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.
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Strategic thinking and practical guidance from our team
The AI landscape changes weekly. Rather than chasing specific tools, you need a durable framework for evaluating any platform against principles that will not change: privacy, transparency, portability, and reproducibility.
An overview of UX events, meetups, and education in Vienna in 2026: community formats, the conference, courses, and the people behind them.
Research disciplines, methods, and principles form a unified system. Understanding this framework is what separates scattered activities from strategic research practice.