This guide explains how organizations can evaluate and prepare their data center infrastructure to support AI and machine learning workloads, which require significantly higher compute, storage, networking, and power capabilities than traditional systems. It introduces five key readiness dimensions: GPU-based compute, high-throughput storage, low-latency networking, high-density power and cooling, and orchestration platforms such as Kubernetes. The guide includes a scoring framework to assess current readiness and identify gaps, along with a phased approach starting with inference workloads and scaling to full training environments. By addressing infrastructure requirements and planning incrementally, organizations can build scalable, AI-ready environments that support advanced analytics a

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