Start with a task you can describe
Choose a workflow with a recognizable trigger, a clear set of inputs, and a defined next action. “Improve customer service” is a goal. “Classify an incoming request and prepare a reply for review” is a task you can examine and test.
Check whether simpler rules are enough
If a task can be expressed through stable conditions, conventional automation may be easier to maintain. AI becomes relevant when the input varies: natural-language requests, documents, or information that needs interpretation. Use it for the uncertain part of the workflow, rather than adding it to every step.
Make the boundaries explicit
List the information the system may access, the actions it may propose, and the actions it may execute. Separate drafting from sending, recommending from approving, and identifying a record from changing it. Decide who receives exceptions and what happens when no one responds.
Build a small, representative evaluation set
Collect examples the team is authorized to use, including ambiguous requests, incomplete information, and cases that should be rejected. Have a knowledgeable person define acceptable responses. Compare the system against those examples before connecting it to consequential actions.
Measure usefulness, not just activity
Track whether a reviewer accepts a suggestion, how often they need to correct it, and whether the total time spent—including review—improves. Record failure modes as well as successes. A high number of generated answers tells you little about whether the workflow helps.
Agree a first-release checklist
Before launch, identify the workflow owner, approved data sources, review gates, fallback route, monitoring method, and stop condition. Start with a limited scope. Expand only when the evidence and the people responsible for the process support it.