A promising AI pilot can stall because the demo answered a smaller question than the organization needs to answer. Producing a useful result on selected examples does not establish that the result will survive ordinary inputs, fit the next person's work or justify a team taking responsibility for it.
For a pilot sponsor, the next step is to identify the unresolved condition. Is there evidence nobody has collected, work nobody has committed to do, or a benefit too small to justify proceeding? Each needs a different response. Another polished demonstration may resolve none of them.
This guide focuses on that conversation between the pilot team and the people who must accept the work. The broader AI adoption strategy connects it to program ownership and priorities. If a rollout reached regular use and was later abandoned, start with the operational recovery guide.
Write down what the demo actually established
Ask the team to describe the result without expanding its scope. “It drafted a return case from these invoices, with a specialist checking each result” is more informative than “the returns assistant works.” Preserve what succeeded, then name the conditions that helped it succeed.
Look for assumptions such as:
- Someone selected complete, readable inputs in advance.
- A specialist supplied missing context or corrected the output.
- The demonstration used access that ordinary employees do not have.
- The result stopped before a downstream colleague had to accept it.
These conditions do not make the demonstration worthless. They tell you which claims still need evidence.
RAND's 2024 study of AI project failures drew on interviews with 65 experienced practitioners. It identified problems including misunderstanding the task, unsuitable data and inadequate infrastructure. The interviews were qualitative, and the study excluded projects that simply applied pretrained language models through prompting. It offers questions to investigate, not a failure rate for your generative AI pilot.
The wider system also matters. The engineering paper Hidden Technical Debt in Machine Learning Systems describes how data dependencies and interactions create maintenance obligations beyond the model itself. It predates today's generative AI tools, but its distinction between a prototype and the system around it remains a useful diagnostic lens.
Keep the successful result and the unresolved condition in separate sentences. A pilot can have produced something valuable while leaving an essential part of the job untested.
Follow one ordinary case beyond the impressive output
Suppose Alex Morgan coordinates returns for a wholesale supplier. Customers email invoices, product references and explanations. Alex assembles a return case for an authorized colleague to approve. An AI assistant can draft that case, but it cannot authorize the return or issue a refund.
The demo uses clear invoices and current product codes. In everyday work, an email may contain several attachments, an older code or a photograph that does not establish which item the customer means. Alex has to resolve those details before the approver can act.
Walk through a representative case with Alex and the approver. Include a difficult case the demo did not cover. Ask them to show:
- What arrives. Which information is available, missing or contradictory?
- What they add or repair. Which checks make the draft usable, and who performs them?
- What the next person accepts. What must be present before the return case can move forward?
- What happens when it cannot. Where does an incomplete case wait, and who resolves it?
This is a way to locate the unanswered question, not enough testing to establish production readiness. The NIST AI Risk Management Framework's MEASURE 2.3 guidance calls for evaluating performance or assurance criteria under conditions similar to deployment. One convenient example cannot represent all of those conditions.
In the returns scenario, the important finding might be that the model drafts well but cannot reliably identify an older product. Or the product lookup might already work, while the team that controls access has not agreed to support it. Those are different findings even if both appear on a dashboard as “pilot delayed.”
Separate missing evidence from a missing commitment
Use the observed case to make the blocker specific. Avoid assigning every delay to model quality or every unanswered question to governance.
| What is keeping the pilot waiting? | What would move the decision forward? | Who needs to respond? |
|---|---|---|
Nobody knows whether older product codes are handled correctly | Representative cases, expected answers and a review of incorrect or uncertain matches | The technical lead and product-data expert |
The lookup works, but ongoing access and support are unassigned | An agreed scope of service, capacity and ownership, or an explicit refusal | The team that controls the lookup and its accountable manager |
Checking the draft costs as much effort as preparing the case normally | A complete-task comparison and a decision about whether the remaining benefit is worthwhile | The returns workflow owner |
The case is useful, but the proposed automation exceeds approved authority | A narrower permitted use or the required review of the proposed action | The responsible business and control owners |
A missing commitment cannot be solved by testing indefinitely. If a platform team has no capacity to support the lookup, another accuracy chart does not create that capacity. Equally, assigning an owner does not establish that uncertain product matches are correct.
A Reddit user in a September 2025 MLOps discussion described pilots depending on other teams that were busy, lost staff or did not share the enthusiasm of senior executives. That is an unverified personal account with no stated employer size. Its useful detail is the difference between executive encouragement and the availability of the people needed to finish the work.
Bring that difference into the review. Ask the team controlling the dependency what it can actually provide, by when and under which conditions. Record “not committed” when that is the answer, rather than translating it into “engineering is still improving the pilot.”
Ask the person who can change the condition
Responsibility on a slide is insufficient if the named person cannot make the necessary decision. A project manager may coordinate the return-case pilot but be unable to allocate platform capacity, change the approval process or accept its ongoing cost.
In a September 2026 LinkedIn article and follow-up discussion, AI strategy consultant Tyan Hynes argued that project managers can be left trying to move pilots through checkpoints without sufficient authority. This is a consultant's judgment, not a measured account of how frequently pilots fail. It raises a practical question: who can resolve the condition your team has identified?
For the returns pilot, put the question to the person who controls the disputed requirement. Ask for an answer specific enough to check later.
That request names the uncertainty, the evidence and the people producing it. Agree on a real date and the acceptance conditions before doing the additional work. Do not set a universal accuracy threshold detached from what a wrong match would cause.
If the blocker is capacity instead, the request should say so. Ask the platform manager whether the team will support the agreed lookup scope, or which priority must move to make that possible. An explicit refusal lets the sponsor reconsider the scope or stop. An indefinite promise keeps everyone waiting.
Make the next review capable of ending the pilot
A useful review ends with a bounded next action and the decision it enables. Record:
- The condition still unresolved, in language the workflow owner recognizes.
- The evidence or commitment required, with a named person responsible for bringing it back.
- The review date and decision owner, including what happens if the condition cannot be met.
Keep required controls intact. Narrowing the returns assistant to preparing a case for human approval may be appropriate; quietly treating an unapproved refund action as part of the pilot is not.
Slow progress is not automatically a stalled project. In the same Reddit discussion, another user described two relatively successful projects that began with simple question answering for underserved users and added complexity gradually. The results are self-reported and unverified. They nevertheless provide a useful counterpoint to the assumption that a pilot must immediately become a large autonomous system.
Look for a change in evidence or commitment between reviews. A smaller use that helps Alex prepare accurate return cases may justify continuing. Repeating the same demo while no team accepts the next requirement does not demonstrate progress.
When the outstanding condition is clear, use the stop, extend or scale guide to decide what the next investment should buy. Some pilots need a focused test. Some need a sponsor's decision about resources. Some have already shown that the proposed use is not worth pursuing.
Questions about stalled AI pilots
Why can a successful AI demo fail to reach production?
A successful AI demo can establish a capability under selected conditions while leaving ordinary inputs, downstream acceptance or operating ownership untested. Identify what the demonstration actually proved, then follow a real case through the remaining work. For a returns assistant, a good draft still needs correct product identification and an approver who can use it. Start with the demo assumptions before deciding that the model needs replacing.
Who should own the move from an AI pilot to regular use?
The workflow owner should decide whether the proposed use improves the work, while technical and control owners remain responsible for their requirements. A sponsor must be able to resolve cross-team priorities that the pilot coordinator cannot change. Name the person who controls each unresolved condition, rather than assigning every dependency to the person who built the demo. The authority check helps make that responsibility actionable.
Should we extend a stalled pilot to collect more data?
Extend an AI pilot when additional evidence can resolve a specific uncertainty and someone can act on the result. More data will not resolve a platform team's uncommitted capacity or a sponsor's unwillingness to fund ongoing support. State what the extension must establish, who will review it and when the decision will happen. If the benefit is already too small, consider stopping. Use the funding decision guide to distinguish a useful extension from indefinite continuation.
What evidence is needed before putting an AI pilot into live use?
Evidence should cover the intended work, its important failure conditions and the checks that make the result acceptable. The responsible owners also need to agree on permitted use, support and what happens when the system cannot complete the task. The amount and kind of testing depend on the consequences of failure; there is no universal sample size or model score that establishes readiness. Use the ordinary-case walkthrough to find missing conditions, then have the relevant owners define the release requirements.
