Getting AI from pilot to production

Pilots are easy. Nearly every company we speak to has three or four running somewhere. The gap that matters is between a pilot that demonstrated well and something the business can depend on at nine on a Monday morning.

Decide what working means, in advance

Before you expand anything, write the outcome down in a form you can check later. Fewer handoffs per ticket. Resolution time down by a stated amount. Month-end close a day shorter.

Vague goals get settled by whoever has the strongest opinion in the room. Specific ones settle themselves.

Make promotion boring

Most teams have a sandbox. Fewer have a defined way out of it.

Before anything reaches production it needs what any other system needs. Identity and access under your normal controls. Logging you can query afterwards, when someone asks what happened. A data classification that states what it may touch. And a way to switch it off that does not depend on the person who built it being reachable.

None of that is interesting, which is the point. Promotion should be a checklist rather than a negotiation.

Somebody owns the lifecycle

This is where AI operations quietly come apart.

Models get deprecated. Prompts drift as people edit them in place. Costs creep, usually in a line nobody reviews. Someone changes a permission upstream and an agent silently loses half its context, which is worse than it failing outright, because it keeps answering.

Give it an owner in IT and a sponsor in the business, put it on a review schedule, and treat the cost line the way you treat every other recurring spend.

We run our own service desk and SOC this way. That is the only reason we are comfortable recommending it.

Next step: the AI Readiness Assessment takes five minutes and shows you which of these you are missing.