Srinivasa Kattuboina

Every enterprise wants AI, and most have more raw material than they realize. Industry research by IDC shows that while over 80% of enterprise content is unstructured, less than 1% of it is currently ready for AI consumption. This gap is what we call the "Context Crisis", which is the difference between data that's stored and data that's truly seen. Closing it is what turns models that guess into models that know, and agents that assist into agents that can be trusted with real decisions. This keynote argues that AI reliability is a data engineering problem before it is ever a model problem. Drawing from real-world failure patterns, like dark data, broken lineage, missing metadata, unversioned documents, and more, we'll explore how modern DataOps disciplines turn this crisis into a competitive edge. From intelligent ingestion and semantic layers to data products and governance as code, we'll walk through the engineering blueprint that transforms fragmented documents and unversioned information into the contextual fuel agentic AI demands.