AI in the UX research process — Sparkbox talk

What the video covers

Chris, UX lead at Sparkbox with over 25 years of experience, walks through how AI fits into two stages of UX research: data collection and synthesis. The talk uses a competitive audit as the running example, showing how ChatGPT handles the initial research and how FigJam AI assists with affinity diagramming and summarization.

Who it’s for

UX designers and researchers who want a concrete, tool-specific demonstration of AI in everyday research tasks. The examples are practical rather than theoretical, focused on the work most UX practitioners already do, such as competitive audits, affinity mapping, and stakeholder-ready summaries.

Key takeaways

1. Start with project setup in AI before the audit itself. Chris shows how to give ChatGPT enough context about the product, the competitive set, and the research goals before asking it to conduct desk research. This preparation step determines the quality of everything that follows.

2. FigJam AI handles the synthesis grunt work. The built-in AI features in Figma’s FigJam can organize sticky notes into thematic clusters, reducing the time spent on manual affinity diagramming from hours to minutes. The researcher still reviews and adjusts, but the first pass is automated.

3. The biggest lift is turnaround time. Across the research process, Chris reports that AI has roughly halved project timelines, not by replacing human judgment but by accelerating the most time-consuming mechanical steps of data collection and initial organization.

4. Challenging assumptions is where AI adds unexpected value. Beyond speed, one of the most useful applications is using AI as a thinking partner to pressure-test research assumptions and highlight blind spots the researcher might not have considered.

Worth watching if…

You run competitive audits or affinity mapping sessions regularly and want to see a realistic, no-hype demonstration of where AI tools actually save time in those specific workflows.