I was talking with a product lead at a major tech company last week, and she said something that stuck with me: 「My team is starting to feel like AI is doing our thinking for us.」 It wasn’t said with anger or fear, but with this weird mix of relief and unease. And it got me wondering – what happens to our identity when the tools we use start reshaping how we think?
Remember when we used to joke that product managers were the 「CEO of the product」? That always felt a bit grandiose, but it captured something essential about the role – we were the synthesizers, the connectors, the people who held the whole messy picture in our heads. We’d gather requirements, talk to users, analyze data, and somehow distill all that noise into something coherent. That was our superpower.
Now enter AI. I’ve been playing with various AI tools that can analyze user feedback, generate feature ideas, even write product specs. And they’re good. Scary good. The other day, I watched an AI system process six months of customer support tickets in about three minutes and identify patterns that would have taken my team weeks to uncover. It felt like watching a magician perform a trick you know you’ll never figure out.
But here’s the thing that keeps me up at night: when AI starts doing the pattern recognition, the data synthesis, the initial ideation – what’s left for us? Are we becoming glorified prompt engineers? Are we just the human layer that approves or rejects what the machine suggests?
I’ve been thinking about this through the lens of The Qgenius Golden Rules of Product Development, particularly the principle about 「mental models.」 The rules emphasize that what defines our user segments isn’t demographics or behavior patterns, but how people think about problems. And right now, I’m watching product managers struggle with their own mental models about what their job actually is.
Some PMs I know are leaning into the change. They’re treating AI as the ultimate assistant – something that handles the grunt work so they can focus on higher-level strategy and stakeholder management. Others are resisting, insisting that 「real product sense」 can’t be automated. Both positions make sense, but I think they’re missing the bigger picture.
The real opportunity, in my view, isn’t about replacing or resisting AI, but about redefining what product management means in an AI-driven world. If machines can handle the quantitative analysis and initial synthesis, maybe our role shifts toward the qualitative, the empathetic, the deeply human aspects of product development. Maybe we become the bridge between what the data says and what people actually need.
I keep coming back to that Qgenius principle about 「reducing cognitive load」 – not just for our users, but for ourselves. The best products flow effortlessly, and maybe the best product managers will learn to flow with AI rather than fight against it. We might need to develop new muscles – maybe we’ll spend more time on organizational psychology, on change management, on the messy human dynamics that AI can’t navigate.
What do you think? Is AI enhancing your product instincts or replacing them? Are we witnessing the evolution of product management or its eventual obsolescence? The answers might determine not just what tools we use, but who we become as professionals.