New Delhi, Sept. 3 -- Enterprise AI is entering a phase where the discussion is no longer simply how much compute a company can access. As AI moves from experiments into production, organizations are having to think about where their data sits, who controls workloads, how models are governed, and whether AI-driven decisions can be audited.

That is putting data sovereignty and infrastructure control closer to the center of enterprise AI strategy. For regulated sectors and government organizations, the issue is even more direct. Enterprises may still rely on hyperscalers for compute, models and scale, but they also want greater control over the parts of the AI stack that carry strategic or regulatory implications.

In an interaction with C...