AI initiatives often fail for the same reason: the data foundation isn’t ready. Teams move ahead with model development before addressing data quality, governance, or ownership, making it difficult to scale or even deliver reliably. In a recent conversation with leaders at a fintech building ML-based fraud detection, the use cases were clear and the signals were mapped. But during integration, most of those signals turned out to come from legacy pipelines. The data wasn’t com