From AI pilot to production: what needs to be ready?

A successful demonstration is one kind of evidence

A pilot can show that a proposed experience is useful. It does not automatically prove that the system can serve more users, handle exceptions or recover from an interrupted request. Production readiness is a separate review with the business owner, delivery team and operating team.

Describe the production boundary first. Identify the users, data, permitted actions and systems involved. A read-only assistant and an agent that updates customer records need different acceptance scenarios because the consequences of a mistake are different.

Test the work and the failure paths

Keep a representative evaluation set with expected behaviours. Include incomplete requests, conflicting source documents and cases where the correct response is to ask for help. Test whether a user can access only the records they are entitled to see, including after their access changes.

For a system that performs actions, test duplicate requests, interrupted work and upstream failures. Decide how the user knows what completed. A generic success message is not enough when a write to one system succeeded and a second write failed. Store enough information for an authorised operator to investigate.

Agree the release checklist

• Identify the business owner and technical operating owner.
• Verify production identity and permissions with ordinary user accounts.
• Confirm the integration contract and error behaviour.
• Review evaluation results and unresolved limitations.
• Define usage limits and how operating costs are measured.
• Test a fallback or rollback procedure.
• Document support contacts and incident handling.
• Agree what evidence permits the rollout to expand.

Treat each item as evidence to review, not a box that becomes complete because someone added it to a presentation. Where an item is outside the delivery partner's scope, name the team responsible for supplying it.

Make ongoing checks part of the service

Model behaviour, source information and business processes can change. Keep regression examples, record configuration changes and review failures alongside user feedback. A useful operating dashboard connects technical events to the affected business task rather than reporting activity alone.

The NIST AI Risk Management Framework provides a voluntary framework for managing AI risk across design, development, use and evaluation. It can inform the review structure; using it is not a certification or a guarantee that a system is ready.

Roll out in a controlled scope

Start with a defined group and retain a route to human handling. Review whether people can recognise errors and whether support can resolve them. Agree the next review date before expanding the service. A narrower release with clear ownership is often easier to improve because the evidence remains interpretable.

Experrt AI Labs helps connect product development with implementation and operational handover. For systems that can take actions, also read how to scope an AI agent.