User research is conducted through iterative loops to uncover user needs, motivations, and pain points.
AI is leveraged to accelerate analysis, transcribing, summarizing, and clustering data, but never replaces human judgment.
Researchers perform semi-structured interviews, using AI to handle transcription and note-taking so they can focus fully on engaging participants.
AI-generated summaries extract key insights and timestamps, creating rich, structured data for later analysis.
After interviews, the team defines personas and JTBDs based on real evidence from research.
AI co-pilots can compile persona narratives by aggregating recurring goals, behaviors, and constraints.
The researcher provides verified findings; AI then drafts coherent persona profiles (e.g., name, context, motivations) to speed up documentation.
However, all persona details must remain grounded in real data — AI assists with synthesis, not invention.
Final personas are human-reviewed to ensure accuracy and depth.
AI-assisted text analysis helps in coding qualitative data, identifying recurring patterns and extracting meaningful quotes.
This provides a first-pass clustering of common themes or sentiments.
The research team then refines these clusters to identify high-signal insights — those “aha!” moments that truly drive design direction.
Human oversight remains essential: AI can miss nuance or context, so researchers verify every insight to avoid distortion.