The short answer
Move an AI pilot into production only after reviewing its results against agreed criteria, resolving the required controls and integrations, and assigning responsibility for rollout, support, and recovery.
AI value realization
How to move AI pilots into production
Make production a separate decision supported by workflow evidence, release criteria, and a named owner.
Cortez Lake
A practical production decision for an AI pilot: assess workflow results, close access and integration gaps, assign operating responsibility, and plan a controlled rollout.
What this means in practice
A useful demonstration establishes a reason to investigate. A production decision also needs evidence about review effort, exceptions, access, reliability, and the work required to keep the system running.
Bring H2H in when…
- A useful prototype has no agreed production owner or support plan.
- The pilot produced good examples but has not been assessed against a workflow baseline.
- Security and operations need a defined review before rollout.
Related H2H pages
Frontier AI deployment partnerScope the task, integrations, evaluation, and rollout work around a frontier model.AI-native product developmentBuild an AI feature with agreed acceptance and release criteria.Why frontier-model investments need an operating layerAccount for the software and operating work around model access.
Review the pilot against its original decision
Start with what the pilot was intended to establish. Did it improve the chosen task compared with the baseline? Were the inputs representative? How much work did reviewers spend correcting output or handling exceptions?
Record what the evidence supports and where it remains incomplete. A few compelling examples do not establish how the system will perform across the intended workflow. The next step may be a revised pilot or a decision to stop, as well as a production build.
Define what is entering production
Specify the users, inputs, outputs, integrations, and actions covered by the release. A production scope should say what AI can draft or recommend, what it can execute, and when a person must review it.
Resolve the practical gaps exposed by the pilot: missing permissions, unavailable data, unclear handoffs, and failure handling. Treat additional use cases as new scope so the release decision continues to match the system being delivered.
- Assign owners for the workflow, integrations, and operating support.
- Document human review, override, and escalation paths.
- Agree on quality, access, failure, and recovery criteria before release.
Build a release record people can assess
Bring the evaluation results, remaining limitations, control checks, and operating plan into one review. Include examples of failures and the planned response, rather than presenting only successful outputs.
The reviewers should be able to connect each material requirement to evidence. An action log can support investigation, but it does not establish that every output was correct or every policy was satisfied. State what was tested and which risks remain.
Roll out within limits and review actual use
Define the initial users and workload, the conditions for expansion, and when the team will pause or roll back. Confirm that operators know how to report a problem and continue the work when the AI path is unavailable.
After launch, compare real use with the pilot assumptions. Monitor agreed measures such as review effort, exceptions, errors, and operating cost. H2H helps teams scope this transition; completion of a pilot does not itself authorize broader production use.
Next step
Make the next production decision with evidence.
H2H helps assess the pilot, define the remaining implementation work, and prepare the system for an agreed release review.