AI Hypothesis Generation

Why is this important? 

First, it helps you define problems more clearly. You’ll move more quickly in discovery workshops, reduce bias when forming assumptions, and design more focused experiments. Your research questions also become more effective.

So, what’s the process? 

AI scans feedback and detects recurring themes. You get statements like, “Users skip onboarding because the instructions are confusing,” or, “Teams want custom exports to share reports externally.” Sometimes, it’s, “Mobile delays reduce engagement rates.” These aren’t just ideas—they directly inform your next interviews and A/B tests.

A couple of quick examples:

When you sort through complaints, you might find, “Users don’t understand the search hierarchy.” Or, if you notice frequent requests for reports, the AI might highlight, “Teams need flexible, portable insights.”

Where does this make a difference? 

Practically everywhere you need better understanding: aligning during discovery, defining issues, planning experiments, mapping out your roadmap, and conducting UX research.

Consider Usersnap (Airis) as a case in point.

Airis takes groups of feedback and turns them into hypotheses so product managers can act quickly—from spotting a pattern, to exploring it, to validating it. No more guessing—just real progress.