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Your Enterprise Playbook Is Why Your AI Project Failed

I spent 20 years running technology at IBM and Kyndryl leading 200+ engineers, governance boards, phased rollouts, the whole apparatus. That same enterprise playbook is quietly strangling AI projects right now. Not because the discipline is wrong, but because it’s aimed at the wrong problem, at the wrong speed. Here’s why enterprise process front-loads approval and back-loads the infrastructure truth that actually decides whether your AI project lives or dies and what to do instead.

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What AI-Ready Infrastructure Actually Costs to Build

A client wanted one number for getting “AI-ready” before our call ended. Three vendors had already given him one — and that’s exactly the problem. The number you’re quoted is almost always the model, the cheapest part. The real budget lives in data readiness, integration, security, and adoption — the work that has to be true before any model does anything useful. Here’s what each category actually costs, why two companies the same size can land 3x apart, and how to get a real number before you commit a dollar.

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