AI initiatives often fail due to a lack of disciplined evaluation and timely decisions. Many AI projects proceed without clear success criteria, rendering leaders unable to tell whether an approach still delivers value. There is also a bias from organisations to lean towards general-purpose LLMs due to their versatility, familiarity within teams, and rapid onboarding. This causes specialised LLMs to be ignored despite the ability to provide greater strategic value in some scenarios. Both LLM approaches must be examined to find the right solution for each use case. When a chosen approach fails, organisations must pivot by defining performance, cost, and risk thresholds upfront and tracking them rigorously. These guardrails give evidence to act early (for example, tuning a model, tightening governance, or shifting architecture) before a situation worsens. CIOs and IT leaders must treat LLMs as continuously evaluated systems to ensure …