Pawel Huryn’s Proven Templates for AI-Ready Product Discovery: Stop Collecting Useless Data

Too many product teams spend months or even millions training AI models that never deliver real value.

Why? They never ask the right questions or discover the right data in the first place.

This is exactly what happens when you skip structured AI data discovery.

To help you avoid this trap, we teamed up with Paweł Huryn – trusted by nearly 200,000 product leaders and thousands of Product Compass subscribers to unpack his actionable AI Data Discovery Framework.

Paweł’s proven approach ensures you’re not just hoarding data, but collecting strategic data — the kind that unlocks automation, personalization, and truly intelligent products.

TL;DR: Key Takeaways

• How AI data discovery differs from standard research
• How Pawel’s templates help you gather meaningful, usable data
• When to run these surveys for maximum impact
• Top mistakes to avoid when automating too early
• Free Usersnap templates you can launch today

Who Is Pawel Huryn?

Paweł Huryn is an internationally known product advisor and experienced product manager who helps teams cut through AI hype and discover what really matters. With nearly 200k LinkedIn followers and a fast-growing newsletter, he’s trusted for frameworks grounded in practical product management.

Pawel’s Core Principle

“Most teams collect too much data — but not the right kind.”

Paweł’s framework flips the typical approach:

Start with the value, then map the critical data needed to unlock it.

Our & Paweł AI Product Discovery Templates

1. AI Data Collection Survey Template

Purpose of this template:

Align your internal team on what strategic data is missing, so you only collect what drives real insight.

Key Questions:

• What’s the one piece of data we must collect to gain an unfair market advantage?
• Which critical use cases depend on accurate data?
• Any blind spots or risks we haven’t considered?
• What challenges do we face in collecting this data?

2. AI Data Strategy Discovery

Purpose of this template:

Understand how customers handle data today — and how you can solve real pains.

Key Questions:

• What outcome makes our AI solution a success for you?
• Is data transformation or cleanup a pain point?
• What systems should we integrate with?
• What are your biggest privacy or compliance concerns?
• How can our product directly solve your top challenges?

3. AI JTBD Discovery Template

Purpose of this template:

Find repeatable tasks worth automating and test new feature ideas.

Key Questions:

• What’s the most annoying workflow you’d love to automate?
• What problem does automating this solve?
• What data does it depend on?
• How much time would AI save you?
• Would you pay for this?

👉 All these templates are ready to run in Usersnap — no extra tools needed.

Team Participation & Collaboration in AI Discovery

Successful product discovery doesn’t happen in a vacuum; it’s a team sport.

When it comes to AI discovery, cross-functional collaboration is more important than ever.

Cross-Functional Collaboration Wins

Bring product managers, designers, engineers, and stakeholders together early. Teams that collaborate upfront can spot hidden customer pains, align on business metrics, and shape discovery to drive real outcomes.

Context Powers Better AI

Your AI is only as smart as the context you feed it. Large language models or deep learning tools can surface patterns, but real insight comes from your team’s shared understanding of your users, market, and data.

Transparency Creates Trust

Open communication keeps everyone aligned. When teams share findings, challenge assumptions, and co-create ideas, you avoid blind spots and build better products.

Experiment and Learn Together

Embrace a test-and-learn mindset. Review AI-driven insights as a team, validate what works, adjust roadmaps, and keep customer needs front and center.

Use the Right Tools

NLP platforms, feedback tools, and prediction models can supercharge discovery but they should support your team’s judgment, not replace it.

The key takeaway:

Strong collaboration turns AI insights into real business value.

When Should You Run These Surveys?

“The earlier the better — but it’s never too late.” — Paweł Huryn

Whether you’re shaping an MVP, proving product-market fit, or scaling AI features, smart data discovery stops wasted effort and creates real AI ROI.

Common Pitfalls to Avoid

• Automating too soon without clear signals
• Treating all data as equally valuable
• Collecting data with no plan for integration or privacy
• Running surveys but never validating insights in real workflows
• Chasing solutions without validating assumptions
• Treating AI discovery as a black box instead of a transparent, evidence-based process

Watch the Full Interview

Want the real playbook? Watch Paweł and Shannon Vettes break this down in 10 minutes with real stories, mistakes, and examples of what good looks like.

Next Steps: Put Pawel’s Templates to Work

✔️ Grab your free Usersnap templates
✔️ Run them with your product teams, delivery teams, and top customers
✔️ Use what you learn to align everyone and build AI features that solve real problems
✔️ Learn more about feature discovery processes

By involving delivery teams early and keeping everyone aligned, you move seamlessly from discovery to delivery creating real customer value and stronger revenue growth.

Stop wasting months training AI on useless data.
Collect the right data and make your product indispensable.

Quick FAQs

Q: What is AI data discovery?
A: It’s the process of finding and collecting the specific data that makes AI features valuable and usable, instead of hoarding irrelevant data.

Q: Why is Pawel Huryn’s framework different?
A: It flips the approach value first, then data. You discover only what you need to solve real problems.

Q: Can I use these survey templates for free?
A: Yes, Usersnap’s AI Data Discovery templates are free and ready to launch.

Q: Who should use these templates?
A: Product managers, designers, engineers, and data teams who want to build AI features that deliver real results.

Q: Is user perspective and impact mapping part of this process?
A: Absolutely. Understanding the user perspective and mapping impact are key to building features that solve the right problems.