User Research for AI-Powered Products

Understanding how people interact with AI products requires research methods that go beyond traditional usability testing. Users often can't articulate what they want from AI because they don't have clear mental models of what it can do. They may over-trust AI outputs in some contexts and under-trust them in others, and their behaviour changes over time as they learn the system's strengths and weaknesses. Effective user research for AI products includes observing how people interact with AI outputs in realistic contexts, understanding their expectations and where those expectations diverge from reality, and exploring how trust develops or erodes over repeated interactions. You need to test not just whether the AI gives good answers, but whether users can tell when it gives bad ones and what they do about it. Longitudinal studies matter more than one-off usability sessions because the relationship between users and AI systems evolves significantly over weeks and months. Pay particular attention to edge cases and failure modes, because user trust can be fragile - a single dramatic failure can undo weeks of positive experiences.