A successful demonstration answers whether an idea might work. A production service has to answer harder questions: can people depend on it, can the organisation operate it, and can it be stopped safely?
The transition is not simply a larger deployment. It requires evidence about the real workflow, explicit service ownership and controls that survive routine changes.
1. Compare against the existing workflow
Define a baseline before attributing value to the pilot. Measure the time, cost and quality of the current process, including the work required to review and correct an AI output.
Choose a small number of outcome measures and guardrails. Faster answers are not an improvement if they create more incorrect decisions or inaccessible experiences. Agree which trade-offs are acceptable with the people responsible for the service.
2. Build an evaluation set that resembles reality
Include common requests, edge cases, incomplete inputs and situations where the system should decline to answer. Cover relevant languages and user groups rather than relying only on examples selected by the project team.
Keep a stable test set for regression checks and separate examples for development. Document the limits of the evaluation: good results on one collection of tasks do not demonstrate reliability on every future request.
- Test incorrect but plausible answers, not just obvious failures.
- Measure when a reviewer must intervene.
- Repeat evaluation after a model, prompt or knowledge-source change.
3. Close the operational gaps
Replace shared test accounts with role-based access. Establish rules for handling sensitive data, controlling supplier access and retaining appropriate audit records.
Define limits for usage and expenditure, and test service behaviour when a dependency is slow or unavailable. Treat prompt injection and unsafe data disclosure as concrete test scenarios, particularly when the service can retrieve documents or perform actions.
4. Name the team that will run the service
Identify who owns quality, security, incident response and user support after the pilot team moves on. Agree escalation contacts and the resources available to investigate a failure.
Monitoring should distinguish technical availability from useful performance. A service can respond quickly while giving poor answers. Review a proportionate sample of outputs and feedback without unnecessarily collecting personal data.
5. Release in stages and test the fallback
Start with a bounded audience and a reversible workflow. Set explicit criteria for widening access, pausing the service or returning to the previous process.
Practise the fallback before launch. Users should know when the AI service is unavailable, how to reach a human and what happens to work already in progress. A controlled, evidence-led release is a stronger outcome than declaring a prototype production-ready on the strength of a demonstration.
General practical guidance, not legal advice. Adapt these steps to your organisation and seek appropriate specialist advice.