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.
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.
“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.
Purpose of this template:
Align your internal team on what strategic data is missing, so you only collect what drives real insight.
Key Questions:
Purpose of this template:
Understand how customers handle data today — and how you can solve real pains.
Key Questions:
Purpose of this template:
Find repeatable tasks worth automating and test new feature ideas.
Key Questions:
👉 All these templates are ready to run in Usersnap — no extra tools needed.
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.
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.
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.
Open communication keeps everyone aligned. When teams share findings, challenge assumptions, and co-create ideas, you avoid blind spots and build better products.
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.
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.
“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.
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.
✔️ 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.
AI data discovery uses artificial intelligence, machine learning, and natural language processing to simplify finding and understanding your data. It’s more than just uncovering random datasets—AI understands context, identifies connections, flags sensitive information, and even suggests next steps. This streamlines data governance, accelerates analysis, and ensures teams focus only on the data that truly powers AI and business outcomes.
Paweł Huryn’s AI Data Discovery Framework takes a different approach. Instead of collecting all the data first and figuring things out later, it starts by asking what’s actually valuable—what problems need solving. With that clarity, you focus only on the data you really need. This value-first strategy prevents teams from wasting effort on unnecessary data and helps them gather the right information—the data that drives automation, personalization, and real impact.
Teams benefit most from AI discovery surveys by conducting them early—when defining the problem—and then again after major milestones. Early surveys help identify the best use cases, check data readiness, and align the team. Later, they are useful for monitoring user adoption, detecting bias, and tracking model drift. Ongoing discovery ensures your AI project stays focused, effective, and connected to real business value.
Usersnap’s AI Data Discovery Templates are ideal for cross-functional teams—product managers, designers, engineers, and data specialists all gain from using them. They help everyone agree on what data matters, validate user problems, and identify workflow pain points. Whether you’re launching a new AI feature or expanding an existing system, these templates provide a clear, organized way to discover what drives better, faster decisions.
Usersnap simplifies feedback and data collection with in-app surveys, visual feedback, and AI-powered analysis. Teams can collect annotated screenshots, organize open-text responses, and send insights directly to tools like Jira or Slack. Everything is centralized, creating a true feedback loop that leads to smarter discovery, faster iterations, and evidence-backed decisions throughout the product lifecycle.
AI makes it easier than ever to spin up prototypes, test landing pages, and understand…
Over the past few months, we’ve heard the same story again and again through surveys,…
“Delight is not just solving a problem - it’s creating a positive emotional memory.”— Nesrine…
If your AI strategy feels like it’s solving everything except what matters, you’re not alone.…
Too many discovery efforts fail silently. Teams run interviews, ship features, and sprint ahead -…
If you're sprinting with delivery while discovery is stuck in the parking lot, you're not…