Right now, the barrier to building AI features is lower than ever. The real challenge isn’t writing the code; it’s figuring out if anyone actually needs what you’re building. Traditional customer discovery frameworks often fail here because customers don't always know what's possible with AI, or they end up falling in love with the novelty rather than the actual utility.

In this session, we will break down how to run effective, bias-free customer discovery specifically for AI-powered products. You’ll learn how to look past the initial "wow factor," surface a customer's true workflows, and validate that your AI solution solves a high-frequency, high-pain problem that people will pay to fix.

No theoretical fluff—just actionable strategies to help you find true product-market fit in the age of AI.

Key Takeaways
The AI Novelty Filter: Frameworks to separate superficial "cool factor" feedback from deep, repeatable customer pain points.

Discovery Frameworks: How to interview customers about automated workflows when they don’t yet understand what AI can automate.

Prototyping on a Dime: Low-fidelity and "Wizard of Oz" prototyping techniques to test AI value propositions before training models or racking up API bills.

Value vs. Cost Discovery: How to validate a customer’s willingness to pay relative to the variable compute costs of running AI at scale.