Most AI pilots that fail to reach production do so at the handover to operations, long after the technology has proved itself. Gartner research published in September found that only 22% of companies have scaled AI across more than one business unit, from a survey of 1,303 firms. The other 78% are rarely short of activity. They have pilots that worked and tools that individual teams now rely on. What they lack is a route from a successful pilot into the way the business runs every day, and that route is almost never designed at the outset.
The problem persists because pilots are set up to answer a narrow question. Can this model classify these documents, route these calls or predict this demand with acceptable accuracy? A small team with protected time and a clean extract of data can usually answer that within a few months. The pilot is judged a success, and the business assumes the hard part is over. In practice it has not started, because production asks questions the pilot was deliberately shielded from: who owns the changed process, which systems the AI must connect to, who supports it out of hours and which budget pays for it next year.
The data dimension is usually the first to surface. A pilot built on a curated extract tells you very little about whether live data will support the same result. In many firms we work with, the extract was assembled by hand, cleaned by an analyst and never refreshed. Production needs a live connection into systems of record that were not designed for it, with records that are incomplete or duplicated. Nobody owns the gap between the pilot data and the live data, so it is discovered late, usually by the engineers asked to build the integration. By then the business case has been approved on numbers.
A familiar moment follows. The pilot team presents strong results and asks for approval to go live. The operations director, seeing the proposal for the first time, asks who will handle the cases the model cannot, how staff targets will change and what happens if accuracy drops on a busy Monday. Finance asks which cost centre will carry the licences and hosting. IT security asks for a review that has not been scheduled. Each question is fair and each takes weeks to answer. Together they turn a three-month pilot into a year of waiting, and the sponsor’s attention moves elsewhere.
A more useful way to see this is that a pilot is the first phase of a production service, and should be designed as one. That changes what a pilot is for. Its job is to prove the model works and to prove the business can run it, with the operational, data and financial questions answered alongside the technical ones. Governance, in this framing, speeds delivery up. The evidence a risk or operations leader needs is built into the pilot from the first week, so approval becomes a decision against criteria everyone agreed at the start.
Firms that scale AI consistently tend to agree four things before a pilot begins. They name a business owner for the process the AI changes, someone measured on the outcome and with authority over how the team works. They identify the systems of record the service will read from and write to, and test the live data against the pilot’s assumptions early. They agree a run budget, so a successful pilot lands in an operational cost line that someone has already accepted. They define an operational measure, such as handling time or first-time fix rate, with a baseline taken before go-live. None of this slows the pilot down. It removes the months of negotiation that otherwise follow it.
For leadership teams with pilots in flight, the practical steps are specific:
Most of the AI value boards are waiting for already sits in pilots that worked. The gap between those pilots and production is rarely technical, and a better model will not close it. It closes when someone decides, early and explicitly, who will run the service and on what terms. This is the part of delivery where we spend most of our time.
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