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It seems like every PM tool now has an “AI co-pilot.” We’re all seeing the promise: instant user story generation, automated feedback summaries, and lightning-fast PRD drafts. The push for efficiency is immense, and these tools are delivering speed in spades. We can certainly build and ship faster.
But I’ve been thinking a lot about a crucial distinction: are these tools making our product strategy smarter, or just faster?
True product discovery is often messy and inefficient. It’s about sensing the subtle hesitation in a user’s voice during an interview, connecting disparate ideas from a brainstorming session, or understanding the deep, unarticulated need behind a feature request. AI is brilliant at synthesizing known information, but can it replicate the intuition required to find the unknown-unknowns?
The danger is becoming a highly-efficient feature factory, churning out well-written but soulless requirements. The real leverage comes from using AI to handle the grunt work, freeing up our cognitive space for deep strategic thinking. Platforms that help centralize and make sense of qualitative data, like the feedback synthesis tools in leera.io, provide the rich, human context that AI needs to be a truly smart partner, not just a fast one.
So, how are you using AI in your discovery process without letting it dilute the critical human element of deep customer empathy?