Before you add AI, understand the workflow.

Start with a task that your team can describe clearly. Who initiates it? What information is needed? What does a useful result look like? If those answers are uncertain, adding a model introduces another uncertainty rather than resolving the original one.

Map the handoffs, exceptions, and checks in the current workflow. Repetitive, well-defined steps may need ordinary automation. Tasks that depend on unstructured documents or contextual interpretation may benefit from AI. The right solution can combine both.

Choose a narrow first release. Give the system access only to the information and actions it needs. Put human review before consequential changes, and make it possible to understand why a result was produced.

Agree on an evaluation set before launch. Include routine inputs, incomplete information, and awkward exceptions. Compare results against an agreed baseline rather than treating a convincing demonstration as proof.

Measure the whole workflow: time to a reviewed result, correction effort, escalation rate, and operational cost. The goal is a useful business process, not simply a successful model response.