Free Lesson

Get Synthetic Feedback You Can Trust

Part of The AI Research Capability Stack

Hosted by John Whalen, PhD

Wed, Jul 15, 2026

4:00 PM UTC (30 minutes)

Virtual (Zoom)

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AI for Customer Research: Deliver Trusted Insights at Scale
John Whalen, PhD
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What you'll learn

Use synthetic feedback as preflight

Learn the right role for synthetic users: finding weak assumptions and improving the research plan before real recruiting.

Ground the synthetic audience

Build synthetic participants from clear segments, constraints, behaviors, and evidence rather than generic persona labels.

Set confidence boundaries

Decide what synthetic feedback can suggest, what it cannot prove, and what must be validated with real customers.

Why this topic matters

Synthetic users can help you pressure-test an idea before you recruit real participants. They can also create false confidence if you treat simulated reactions like customer evidence.In this Lightning Lesson, John shows how to use synthetic feedback as a preflight step. You will learn how to ground synthetic users in real context, ask questions, and identify opportunities you might have missed.

You'll learn from

John Whalen, PhD

CEO, AI Research Leader, Cognitive Scientist, O'Reilly Author

I’m CEO of Brilliant Experience, have a PhD in cognitive science, and wrote Design for How People Think (O’Reilly).

For 20 years, I’ve helped teams at companies like Google, GSK, and Capital One better understand their customers, capture insights, and build better products.

When AI started reshaping research, I got curious and learned. My team ran “AI vs. Humans” studies to see where AI excels, where human judgment matters most, and how to effectively blend both.

I’ll teach you how to create trustworthy AI research workflows that let you trace every step and insight.

Then advance to the next course and learn to orchestrate agentic research teams, work strategically with synthetic users grounded in real data, and bring in human judgement so you can scale research without losing rigor.

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