Quick answer
Move an AI pilot to production by defining the full workflow, validating it on representative cases, connecting approved systems, and establishing review and recovery paths. Launch with a limited scope, measure live outcomes, and name the people responsible for support. A successful demo is an input to this process, not its completion.
Write the production acceptance criteria first
Describe what starts a case, what must be produced, and which actions the system may take. Separate model quality from business completion: extracting an address correctly does not mean an order was accepted by the destination system. Define both kinds of success.
Ask the sponsor, process owner, and delivery team to agree unacceptable failures. A harmless wording issue and a wrong customer update should not share one average quality score. Keep the acceptance criteria visible as the implementation changes so the team does not gradually redefine success around what the prototype already does.
Use evidence-based release gates
These gates are a proposed delivery checklist. Their depth should match the consequences of an error, and each gate needs an accountable owner.
| Gate | Evidence before proceeding |
|---|---|
| Feasibility | Approved access and representative source data |
| Quality | Expected outcomes on normal, ambiguous, and failure cases |
| Integration | Confirmed writes, duplicate handling, and recovery |
| Operations | Review queue, pause control, and support ownership |
| Acceptance | Operators complete real tasks and understand exceptions |
Start with observation, then limited action
Where practical, run the workflow alongside the existing process before letting it act. Compare its proposed outputs with what operators actually do. Investigate differences instead of assuming the historical process or the model is always right.
Then enable a limited, agreed set of cases. Keep a route back to manual handling and a way to stop new actions. Expand only after the operating team can handle exceptions and the measurements support the next step. Avoid expanding volume and action authority at the same time; it makes failures harder to attribute.
Plan for the second week after launch
Someone needs to inspect failed cases, update changed integrations, and decide whether a new model or prompt is safe to release. Preserve a record of what ran so incidents can be traced to the relevant version. OpenAI’s evaluation guidance describes structured testing for variable model behavior; the buyer’s requirement is evidence that changes are tested against the actual task.
Agree a review cadence for business outcomes, including total handling time and operator corrections. A system that is available but quietly adds rework needs intervention just as much as one that stops running. Support should cover that distinction.
Common questions
Can a pilot be deployed as-is?
Only if it already meets the production requirements. Inspect integrations, access boundaries, failure handling, operator acceptance, and support rather than assuming the pilot must be rebuilt or is ready.
What most often controls the schedule?
For your project, identify dependencies such as access approvals, representative data, system changes, and operator availability. Ask for dates tied to those dependencies rather than a generic AI delivery timeline.
Sources & further reading
Primary documentation for the technical points cited above. The decision frameworks and illustrative examples are PlatformBridge’s editorial guidance.