The conviction running through our conversation was direct: stop tolerating fragmentation. The companies that connect AI ambition with a strong enterprise foundation will win. Everyone else is running expensive experiments.

Underpinning this strategy is the Databricks Platform, which Albertsons uses across data engineering, ML, governance, and analytics. This shared foundation makes the 'one platform’ mandate real, giving every team the same starting line rather than a different set of tools.

Aly McGue: How did you move your organization from fragmented, business-unit-owned AI experiments to a centralized AI core team and operating model?

Sunil Gopinath: We stopped tolerating fragmentation and made a firm architectural decision. One team, one platform, one operating model. We organized around four big bets in AI: customer experience, merchandising intelligence, labor, and supply chain. Those gave us strategic focus. The centralized AI core gave us the muscle to execute.

The logic was straightforward. There was a clear organizational need for common horizontal components, things like governance, security and a central repository of reusable models. A dedicated team focused on those building blocks means the application teams don't have to worry about hygiene and foundations. They can focus entirely on making the business better, more predictable, more actionable.