Every enterprise has now run an AI pilot. Far fewer have something running in production that a CFO would defend. The gap between the two is rarely the model — it is the engineering discipline around it.
A demo answers a question once. A production system answers it ten thousand times a day, under load, with auditability, fallbacks, and a cost ceiling. Getting there means treating AI features like any other distributed system: observability, evaluation harnesses, guardrails, and a clear human-in-the-loop story.
The teams seeing real ROI share a pattern. They scope narrowly, instrument relentlessly, and ship a thin slice to real users early — then expand only where the numbers justify it.
The durable wins are unglamorous: classification, extraction, summarization, and routing inside existing workflows. They compound because they remove friction from work people already do, rather than asking them to adopt something new.