A POC always looks promising. It’s fast to build, lives inside a notebook, and uses cherry-picked documents. Every retrieval works, every answer looks smart, and everyone walks away thinking, “This is going to change everything.” But once you try scaling that prototype into production, the illusion disappears.
In production, your POC faces real traffic, real latency constraints, real edge cases, and real messy data. Documents change. Product names differ. Inputs get noisy. LLM responses vary. Suddenly the once-beautiful prototype turns brittle. And then the question arises: “Why doesn’t the POC work in real life?”
The truth is, cool tools don’t equal production systems. Building a scalable AI feature requires governance, monitoring, versioning, reliability, and data quality. This post explains why 90% of AI POCs die at the edge of production — and how companies can escape the POC trap entirely.
