1. Define the business question
Choose a process with a clear owner and a measurable problem. Record how it works today, where people lose time and what a useful improvement would look like. Include the cost of reviewing AI output in that assessment. A faster draft is valuable only if the overall task becomes easier or better for the people responsible.
2. Scope a pilot around real examples
Select representative tasks and agree which data the pilot may use. Define the required integrations, expected outputs and boundaries of the experiment. A focused pilot should answer a decision: whether this use case is worth developing further. It should also reveal dependencies such as missing documentation, inconsistent records or unclear ownership.
3. Evaluate before expanding
OpenAI recommends task-specific evaluations and ongoing testing as applications change. For your project, agree examples of acceptable and unacceptable results, then assess quality alongside response time and operating cost. Include difficult and incomplete requests. Use what you learn to refine the workflow and decide whether the result is ready for a wider group.
4. Prepare for ongoing operation
Before rollout, agree who supports users, reviews issues and maintains connected data. Discuss access management, monitoring and how changes will be tested. Model behaviour and business requirements can change, so an AI application needs a maintenance plan. We can scope that work alongside development, with responsibilities and support arrangements agreed for your project.
Source and further reading: OpenAI: evaluation best practices