A proof of concept exists to answer one narrow question: can this approach work at all, on our data, for our use case? It’s deliberately small, usually built by one or two people in a few weeks, with the messy edges, error handling, and security work left out on purpose, because none of that is what’s being tested yet.
The trouble starts when a successful POC gets treated as a finished product instead of a finished question. The demo that impressed a room full of stakeholders was never built to hold up under real traffic, real edge cases, or real accountability, and the gap between “it worked in the demo” and “it’s safe to run this on live customers” is usually where the actual engineering effort lives. Gartner has estimated that roughly 30% of generative AI projects get abandoned at exactly this junction, not because the concept failed, but because nobody planned for the harder work of turning a proof into a product.
Treat a POC’s success as permission to ask the next, harder question, not as the finish line. The organisations that scale well are the ones that budget time and headcount for the rebuild between proof and production, rather than assuming the demo they already have is most of the way there.