Start with one business measure
Define the improvement you need: less time per task, a better customer outcome or a lower cost to serve. Record a baseline, a target and an owner. A popular tool is not evidence of a return.
Check answers after every meaningful change
An AI answer that was useful yesterday can change when the model, instructions or company data changes. Test realistic examples before releases and keep a review path for uncertain answers. This is what technical teams mean by regression testing.
Manage cost per completed task
Count the full cost of a workflow, including repeated attempts and human review. Set spending limits and track usage by business activity so increased adoption does not hide deteriorating economics.
Keep company knowledge current
Agree who updates the information the AI uses. Old policies and incomplete customer records can create confident but unhelpful answers. Source visibility and content ownership matter.
Define what AI may do on its own
Separate recommendations from consequential actions. Use explicit approval points, appropriate access and a practical way to stop or reverse a workflow.
Plan adoption as part of delivery
Train the people who will use the tool and measure whether it saves them time. A release becomes valuable when it improves the work, not simply when it goes live.