The pilot proves possibility, not reliability
A pilot answers one question: can this work at all? Production asks a harder one: will it work every day, for every user, at a cost we can sustain? The gap between those questions is where most AI initiatives stall.
The good news is that the gap is predictable. The same handful of capabilities separate demos from dependable systems, and they can be planned for from the start.
Five things production AI needs
Before scaling an AI pilot, check that you have a plan for each of these:
- Evaluation: a representative test set and automated scoring, run on every change.
- Data pipelines: reliable ingestion and refresh, not a one-off export.
- Security: access control, prompt-injection defences and data-handling rules.
- Cost control: caching, model routing and monitoring of spend per task.
- Ownership: a named team responsible for quality after launch.
Measure before you scale
Agree the success metric before building, measure a baseline, and compare after launch. Without a baseline, even a successful system struggles to win continued investment.