For years, the feature-based roadmap has been our north star. A clear, sequential list of what we’re building and when. It gives stakeholders comfort and teams a sense of direction. But let’s be honest: in the age of AI, this model is starting to feel incredibly fragile.
AI development isn’t linear; it’s a process of discovery. We aren’t just shipping features; we’re training models and exploring capabilities, where the outcome is often uncertain. This requires a fundamental shift from a ‘feature factory’ to a ‘learning factory.’
Instead of a roadmap of outputs (‘Build X by Q3’), we need a roadmap of outcomes and hypotheses (‘Test if our new model can reduce support ticket resolution time by 15%’). This reframes the conversation with leadership and aligns the entire team around learning and impact, not just shipping. It turns the roadmap from a static document into a dynamic guide for navigating uncertainty.
How are you adapting your roadmapping process to better suit the experimental nature of AI-driven product development?