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

At Snowflake Summit 2025, OpenAI CEO Sam Altman and Snowflake CEO Sridhar Ramaswamy captured a truth that every enterprise pursuing AI eventually faces: there is no AI strategy without a data strategy. Trusted, governed data is the foundation for every scalable AI effort. In leading enterprise programs to unify customer data and enable ML-driven personalization, I have seen how gaps in governance stall progress. One organization I worked with was onboarding ML teams to a new

In every large-scale Agile transformation I’ve led, one theme keeps resurfacing: Agile delivery cannot succeed without an architectural runway. It’s the unseen structure that allows teams to move fast without losing alignment or stability. When the runway is weak, velocity increases risk instead of value. In one enterprise program, architecture diagrams varied so widely that cross-team discussions broke down. Structural and behavioral flows were mixed, documentation was incon