4 Opportunities to Layer AI into Your Product Discovery Activities

​Dear Reader,​

To use AI to shorten your lead time and reduce uncertainty, consider it a layer to supercharge your existing workflow.

Instead of generating with AI, think about supercharging with AI. Here are four common Product Discovery activities where thoughtful AI integration can dramatically reduce uncertainty without sacrificing quality:

#1: Writing Interview Questionnaires

DON’T: Ask an LLM to "write interview questions about our app" and use the output verbatim. You'll end up with generic questions that miss your specific research goals and introduce subtle biases that skew your findings.

DO: Feed the LLM with the context and constraints for your interview. Get crystal clear on your business goals and the research intent questions or assumptions you need to find answers to. Share what existing insights emerged from quantitative data.

Use (re-) documented internal better practices for interviews and offer these as constraints. Add the interview duration and input on prototypes you want to use—if possible, share “what “good looks like” from” other interviews. Then, get to drafting. You'llmazed by the (reduced) amount of correction required.

#2: Synthesizing Research Insights

Utilize built-in capabilities of research repositories like Dovetail:

• Live transcription and marking of insights
• Anonymize Recordings to reduce biases
• Identifying patterns across interviews and feedback
• Query existing insights for answers to what you want to learn

None of these should be done solely by hand. Note how these also stand on the shoulders of your clarity of research goals and method execution quality.

#3: Prototyping

Tools uizard make getting to a first draft easy–given the correct input. Create a prompt for what you want to do, feed it with screenshots of your product or a competitor solution, and have it create an editable and clickable prototype using your brand colors in minutes. While not immediately usable for all user testing out of the box, imagine how powerful the presentation of your ideas becomes if you quickly spin it up and modify it in a meeting to help communicate with domain experts.

There are many more opportunities to take this further through Lovable or Cursor, but I haven't ventured into these territories yet.

#4: Scaling Discovery Knowledge

Curate your company's commonly agreed-upon best practices for Product Discovery and feed these into a CustomGPT (or equivalent). Then, teach teams how to prompt it to scale “Discovery Coaches” and overcome” the bottlenecks of centralized departments. These can still come in for hands-on contextual support beyond the LLM materials.

Garbe-in-Garbage-Out

Beware that this is (for now) often a situation in which the quality of your inputs determines the quality of your outputs. You can talk to hundreds of random people, but if you only ask how they like your product, the AI will only surface these lip service-like insights.

Your unfair advantage remains your domain expertise. Use AI to scale processes, but rely on your critical thinking to set direction and prioritize what matters in your context.

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Thank you for Practicing Product,

​Tim​

Content I found Practical This Week

The Feedback Fragmentation Tax: How Product Teams Lose Touch (And How To Fix It) Read it here

Start testing B2B ideas: the story of Zapier ​ Read it here

Who is Tim Herbig?

As a Product Management Coach, I guide Product Teams to measure the real progress of their evidence-informed decisions.

I focus on better practices to connect the dots of Product Strategy, Product OKRs, and Product Discovery.