Everyone’s talking about building AI into our products, but I’m more fascinated by how AI is changing the process of product management itself, especially in discovery. We now have incredible tools for transcribing user interviews, summarizing feedback, and even generating user personas from data sets. This promises to accelerate our learning cycles dramatically.
But here’s the rub: are we trading depth for speed? AI is brilliant at synthesizing the ‘what’—the patterns and common themes from dozens of interviews. However, it often misses the ‘why.’ The hesitation in a customer’s voice, the context of their environment, the outlier comment that contradicts the consensus but hints at a game-changing opportunity—these are the nuanced data points that true empathy is built on.
By letting AI do all the synthesis, we risk becoming disconnected from the raw, unfiltered voice of the customer. We might be building for an averaged-out persona instead of a real person with a real problem. The new core skill for PMs isn’t just using these tools, but knowing when to put them aside and engage directly.
How are you balancing the efficiency of AI-powered discovery with the essential need for deep, first-hand user empathy?