Every team has seen the demo: a prototype wired to a model API that answers impressively on the five examples shown in the meeting. Far fewer teams have seen that prototype survive contact with real users, real data, and real failure modes.
The gap is not the model
The features that die rarely die because the model was too weak. They die because nobody defined what “good enough” meant, so nobody could say whether the system was improving or regressing. They die because the data the prototype was built on looked nothing like the data in production. And they die because the error handling, monitoring, and escalation paths that every other production system gets were treated as optional for the AI one.
Our rule: an AI feature is not a model call, it is a system. Evaluation, retrieval quality, guardrails, and observability are part of the feature, not follow-up work. When we scope AI engagements, those pieces are in the first estimate — because shipping without them is how you end up with a demo that never leaves staging.