Why most AI POCs never reach production

POC, pilot, MVP, production: telling them apart

Stage	What it proves	Common trap
POC	Technical feasibility	Stopping there and thinking the problem is solved
Pilot	That it works on a real but restricted scope	Never scaling it up
MVP	The minimum useful value for real users	Confusing a demo with actual use
Production	Value that is sustained, measured, adopted, maintained	Underestimating adoption and maintenance

The five reasons a POC stays stuck

• No baseline: without measuring the before, the gain cannot be proven, so the next investment cannot be justified.
• The wrong workflow: a spectacular but peripheral case, instead of a profitable and central one.
• No planned adoption: a tool nobody fills in is worth nothing, however good the engineering.
• No industrialisation: no evaluation, monitoring or governance, so the solution drifts and nobody notices.
• Integration with the information system neglected: an isolated prototype that does not connect to the real tools cannot scale.

The method to make the leap

A costed baseline upfront, a profitable workflow chosen over the most spectacular one, adoption planned from the start, industrialisation (evaluation, monitoring), governance and integration with the information system: these five steps lead to production that is genuinely measured. At Kolibro, every initiative carries from day one a costed baseline, a business owner and a success metric, and the measure of success is not going live but observed usage. We build for production, not for the demo, following our six-step method.

Signs that a POC is, or is not, ready for production

Ready: a measured baseline, a gain demonstrated on a real case, users who adopt it spontaneously, integration with the information system scoped, a monitoring plan. Not ready: a gain estimated by guesswork, forced usage, an isolated prototype, no governance. A diagnostic lets you decide before investing further.