Name the mechanism: what specifically caused the change.
— Put one quotable sentence per section that stands alone out of context.
— Use a descriptive subhead, not a clever one ('How we cut onboarding time' beats 'The turning point').
— Include the timeframe; 'in Q3' is more citable than 'eventually'.

Limitations: AI citation behavior is opaque and shifts model-to-model; these are inferences from observed retrieval patterns, not guaranteed mechanics. Treat as directional.

What it means for B2B: a case study optimized for extraction outperforms a beautifully narrated one that hides its proof.

Bottom line: write every paragraph to survive being quoted alone — that serves both the skimmer and the machine.