Define a measurable outcome

An AI strategy starts with a question about work: what outcome should improve, for whom, and subject to which constraint? 'We need an intelligent assistant' selects a solution before defining the problem. 'Reduce request resolution time while preserving answer accuracy and the right to challenge a decision' defines something testable. Specify the unit of measurement, such as a closed request rather than a message sent, and define when the clock starts and stops. Compare requests with similar complexity, channels and periods; a different case mix can create an apparent improvement. A baseline includes volume, completion time, rework, service cost and error distribution across groups. An average alone can hide long waits for a small group.

Write a testable causal hypothesis: if the system helps staff find approved instructions, search time should fall; resolution time may then improve provided review and rework do not increase. This chain shows where the hypothesis can fail. Name a realistic alternative without AI, such as better knowledge indexing or a simpler form, because the decision allocates resources among available options. Identify the outcome owner and data owner. Separate activity measures, such as usage, from outcome measures, such as correct first-time resolution. An improvement observed after launch is not automatically caused by the system; seasonality, training or a team change may provide another explanation.