2 posts tagged with "product-discovery"

Somewhere in your observability stack is a table that holds every prompt a user typed into your AI feature last quarter. If your team is like most, that table is used for three things: cost attribution, abuse detection, and the occasional debugging session when a customer reports a bad answer. Nobody on the product team has ever opened it. Nobody on the research team has clustered it. The PM running the AI roadmap has never read a single row.

This is the most expensive oversight in your product organization. The prompts your users typed — especially the ones your feature handled badly — are the highest-resolution form of "what users wish this product did" you will ever collect. You are paying inference costs to generate this signal in real time, and you are throwing it away because nobody decided whose job it was to read it.

Most teams decide they're building an AI feature, then ask users: "Would you want this?" Users say yes. The feature ships. Three months later, weekly active usage is at 12% and plateauing. The postmortem blames implementation or adoption, but the real failure happened before a single line of code was written — in the user research phase that felt thorough but was methodologically broken.

The core problem: users cannot accurately predict their preferences for capabilities they have never experienced. This isn't a minor wrinkle. A study on AI writing assistance found that systems designed from users' stated preferences achieved only 57.7% accuracy — actually underperforming naive baselines that ignored user-stated preferences entirely. You can do a user research sprint that runs for weeks, collect extensive qualitative feedback, and end up with a product nobody uses — not despite the research, but partly because of how it was conducted.