Shawhin Mosadeghzad

Between new models shipping every month and regulations being rewritten just as fast, most enterprise AI architecture decisions are built on an assumption that today's constraints will hold. That assumption leads to pilots stalling out or landing in pilot purgatory. This session looks at why AI infrastructure needs to be built for change from the start, not retrofitted once the ground shifts. We'll walk through the architectural pattern that lets organizations adapt to new models, new regulations, and new data environments without rebuilding. Flexibility and control, delivered without the runaway costs typically attached to both. We'll share the architectural requirements: sovereignty, governance, and context, that separate AI systems built to stand the test of change. We'll show you how to design for adaptability so that new models, new regulations, and new data sources are things your AI absorbs rather than breaks under, and how to get there with an approach that makes your data sovereign by default for AI.

Most AI initiatives stall in pilot for the same reason: production data is hard to access, govern, and trust. In this session, we break down the real blockers teams face when connecting AI to live enterprise data and show how governed access, business context, and verifiable results change the equation. Learn how organizations are moving AI into production, with Simba Intelligence, without copying data, breaking pipelines, or losing control.

Most AI initiatives stall in pilot for the same reason: production data is hard to access, govern, and trust. In this session, we break down the real blockers teams face when connecting AI to live enterprise data and show how governed access, business context, and verifiable results change the equation. Learn how organizations are moving AI into production, with Simba Intelligence, without copying data, breaking pipelines, or losing control.