Every product leader I talk to is being asked the same question from their board: ‘What’s our AI strategy?’ Too often, the answer is a laundry list of features—a smart summary here, a chatbot there. We’re bolting AI onto our existing products. But I believe we’re on the cusp of a much bigger shift that requires a fundamental change in how we think.
The real challenge isn’t about adding AI features; it’s about building AI-native products. The difference is crucial. An AI feature is an enhancement. An AI-native product’s core value proposition is inextricably linked to the model’s ability to learn and adapt. Think less ‘add a recommendation engine to our e-commerce site’ and more ‘build a platform that curates a personalized store for every single user.’
This changes everything. Your neat, linear roadmap? It becomes a fluid plan for data acquisition and model iteration. User research? It’s less about asking what users want and more about analyzing behavioral data to feed the system. It requires a deeper partnership with data science from day one. We, as product managers, need to evolve our skills to lead this new type of product development.
How are you shifting your product management practices—from roadmapping to user research—to move beyond just sprinkling in AI features and start building truly AI-native products?