Start with a repeated task, clear boundaries and a way for your team to judge whether the result helps.
Choose a task you understand
Look for work that happens often enough to matter and has a recognizable beginning and end. A request arriving, a document needing classification or a draft being prepared can be easier to evaluate than a vague ambition to automate a whole department. Ask the people doing the work where time is actually going.
Separate rules from interpretation
Map the steps before choosing the tools. Some steps follow predictable rules, while others require interpreting language or extracting information from varied inputs. Use ordinary automation where rules are sufficient and consider AI for the parts that need interpretation.
Define what happens when it is wrong
Write down the exceptions and the actions that need approval. Decide what the workflow may do on its own, what information it may use and when it should ask a person. Logs and a clear handover path make the system easier for your team to inspect and improve.
Run a contained pilot
Test realistic inputs, including incomplete and difficult examples, before expanding the scope. Compare time spent, correction work and the usefulness of the output against the current process. Keep the next decision simple: improve the workflow, expand it carefully or stop because the evidence does not support it.
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