Separating an AI proof of concept from production

List the responsibilities hidden by a prototype

A proof of concept can use curated inputs, manual preparation and a developer watching every run. Production needs to handle ordinary users, malformed inputs, unavailable services, access controls and changing data. Document the assumptions that made the prototype work. Each assumption becomes an implementation requirement, an operating process or a reason to narrow the launch scope.

Worked example

Illustrative scenario; not a claim of measured client results.

A demo assistant may answer correctly because someone manually selected the supporting document. Production must retrieve that evidence consistently and respect permissions for each user. A demo may also ignore retries, logging and failure recovery. Test those paths before allowing the system to perform work on which customers or staff depend.

Checks, limitations and next decisions

If the prototype's quality cannot be reproduced, inspect the missing preparation rather than simply enlarging the prompt. Define support ownership, monitoring and rollback before release. Keep the distinction visible in proposals and internal reporting: feasibility has been demonstrated under certain conditions, while dependable operation across the intended workload still requires evidence.

A practical starting point

Work through these checks in order. Use the results to decide what needs to change in your own workflow.

1. List assumptions hidden by the demonstration.
2. Add access controls, monitoring, error handling and recovery.
3. Test realistic volumes and edge cases with the actual workflow owners.

A mistake to avoid

Do not treat a polished interface as evidence that operational risks are solved.

How to check progress

Track readiness gaps by owner and verify each before launch.