An AI POC proves an idea is possible. Putting it in production proves it is reliable, operable and maintainable. The whole gap is there.
A demo impresses in a meeting and disappoints in real conditions. Moving from POC to production is rarely a model question. It is a system question.
What separates the demo from production
A reliable data pipeline, reproducible evaluation, guardrails, edge-case handling, monitoring. The model is only one component. Around it, you need a chain that holds when data drifts and traffic grows.
Inference is a decision too: edge or cloud, acceptable latency, cost per request, privacy. These constraints shape the architecture as much as model accuracy.
Industrialise without over-promising
Frame feasibility first. Many use cases do not justify a custom model. When they do, you design for operation: version, evaluate, monitor, be able to roll back.
Shipping an AI system is not shipping a notebook. It is shipping something a team can run, measure and improve without its author.