There's a persistent myth in the AI product world that the best products emerge from pure data analysis. Our research tells a different story entirely.
The Intuition Gap
After interviewing 52 product leaders who shipped successful AI features in the past year, a clear pattern emerged: the initial spark almost always came from human intuition, not from a data insight.
One VP of Product at a Fortune 500 company put it bluntly: "The data told us what users were doing. Our gut told us what they were trying to do. Those are very different things."
When Data Misleads
Data is retrospective by nature. It tells you what happened, not what could happen. When you're building something genuinely new—which is what most AI products are—historical data can actually lead you astray.
The teams that fell into this trap optimized for metrics that measured the old way of doing things. They built AI features that were incrementally better rather than fundamentally different.
Building an Intuition Practice
The best product teams we studied had explicit practices for developing and testing intuition. They conducted regular "intuition audits" where team members would articulate their hunches about user needs before looking at any data.
This isn't anti-data. It's pre-data. It creates hypotheses worth testing rather than letting the data define the boundaries of imagination.