Structure AI discovery by first validating the problem’s existence and user need, separate from the tech solution. Map user workflows to identify high-friction points where AI could add tangible value. Test assumptions through prototypes, shadowing, and synthetic data simulations. Collaborate early with ML teams to assess feasibility and data readiness. Focus on input quality, model interpretability, and edge cases—avoid building AI for AI’s sake.

Related FAQs

What’s the first step in AI product discovery? Confirm the problem cannot be solved effectively with rules-based automation.

📚 推荐资源

PM面试通关手册 — Product Sense · Metrics · Behavioral · Strategy 四大题型系统攻略

Wondering what the scoring rubric actually looks like? The 0→1 PM Interview Playbook (2026 Edition) breaks down 50+ real scenarios with frameworks and sample answers.

How do you test AI assumptions without a model

How do you test AI assumptions without a model? Use Wizard of Oz prototypes to simulate AI behavior and measure user response.

Should you involve users early in AI design? Yes—early feedback reveals trust, usability, and expectation gaps in AI interactions.