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Retrospective Template

AI Product Discovery Review

Separate user evidence from AI-generated assumptions and choose the next discovery experiment.

4 columns
AI at Work
1 min read
product management, AI product discovery, user research

The AI Product Discovery Review helps product teams move faster without confusing generated synthesis with customer evidence. It makes the source of every product belief visible before the team commits to a roadmap decision.

Separate evidence from plausible output

Start with direct user evidence: interviews, behavior, support conversations, sales objections, or product data. Put AI-generated themes, summaries, and hypotheses in a separate column. Then identify the unknowns and risks that matter most and choose the next validation step.

This board is useful because AI can accelerate discovery work while also making weak assumptions sound unusually complete. The structure keeps speed and epistemic discipline together.

How to facilitate

  1. 1
    Attach a source. Every evidence card should point to a customer, observation, or metric.
  2. 2
    Label synthetic insight. AI output is a hypothesis until a user or behavior supports it.
  3. 3
    Prioritize the riskiest unknown. Validate what could most change the product decision.
  4. 4
    Choose a cheap test. Prefer an interview, prototype, concierge test, or data check before a build.

When to use this template

Use this review after AI-assisted interview synthesis, market research, opportunity mapping, feature ideation, or before moving an AI-generated concept into delivery.

Template Columns

Column structure and prompts for your retrospective

User evidence

Items for User evidence

AI-generated assumptions

Items for AI-generated assumptions

Unknowns and risks

Items for Unknowns and risks

Next validation

Items for Next validation