Remember those endless product discovery meetings? The whiteboards filled with sticky notes, the heated debates about user needs, the countless surveys and interviews? Well, I’ve got news for you: that era is rapidly coming to an end.
AI isn’t just changing how we build products—it’s fundamentally transforming how we discover what to build in the first place. And frankly, it’s about time. For too long, product discovery has been more art than science, more gut feeling than data-driven insight.
Let me share something I’ve observed across multiple startups and enterprise teams. Traditional product discovery methods often miss the mark because they rely too heavily on what users say they want, rather than what they actually need. Henry Ford’s famous quote comes to mind: 「If I had asked people what they wanted, they would have said faster horses.」 We’ve been chasing faster horses for decades.
Now, consider how AI changes this dynamic. Tools like ChatGPT and Claude can analyze thousands of customer conversations, support tickets, and social media posts in minutes—uncovering patterns and pain points that would take human researchers weeks to identify. I recently worked with a fintech startup that used AI to analyze 50,000 customer service interactions and discovered three critical user pain points nobody on the team had even considered.
But here’s where it gets really interesting: AI isn’t just helping us discover better products—it’s revolutionizing how we document and communicate those discoveries through Product Requirements Documents (PRDs). Remember those 50-page PRDs that nobody read? The ones that gathered digital dust while engineering teams built what they thought you meant rather than what you actually wrote?
Modern AI-powered PRDs are living documents. They can automatically update based on new user data, A/B test results, and market changes. They can generate multiple scenarios and predict outcomes. I’ve seen teams using AI to create 「smart PRDs」 that actually answer engineering questions before they’re even asked.
This aligns perfectly with what I call the 「Product Development Golden Rules」 from Qgenius (The Qgenius Golden Rules of Product Development). The principle of 「starting from strong user pain points in niche markets」 becomes dramatically more achievable when AI can identify those pain points with surgical precision.
However—and this is crucial—AI doesn’t replace product intuition. It enhances it. The best product managers I know are using AI as a co-pilot, not an autopilot. They’re combining AI’s data-crunching capabilities with human empathy and strategic thinking. After all, as the Qgenius principles remind us, 「products are compromises between technology and cognition.」
Here’s my concern though: are we becoming too reliant on AI’s answers without questioning its assumptions? I’ve seen teams treat AI recommendations as gospel, forgetting that these systems are trained on historical data and might miss emerging trends or edge cases.
The most successful teams I’ve observed are using AI for what it does best—processing vast amounts of data—while maintaining human oversight for strategic decisions and ethical considerations. They’re creating feedback loops where AI suggestions inform human decisions, and human insights refine AI models.
So where does this leave us? We’re standing at the edge of a transformation where product discovery becomes more scientific, PRDs become more dynamic, and our ability to create products that genuinely solve user problems becomes exponentially better.
But I have to ask: as we embrace these powerful AI tools, are we losing something essential about the human touch in product development? Or are we finally unlocking our potential to create products that truly matter?