AI product development is going through a major shift. In the past, many teams focused on whether they could build a functioning model. Increasingly, the more important question is what kind of evidence foundation that model is built on. This is not just a change in language. It reflects a more mature stage of the industry. In healthcare especially, AI development is becoming more dependent on evidence-grounded data—data that is not only large in volume, but also meaningfully connected to clinical questions, real outcomes, validation logic, and future use settings.