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I’ve noticed a recurring pattern in boardrooms and team meetings: the mandate to ‘add AI’ to the product. Too often, this translates into a surface-level feature—a chatbot, a text summarizer, a ‘smart’ search bar. While these can be useful, they represent a fundamental misunderstanding of the current technological shift.
The real opportunity isn’t in bolting on AI features; it’s in building AI-native products. These are products where the machine learning model isn’t just an enhancement; it is the core value proposition. Think of Midjourney or a predictive analytics platform—they couldn’t exist without the model at their center.
This distinction changes everything for us as product leaders. Our user research must evolve from validating UI flows to exploring problems that were previously unsolvable. Our roadmaps become less about a predictable list of features and more about a strategy for improving model performance and data quality. It requires a much deeper, earlier collaboration with data science and engineering to understand what’s possible.
This shift demands intense cross-functional alignment, ensuring the entire team understands how model improvements connect to user outcomes. Platforms that facilitate this kind of strategic transparency, like Leera.io, become essential for keeping everyone pulling in the same direction.
So, my question to you all is: how are you navigating this shift? Is your team still focused on adding AI features, or are you making the leap to building a truly AI-native product?