Get the pilot into daily use
A production checklist for stalled AI pilots: choose an owner, test representative cases, define exceptions, and decide whether to launch, revise, or stop.
An AI pilot is ready for production when the team can run it on ordinary work, detect mistakes, handle exceptions and keep ownership after the builders step away. A successful demonstration answers only part of that test.
The transition is a live enterprise problem. In Deloitte's 2026 State of AI survey, only a quarter of respondents said they had moved at least 40% of their AI pilots into production. The survey covered 3,235 business and IT leaders in August and September 2025. It is evidence of a deployment gap, not a failure rate for all AI projects.
Find the missing production requirement
When a pilot stalls, ask the operator to process an ordinary item without the builder beside them. Observe where they stop. The problem may be unavailable data, an unclear approval, a slow response, an output they cannot verify, or work that lands in the wrong system.
Write the blocker as a concrete requirement. “Improve trust” becomes “show the source record beside the proposed account change.” “Increase adoption” becomes “put the draft in the queue the team already uses.” Some blockers need engineering; others require a business decision.
Use a release checklist
| Release question | Evidence required |
|---|---|
| Does it handle the real workload? | A test set covering common work, exceptions and known failures. |
| Can we judge the result? | Written acceptance criteria and a reviewer who can apply them. |
| Can it operate within its authority? | Verified data access, action permissions and approval controls. |
| Can we recover? | A rehearsed pause, fallback and duplicate-action check. |
| Who runs it after release? | An operating owner, support contact and maintenance budget. |
A failed critical requirement should hold the release even when the average score looks good. Decide which failures are unacceptable before reviewing the results. A system that retrieves the wrong customer's information needs a different response from one that formats a heading poorly.
Expand responsibility in stages
Begin by running the system alongside the current process without taking live actions. Compare its outputs with reviewed work. Then introduce it to a small group with approval before execution. Remove individual approvals only for specific actions whose performance and recovery are established.
This is a proposed rollout sequence, not a promise that every workflow should become autonomous. Some decisions should remain with an authorized person. Our permissions and approvals article gives a practical way to set that boundary.
A support workflow can combine company context and action-taking tools with human approval. The production design must specify where the employee will inspect and authorize the work.
Decide whether to expand, repair or stop
Review completed work, corrections, review time and support effort against the original baseline. Include low-usage days and difficult cases. A benefit that depends on constant help from the project team is still an implementation problem.
Set a decision date. Expand when the agreed criteria hold; repair a specific blocker when the benefit still justifies it; stop when the operating case no longer does. A stalled pilot with a clear workflow and owner can enter a focused implementation engagement without restarting the entire strategy exercise.