Most AI pilots do not fail on the model. They fail on the last mile — the integration, evaluation, governance, and change management that make a system operational.
Every organisation we speak to has a growing shelf of AI pilots. Some work well in isolation. Very few graduate into everyday operations.
The reason is rarely the model. Modern foundation models are extremely capable. The reason is what surrounds them: the data pipelines, the integrations to the systems where work happens, the evaluation harness that gives leadership confidence, the governance model that satisfies security and legal, and the change management that ensures teams actually adopt the new workflow.
This piece outlines the checklist we use when helping organisations move an AI pilot into production. It is deliberately practical and vendor-neutral.
Define the operational outcome — not the model metric. A pilot proves the model can do the task. Production requires proof it improves the outcome that matters. That is a different conversation, with different stakeholders.
Design the human workflow first. Where does the AI enter, and where does a human enter? What happens on failure? What happens on disagreement? Write it down in the operational language of the team.
Build the evaluation harness before you optimise. If you cannot measure quality on a rolling sample, you cannot safely iterate. Evaluation is a first-class deliverable.
Integrate to the systems where work happens. If the assistant lives outside the tools people already use, adoption stalls. Integration is not a nice-to-have.
Treat governance as an enabler. Access, logging, review, and escalation should be designed with security and legal partners — not retrofitted after an incident.
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