LLM risks are real, but not every deployment needs a firewall. Premature adoption adds cost without reducing exposure. The decision hinges on user trust, data sensitivity, and model autonomy. This guide helps CIOs and CISOs decide when to deploy, how to tier risk, and what to evaluate before committing to a vendor.
AI showback is decision-grade when costs are traceable, value is defensible, and consumption behaviour changes before Finance turns experimentation into a bill.
AI projects do not usually fail because the model stops working; they fail because leaders keep funding them after the evidence says the use case, economics, or risk posture no longer holds.
Long context windows reduce AI build friction, but they do not replace RAG where enterprise workloads demand precision, cost discipline, and defensible source traceability.
AI leaders do not need to chase every new model, but they cannot assume today’s winner will last. The signal is lifecycle risk: build stable, governed AI services around replaceable model dependencies, with baselines, telemetry, fallback paths and migration triggers before production workflows become exposed to vendor retirement or drift.
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