Enterprise AI initiatives often fail not because of model sophistication, but because fragmented data foundations create blind spots that degrade learning systems. At the same time, machine learning teams spend up to 80% of their time on feature engineering rather than innovation. This session explores how unified data architectures and enterprise feature stores move AI development from an artisanal process to industrial-scale engineering. We examine how open table formats such as Delta Lake, Apache Iceberg, and Apache Hudi provide the transactional backbone for unified data lakes, enabling ACID guarantees and schema evolution without costly cross-cloud replication. Key takeaways: → How enterprise feature stores support both predictive ML and generative AI workloads on a single platform → Enabling point-in-time correct features for reproducible models and reliable real-time inference → Using column-level masking and semantic layers to allow governed self-service across AI teams → Improving deployment speed and feature reuse across ML pipelines, reducing time-to-model by up to 70%