Sixty minutes of hands-on synthesis. We took a realistic stack of customer data — 10 discovery interviews, 75 support tickets, 120 NPS responses, and a company brief — and used Claude to turn it into a framework ready for a VP Product conversation. The point wasn't to learn about AI. The point was to make a real decision with it.
Same flow, every time. Steps 1–3 are identical regardless of framework. Steps 4–5 diverge based on which lens you pick.
Anchor Claude to your business goal, your data sources, and the specific question you're answering — before sharing a single data point. Generic input produces generic output.
Start with 4–5 interview transcripts, then add the support ticket and NPS CSVs. Always orient Claude on what each column means.
The highest-value moment in the session. Ask what only shows up cross-source, where segments disagree, and whose voice is missing — and push back when Claude returns patterns dressed up as surprises.
Default: Opportunity Solution Tree. Alternates: Jobs-to-Be-Done, or the Designer Track (How-Might-We + journey map). Same data, different lens.
Argue against your own #1 recommendation. Then reformat as a 1-page exec summary: strategic question, top 3 recommendations ranked by evidence, key risks, what to validate next.
Everything we shared in the room, plus the sample dataset so you can run the workflow again on your own.
The full presentation — every slide, every prompt, every framework.
Download slidesThe full 6-step prompt pack — copy-paste-ready for your next round of discovery data.
Open guideThe whole workshop on a single page. The traps, the corrective prompts, the principles.
View one-pagerLaunchpad case: 10 interviews, 75 tickets, 120 NPS responses, company brief, prompt pack.
Download (.zip)Excellent instructor. Top notch.— AI Power Hour attendee, June 2026
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