A POC that “works” in a demo and an AI service in production are two very different things. The first proves an intuition; the second meets reliability, cost and compliance commitments, day after day. That is exactly the step where many initiatives stumble. Here are the five blockers we see most often.
1. No measurable success criteria
A POC validated “by eye” cannot ship: you know neither what you are industrialising nor when to stop. Before writing a single line of code, set a business metric (time saved, automatic processing rate, quality) and an acceptance threshold.
2. Demo data, not real data
The clean, narrow datasets of a POC hide reality: volume, edge cases, missing values, access rights. Industrialisation starts with an honest map of the data available and its quality.
3. No continuous evaluation loop
A model or an LLM agent drifts. Without a versioned evaluation set run on every change (prompt, model, data), you cannot catch a regression before your users do. Evaluation is a production asset, not a one-off acceptance step.
4. Unit cost is not under control
Cost per call looks negligible in a demo and becomes critical at scale. Measure cost per request and per use case, then make trade-offs: smaller model, caching, context truncation, batch processing.
5. Governance is handled last
Traceability, GDPR, the AI Act, human oversight: bolted on at the end, they block go-live. Anticipated, they become a simple trust layer around the service.
- Define the success metric and its threshold at scoping.
- Test on real data, not an idealised sample.
- Version an evaluation set and run it continuously.
- Track unit cost as a production indicator.
- Embed governance from the design stage.
This is exactly what we tool up in our industrialisation & automation offer. To go further, explore our guides & resources.