What automation has solved, what it hasn't, and why data quality is the real bottleneck now.
AI has changed what research operations look like. Recruiting, transcription, tagging, summarization, segmentation, even first-pass usability evaluation: most of the pipeline is now automatable, and a lot of it is already running in production. That shifts the conversation. The question is no longer "can we automate this step?" but "what are we feeding into the system, and can we trust what comes out?"
This webinar gives a structured overview of where Product, UX, and Market Research stand in 2026: what AI reliably does well, where it still fails, and why sample quality, methodology, and validation matter more than they did five years ago, not less. We'll look at automated usability evaluation, AI moderators, synthetic respondents, and auto-segmentation, and at the data quality problems that scale with them: panel fraud, AI-generated open ends, and over-recruited respondents.
For Product Managers, UX and Research leads, and Insights teams.
Secure your spot — it's free!
Strategic thinking and practical guidance from our team
Research disciplines, methods, and principles form a unified system. Understanding this framework is what separates scattered activities from strategic research practice.
An interactive calculator that uses decision theory to estimate whether a research study is worth the investment, before you run it.
The biggest mistake teams make with AI is treating it like a magic black box. Here is a complete, reliable workflow for using LLMs as research assistants while maintaining critical human oversight.