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Draft: awaiting a named author and editorial review before publication.
'Pilot purgatory' is not a technology problem. In most organizations we meet, the pilots worked: approximately, for the people who built them, on the data they were given. What is missing is the operating model that turns a working demonstration into a run-and-improved capability. Six gaps recur.
1. No production owner
The pilot had a project team; the product needs an owner: someone accountable for its quality, cost and value next quarter, with budget and authority to change it. When the delivery team disbands and no owner exists, the system decays until someone quietly turns it off.
2. Evaluation was a launch event, not an operation
Pilots are evaluated once, before the decision. Production systems drift: source documents change, usage shifts, models get updated. Without continuous evaluation (automated checks plus periodic human review against a defined standard), quality degrades invisibly and trust follows.
3. The workflow was never redesigned
Dropping an assistant next to an unchanged process produces optional AI, used by enthusiasts and ignored by everyone else. Adoption follows when the surrounding workflow is redesigned so the AI-supported path is the default path, with the human decision points made explicit.
4. Data foundations were borrowed, not built
Pilots run on extracts and workarounds. Production needs governed access, refresh, lineage and quality ownership for exactly the data products the use case consumes. Not a multi-year data program, just the specific foundations this capability requires.
5. Governance arrived at the end
When risk, security and legal review a finished pilot, the only available answers are 'no' or 'redo it'. Intake classification, control requirements and evaluation evidence designed in from the start make approval a checkpoint rather than a renegotiation.
6. Value was never measured against a baseline
If nobody recorded what the process cost before, nobody can defend what the system saves now, and the capability loses the budget argument to whatever is measured. A baseline taken before the pilot is the cheapest insurance an AI investment can buy.
None of these gaps is closed by better models. They are closed by treating AI capabilities as products inside an operating model: owned, measured, governed and improved. That is the work between the pilot and the advantage.
