A pilot proves something can work. Production proves it keeps working, safely, for real users and at real volume. Most AI projects stall in the gap between the two.
Why pilots don’t transfer
Pilots are run on curated data, by an enthusiastic team, with a forgiving audience. Production has none of those advantages. The examples are messier, the users are busier and mistakes reach customers. A model that scored well on fifty hand-picked cases can behave very differently on fifty thousand real ones.
What changes in production
- Data arrives messier, in more formats and in larger volumes than the pilot sample.
- Costs per request start to matter once usage grows.
- Latency becomes visible to users who are waiting on a screen.
- Errors reach customers, not just the project team.
- Someone has to own it at 2am.
The minimum to ship
- An evaluation set of real, labelled cases that reflects the variety users will bring.
- Automated tests that run that set on every change to prompts, models or data.
- Monitoring for quality, cost and latency, with alerts when they drift.
- A fallback path when the model fails, times out or is unsure.
- Logging that lets you trace any output back to its input, for debugging and audit.
- A named owner, and a process for users to flag bad results.
Without these, the feature degrades quietly. Model providers update versions, user behaviour shifts and data changes. You find out from a complaint rather than a dashboard.
Ship the monitoring before you ship the model.
Keep people in the loop
For most business processes, the right first production design has the model propose and a person approve. Track how often people accept, edit or reject suggestions. That data tells you where the model is reliable enough to automate further and where it still needs oversight. It also builds trust with the teams who use it.
Control the cost
Costs that were trivial in a pilot can grow quickly. Cache repeated requests, use smaller models for simpler steps and set budgets with alerts. Measure cost per completed task, not just per request, so you can compare the AI path with the manual one honestly.
Governance without paralysis
Write down what the feature is for, what data it uses, who owns it and how it is monitored. Keep it to a page. Review it when anything material changes. This satisfies most internal risk teams and, increasingly, regulators, without slowing delivery.
Next steps
Agree success measures with the business before rollout. Release to a small group, review results weekly and expand only when the numbers are stable. Once one feature runs well in production, the evaluation, monitoring and fallback patterns you built become a platform for the next.