Start with the workflow, not the model
The useful question is not “where can we use AI?” It is “which operating task or decision is worth improving?”
Define the task
Document who does the work today, what inputs they use, what output they produce, how long it takes and where errors occur.
Set the baseline
Choose measures that matter to the operation: cycle time, accuracy, cost, exception rate, adoption, escalation or another relevant business measure.
Design for failure
AI will not be correct all the time. Define which mistakes are tolerable, which require human review and which should cause the system to refuse or escalate.
Evaluate the full workflow
A model score is not the business result. Measure the end-to-end process, including retrieval quality, human intervention, integration reliability and the outcome produced.
Scale only after use
A credible pilot should be tested with real users and real operating constraints before broader rollout.