This guide explains that AI success depends on high-quality, high-performance data—not just model investment. It outlines challenges such as siloed data, I/O bottlenecks, unpredictable scaling, and Kubernetes complexity. It recommends modernizing storage with always-current infrastructure, Kubernetes-native data management, and unified full-stack AI platforms. The report highlights Pure’s Evergreen, FlashBlade, Portworx, and NVIDIA-aligned solutions that accelerate training, unify unstructured data access, automate management, and reduce risk for GenAI, agentic AI, and HPC-scale workloads.

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