Accelerate, expand, and simplify your product management workflow

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TLDR

Product managers struggle with using AI effectively despite the hype around its potential. Valerio Zanini, author of AI for Product Managers, shares practical frameworks for leveraging AI tools in customer discovery, hypothesis validation, and feature selection. Key insights include using AI as a discovery assistant to analyze customer interview transcripts, synthesizing market research across multiple sources, and creating rapid prototypes with AI coding tools. Our conversation addresses real barriers product managers face—from corporate restrictions to lack of expertise—and provides actionable approaches to accelerate time-to-insight from months to weeks or days.

Introduction

Product managers know that discovery and validation can make or break a new product or a new version of a product. But, how can AI help us have more success in these areas while also accelerating our work from months to weeks or even days? Many product teams are drowning in customer data while simultaneously starving for actionable insights—it is a challenge I encounter often when I train product managers in companies. AI brings emerging tools to gain value from this data and improve our work. You’re probably already using AI in your work, but I also bet you want to know how to get more from it—how to unlock it’s real potential. Today, you’ll learn specific approaches for using AI to conduct customer discovery, validate hypotheses faster, and select features.

Our guest, Valerio Zanini, brings 20 years of product experience, from founding startups to leading digital transformation at Capital One. He’s trained thousands of product managers worldwide and literally wrote the book on AI for Product Managers. His frameworks aren’t theoretical—they’re tested across industries and proven to accelerate time-to-insight.

Summary of Concepts Discussed for Product Managers

Valerio’s Book, AI for Product Managers
Valerio wrote AI for Product Managers after discovering a gap between AI hype and reality. While social media showcases impressive AI use cases, his research revealed most product managers don’t use AI due to corporate restrictions, lack knowledge about implementation, or struggle with basic application. The book addresses the practical barriers preventing product teams from capturing AI’s benefits, moving beyond theoretical possibilities to tested frameworks that work across industries. When used correctly, AI tools expand, simplify, and accelerate product managers’ work.

The Gap in AI Adoption by Product Managers
Many product managers face significant obstacles to AI adoption that don’t appear in success stories. Corporate environments often restrict AI tool access due to privacy concerns, leaving teams with sandboxed systems inferior to consumer tools like ChatGPT. Product managers frequently lack permission to use AI, don’t understand how to apply it effectively, or face organizational inertia. This creates a disconnect between the potential demonstrated in workshops and conferences versus day-to-day practice where teams remain starved for actionable insights despite drowning in customer data.

AI as a Discovery Assistant
AI excels at analyzing customer interview transcripts to find patterns and insights that humans might miss. After conducting customer interviews, product managers can feed transcripts into AI tools to identify recurring themes, pain points, and unmet needs across conversations. The AI can also act as a synthetized user, helping to expand thinking into areas not initially considered and providing different perspectives on customer feedback. This approach transforms hours of manual analysis into minutes while uncovering insights that might otherwise remain hidden in the data.

Synthetic Users vs Real Customer Interviews
Valerio shared an example of practicing customer interviews in different settings—sitting in a coffee shop talking to real people versus interviewing synthetic AI customers from your office. Synthetic users are digital personas that can simulate customer interviews, providing insights about behaviors, problems, and needs. This offers two key benefits: speed (conducting research in a day from your desk) and practice (refining interview techniques before engaging real customers). Valerio noticed that AI users can help uncover problems that real people may be uncomfortable sharing. On the other hand, real people helped him find edge cases that AI missed.

Synthesizing Market Research
Product managers typically gather market research from multiple sources—analyst reports, competitor analysis, industry trends—but may struggle to synthesize this information effectively. AI can process and combine insights from diverse sources, identifying connections and patterns across materials. Rather than spending days reading and consolidating reports manually, product managers can use AI to generate comprehensive summaries that highlight key trends, competitive dynamics, and market opportunities. This acceleration from weeks to hours enables faster strategic decision-making.

Problem Framing with AI
Before diving into solutions, product managers need clarity on the problem they’re solving. AI can help frame problems by analyzing customer feedback, market data, and business constraints to articulate the core challenge. This includes defining the problem space, identifying affected customer segments, and understanding the business context. AI tools can generate multiple problem framings from the same data, helping teams avoid premature solution-jumping and ensuring alignment on what problem deserves resources.

Rapid Prototyping and Validation
AI coding tools have eliminated traditional prototyping barriers by enabling anyone to create working prototypes without coding expertise. Product managers can describe a feature idea verbally and generate a functional prototype in hours or even a single day. These prototypes aren’t production-ready but allow teams to test problem-solution fit with customers before writing detailed specifications. The ability to iterate rapidly—testing, gathering feedback, and refining—transforms the validation process from a bottleneck requiring design and engineering resources into an empowering capability for product managers.

Feature Ideation and Prioritization
AI can generate feature ideas based on customer insights, market research, and business goals, then help evaluate and rank these options. Product managers can use AI to apply prioritization frameworks like RICE or opportunity scoring models to assess potential features. However, confidence scores should remain low for AI-generated ideas until validated through customer testing. The combination of AI ideation followed by rapid prototyping enables teams to explore a broader solution space while maintaining validation discipline.

Risks of AI Tools in Product Management
Valerio points out that a risk of using AI tools is that they can also accelerate problematic product management practices like feature creep. AI tools shouldn’t make strategic decisions for product managers. Teams can build features quickly and efficiently while still creating the wrong product if they lack clarity on customer problems and value creation. AI tools can’t replace the fundamental discipline of understanding what customers need.

Useful Links

Check out Valerio’s book, AI for Product Managers
Connect with Valerio on LinkedIn
Get discovery prompts for interviews, synthetic users, conducting the interview, and synthesis

Innovation Quote

“There can be no agility without product thinking.” – Valerio Zanini

Application Questions

How might using AI to analyze customer interview transcripts change your discovery process? What safeguards would you implement to ensure AI-surfaced insights are validated rather than accepted uncritically?
If your organization restricts AI tool access due to privacy concerns, what steps could you take to build a business case for secure AI capabilities? What alternatives exist when full AI access isn’t available?
If you could create working prototypes in hours instead of waiting weeks for design resources, how would this change your validation approach? What new opportunities would this enable in your product development process?
When AI generates feature ideas or prioritization recommendations, how do you determine appropriate confidence levels before customer validation? What criteria distinguish AI suggestions worth prototyping from those requiring additional research first?
How do you ensure your team maintains strong product thinking discipline while adopting AI tools that can accelerate execution? What practices prevent the risk of building the wrong things faster?

Bio

Product Manager Interview - Valerio Zanini

Valerio Zanini is a Certified Product Innovation Trainer (CPIT) and a Certified Scrum Trainer (CST). As a trainer and consultant, Valerio works with companies around the world to help them learn, adopt, and improve their AI and Product Management practices. He has taught thousands of people ranging from small startups to large corporations.

Thanks!

Thank you for taking the journey to product mastery and learning with me from the successes and failures of product innovators, managers, and developers. If you enjoyed the discussion, help out a fellow product manager by sharing it using the social media buttons you see below.

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