AI for User Research & Insights
You already ship insight reports at your company. This module is about 10x-ing that process — synthesizing interviews at scale, auto-tagging feedback, building living insight repositories, and extracting signal from noise.
- Why synthesis, not collection, is the research bottleneck
- How to auto-tag feedback with a custom taxonomy
- The architecture of a living AI-powered insight repository
- Synthesise 50 interview transcripts into structured themes in under 10 minutes
- Build a monthly NPS analysis that runs itself
- Map qualitative feedback to quantifiable signal
Step 1 of 5 · Read the lesson
The real bottleneck
It's not collection. It's synthesis. AI is uniquely good at compressing 50 transcripts into a JTBD map in minutes.
Auto-tagging feedback
Define a taxonomy once. Have AI classify every new piece of feedback into it with a confidence score. Suddenly qualitative data becomes a quantifiable signal you can chart.
Living insight repository
Pipe tagged feedback → weekly digest → Notion database. Insights compound. Patterns surface that no single human would notice.
Reuse the architecture you already have
Your Contentful translation agent is the same shape: input → AI → structured output → destination. Insight automation is a sibling, not a new beast.
Building a living insights repository
The real unlock from AI-powered research isn't individual syntheses — it's a compounding knowledge base. Every month: AI processes new feedback → structured summaries flow into a Notion database → any PM can query "what do users say about onboarding friction?" and get an instant synthesis of 6 months of data.
The architecture: a Notion database with properties for Theme, Source, Date, Sentiment, Representative Quote. AI populates it. You query it. New team members can get up to speed on user context in an hour instead of a month.
The research bottleneck: synthesis, not collection
Most teams collect enough data. The bottleneck is turning it into something actionable. AI doesn't replace your judgment about what matters — it removes the labour of organising what exists so you can apply that judgment faster. The PM who synthesises research 10x faster doesn't just save time. They run more research cycles, which means better product decisions, compounding over time.