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In this episode, Ray Quattromini speaks with Patrick Kay, Product Manager for IBM Content Aware Storage (IBM CAS) at IBM, in one of the most technically detailed conversations on the podcast to date.

IBM CAS is IBM's semantic interface for AI-ready data, a solution designed to transform unstructured enterprise data into a searchable vector database, without requiring costly data migrations or duplication. Patrick explains how IBM CAS uses Active File Management (AFM) to connect disparate storage environments, how the RAG framework is embedded directly into the storage layer, and why incremental vectorization dramatically reduces the GPU compute required to keep AI applications grounded in up-to-date enterprise data.

They also discuss IBM CAS deployment architectures, storage tiering from flash to tape, the impact of the global storage shortage on AI infrastructure planning, and why IBM CAS is increasingly the missing layer between enterprise data estates and production AI workloads.

Essential listening for CIOs, data engineers, storage architects and anyone evaluating IBM CAS or enterprise AI infrastructure.