Putting AI into production without breaking the org
Most enterprise AI dies in the gap between a promising demo and a system people actually trust. Here is how we close it.
Every large company we talk to has the same drawer of impressive AI demos that never shipped. The model was never the hard part. The hard part is the organization around it — the data access, the review process, the person whose job quietly changes when the system goes live.
We treat an AI feature like any other piece of production software: it needs an owner, a fallback, an evaluation harness, and a clear story for what happens when it is wrong. Confidence comes from constraints, not from the size of the model.
Start narrow, earn trust
The teams that succeed start narrow. One workflow, one measurable outcome, one group of users who feel the difference in week one. Scope is the strategy. Once trust compounds, expansion is easy.
The opposite — a company-wide rollout of a system nobody has learned to rely on yet — is how good models get switched off and never turned back on.