A decision-focused checklist for turning a promising AI experiment into a reliable production service without losing sight of data, ownership, cost and user value.
Start with the decision, not the model
A useful AI project starts with a specific decision or task that should become faster, safer or more consistent. “Use generative AI” is not a business outcome. “Reduce the time needed to classify incoming support requests while keeping a human approval step” is.
Write down the current process, its volume, the people involved and the cost of an incorrect answer. This baseline makes it possible to compare the pilot with the existing way of working instead of judging it by an impressive demo.
- Name one primary user and one measurable task.
- Define the acceptable error rate and the cases that always require a person.
- Measure the current time, cost or quality before building.
Test with production-shaped data
A pilot often works because its examples are clean, short and hand-picked. Production inputs contain missing fields, mixed languages, long documents, duplicates and sensitive information. Build an evaluation set that represents this variation before selecting a model or architecture.
Keep the evaluation set separate from prompts and development examples. Score the system against clear criteria, record why failures happen and rerun the same tests whenever the prompt, model, retrieval source or workflow changes.
Design controls around uncertainty
AI output is probabilistic. A production design should assume that some answers will be wrong, incomplete or inappropriate. The right control depends on the consequence: a low-risk draft may only need a visible source link, while a decision affecting money, access or safety may require deterministic checks and explicit human approval.
Define what the service is allowed to do, which data it may access and how users can correct or report an answer. Log enough information to investigate failures without storing unnecessary personal or confidential content.
Make cost and ownership visible
Model cost is only one line in the budget. Production also needs integration work, monitoring, evaluation, security reviews, support and someone who owns the outcome after launch. Estimate cost per completed task at realistic traffic levels, including retries and human review.
Assign a product owner, a technical owner and a person responsible for data or compliance questions. Agree on the rollback path before release. If the system cannot be safely disabled or replaced, the pilot is not yet production-ready.
Use a release gate everyone understands
The final go/no-go decision should fit on one page. State the user group, measured benefit, evaluation result, known failure modes, operating cost, owners and rollback plan. A small release to a controlled user group is usually more informative than another closed demo.
Production is not the end of the AI project. Monitor usefulness and failure patterns, review feedback and rerun evaluations as the underlying data and models change.