What needs to happen after the AI pilot?

A pilot answers whether something might work. Production asks whether it will keep working with real data, real users and changing systems.

Write the acceptance criteria

Define the task, the evaluation cases and what counts as a pass. Include cases where the system should decline, ask a question or hand over to a person.

Make behaviour observable

Review errors, usage and cost with appropriate access and retention controls. Logging should help the owner understand what happened without collecting unnecessary information.

Plan for change

Documents change, APIs change and models change. Use a regression set, a release process and a rollback plan so an update can be reviewed before wider use.

Separate a demonstration from an operating requirement

A pilot often uses a small set of clean examples. Production introduces permissions, missing information, changing sources and people who do not know how the system was built. Start by recording which assumptions made the pilot work.

Then test whether those assumptions hold in the intended environment. A source-grounded assistant needs current documents and appropriate access. A workflow agent needs a defined boundary for actions and a useful way to hand over when it cannot proceed.

Evaluate the behaviour you actually need

Define the expected result for representative cases. Include ambiguous questions, outdated information and requests the system should not fulfil. A response that sounds plausible is not necessarily correct or permitted.

Review the cases with the person responsible for the business process. Record the reason for each failure and distinguish source-data problems from workflow design, integration errors and model behaviour. Those causes need different remedies.

Design the human handover

A human review step needs an owner, a queue and enough context to make a decision. Decide when review is required, which information accompanies the case and how the final decision returns to the workflow.

Test the handover itself. If a reviewer is unavailable, the case must remain visible rather than silently disappear. Define how urgent exceptions are identified and which actions are paused until someone approves them.

Make ongoing costs and changes visible

An operating review should cover usage, errors, accuracy and the cost of the connected services. Agree which information is necessary to understand behaviour, who can access it and how long it should be retained.

Updates need a controlled route. Test changes against the agreed cases, document what changed and keep a way to return to the previous working configuration. A model or integration update is a product change, not simply an administrative task.

Decide whether to recover, narrow or stop

Not every pilot should become a production build. It may need a smaller scope, better source material or a different approach. Sometimes the sensible decision is to stop and address a more valuable problem first.

Zinerge’s AI Pilot Recovery service starts with the failure and its dependencies. Ongoing Agent Operations Care supports live systems under an agreed scope. The transition between them should be based on demonstrated readiness and ownership, not the fact that a pilot has already consumed budget.