Why AI Demos Stall Before Production, and What Teams Must Engineer

Impressive AI demos often take an afternoon to build, but projects can stall for months because a demo tested on a handful of hand-picked examples says little about performance on real cases. Moving to production is mostly ordinary engineering rather than model selection, covering output correctness, integration with software, permission controls, cost tracking, and safe rollout. A central practice is an evaluation set of real inputs with expected correct outputs that can be scored automatically, growing over time as production failures are added. For open-ended outputs, using a second model as a judge can scale evaluation, but it must be calibrated against human labels and its false passes and false fails tracked separately. Structured, schema-validated outputs and specific yes/no rubric questions are also recommended over vague scores or assumed-valid free text.