Scale and measure
From one pilot to operations: ownership, support and ongoing measurement of whether the gain holds.
This is where most stop
Many companies have tried AI, but only a small share of uses reach regular operation. The reasons are seldom technical. More often there is no owner, no plan for support, or no way to see whether the solution still delivers after six months.
Scaling is therefore less about more technology and more about doing what IT already knows how to do: run a service.
From experiment to service
An AI use in production needs a named owner, a description of what it does and does not do, a route for incident reports and changes, and a plan for what happens if the supplier changes terms or model. These are classic ITIL topics: service catalogue, incidents, changes and supplier management.
It helps to give each use a short description that keeps purpose, data, ownership and key figures together.
Measure, so the gain is not just assumed
Key figures from the pilot are followed into operation. That shows whether the gain holds, whether quality drifts and whether costs add up. It also makes it possible to close a use that no longer pays off.
Typical pitfalls are counting saved time that is not actually used for anything else, forgetting the cost of running and checking, and letting the number of uses grow without anyone having an overview.
Other topics
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