Free Lesson

Build Your AI Research Coworker

Part of The AI Research Capability Stack

Hosted by John Whalen, PhD

Wed, Jul 1, 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

Define the coworker's research job

Turn a vague AI assistant into a scoped research coworker with clear inputs, outputs, and limits.

Set up reusable source context

Give the AI the right background, research question, audience, and evidence so each answer starts from the same foundation.

Add human review points

Decide where the AI can draft, where it can compare, and where a human must inspect the output before it leaves the research workflow.

Why this topic matters

Most teams now have access to AI, but very few have a reliable AI research workflow. The result is scattered prompts, duplicated effort, and summaries that sound useful before anyone has checked the evidence.

In this Lightning Lesson, John Whalen shows how to set up an AI research coworker for customer research. You will see how to define the coworker's job, give it the right source material, ask for structured outputs, and add review points so it supports research judgment instead of replacing it.

This is for researchers, marketers, PMs, designers, and founders who want a practical AI workflow they can reuse across studies.

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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