In This Episode:
- Why their early stance on AI-powered interview synthesis has shifted
- What Teresa learned from running 15 interviews through ChatGPT and Claude
- How AI raises the floor for beginners but accelerates experts even more
- The importance of separating analysis from synthesis
- Why most teams struggle with customer interview synthesis in practice
- What happens when interviews pile up — and whether AI can realistically help
- The risks of relying on AI when your interviewing skills aren’t solid yet
- A vision for “expert + AI” synthesis that’s both fast and high-quality
- The ongoing debate about AI-led customer interviews
- A detour into PC-building that perfectly illustrates the limits of AI support
Key Takeaways:
- AI isn’t magic. It can help, but only if your interviews are strong and you provide the right context.
- Beginner + AI is usually better than nothing. But the real performance gains come from expert + AI.
- You still need to synthesize every interview individually. Dumping transcripts into an LLM isn’t a shortcut.
- Customer understanding is a competitive moat. Outsourcing it entirely will cost you in the long run.
- Empathy comes from human interaction. AI can’t replace the experience of talking directly to your customers.
Resources & Links:
Mentioned in this episode:
