Generative AI beyond the pilot: what it takes to reach production
Many generative AI pilots never ship. The ones that do share four habits: clear evaluation, guardrails, cost control and an owner.
A generative AI demo is easy to build and hard to ship. The gap between the two is where most pilots stall. From the projects we have taken into production, four habits make the difference.
Define what good looks like
Before tuning prompts, write down how you will judge the output. Build a test set of real examples with the answers you expect, and score every change against it. Without evaluation, each improvement is an opinion.
Put guardrails around the model
Production systems need limits: which data the model can see, what it may say, and when it should hand over to a person. Guardrails are also where governance becomes concrete, with logging, review and a record of why the system answered as it did.
Watch the cost per task
Token costs that look trivial in a pilot grow quickly at scale. Measure cost per completed task, not per call, and choose the smallest model that passes your evaluation. Caching and retrieval often cut cost further than a model change.
Give it an owner
A model in production needs someone accountable for its quality, its cost and its risk. In organisations without a senior AI leader, that gap is often why pilots stay pilots, and it is the gap our Chief AI Officer as a Service is designed to fill.
A checklist before launch
- An evaluation set and a pass mark
- Guardrails, logging and a human escalation path
- A cost-per-task target
- A named owner and a monitoring plan
