AI Product Discovery: Complete Course
Product Growth Podcast
AI Empowerment for Product Managers
This chapter discusses how AI can boost the productivity of product managers by automating repetitive tasks, enabling a focus on strategic roles such as discovery and customer engagement. It outlines a structured four-stage framework for the product discovery process, emphasizing the iterative approach and the value of clear communication within teams.
We could talk a million things with Tanguy Crusson but I’m keeping it to what I like the most about his work - Product Discovery.
He shares a rare, behind-the-scenes look at how his team at Jira Product Discovery uncovers real user problems, validates solutions quickly, and avoids wasting time on ideas that won’t land.
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Timestamps:
Preview & Intro — 00:00:00
Has AI Actually Changed Product Discovery? — 00:00:27
Ad: Jira Product Discovery — 00:10:50
Ad: AIPM Certification with OpenAI PM — 00:11:45
Where AI Truly Helps in Discovery (Real Use Cases) — 00:12:32
Watch the Live Demo on YouTube for Full Context — 00:14:34
The Discovery Process (Deep Dive)
Phase 1 – Wonder: How Atlassian Uncovers Real Problems — 00:16:51
Why Tanguy Hasn’t Written a PRD in 5 Years (+ Wild Engineer Story) — 00:21:04
How to Lead Great User Interviews (Tips from a UX Researcher) — 00:25:24
Ad: Vanta Compliance & Security — 00:28:30
Ad: AI Evals for PMs & Engineers — 00:29:34
The Right Way to Structure Discovery Documents — 00:30:34
How Atlassian Actually Uses Tools for Product Discovery (Full Stack Demo) — 00:33:23
Why This System Works Better Than Traditional PM Workflows — 00:41:24
Moving Through Discovery
Phase 2 – Explore: Rapid Prototypes & Real Feedback — 00:41:58
Phase 3 – Make: When the Team Commits to Building — 00:49:04
“Just Ship It” is Bad Advice — Here’s Why — 00:52:24
Can You Trust Feedback from Free Users? — 00:56:00
Phase 4 – Impact: Measuring Real-World Results — 01:07:33
How to Build Real Trust as a Product Manager — 01:14:41
PMs Want to Keep Engineers Busy: Good Strategy or Trap? — 01:17:46
Aakash: “I Wish I Talked to You 10 Years Ago”, Here’s Why — 01:22:46
Closing Reflections & Takeaways — 01:26:33
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Key Takeaways:
1. Discovery isn’t a phase, it’s a system. Atlassian runs product discovery continuously, not just “before development.” It’s embedded across problem finding, prototyping, building, and post-launch.
2. Use video, not documents, to communicate user pain. Instead of writing long research summaries, PMs compile 10-minute reels of real customer interviews. Watching raw emotion builds urgency and alignment.
3. Start with ~10 users — not thousands. Atlassian validates ideas with small, focused user groups. It's faster, cheaper, and more revealing than wide surveys or launches.
4. Prototype with whatever is fastest. From AI tools like V0 to basic Figma slides, the goal is speed. You don’t need polished UIs — you need fast feedback on core concepts.
5. Strong user reactions guide investment. When users say “I need this now,” that’s a green light. Mild interest or polite nods? That’s a warning to dig deeper.
6. Build only once you have real pull. They don’t move into development (“Make” stage) until a prototype has strong qualitative validation. Code follows conviction.
7. PMs rotate weekly to tag and analyze feedback. Every week, one PM owns triaging incoming feedback, tagging it to ideas, and surfacing themes. Discovery is part of the rhythm — not a side project.
8. Real discovery requires exposure, not summaries. Dashboards, sanitized reports, and secondhand quotes are not enough. PMs must stay close to raw user input — live or recorded.
9. Post-launch reflection is mandatory. Every shipped feature goes into the “Impact” phase. They assess: is it working? Should we scale, refine, or kill it?
10. Discovery is a team sport. PMs, designers, engineers, even sales, everyone participates in interviews, watches clips, and shapes the product. It’s not just a PM’s job.
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Check it out on Apple, Spotify, or YouTube.
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