01
Choose the right workflow
A repetitive task is not automatically a good candidate for automation. First observe how often it occurs, how much time it actually takes, the errors it produces and the number of exceptions. A rare or poorly defined operation may cost more to automate than to handle manually.
Choose a workflow with a clear trigger, identifiable inputs and a result you can verify. Involve the person who knows the day-to-day work: they should be able to explain cases that fall outside the normal path.
02
Define rules and exceptions
Write down the journey from start to finish: triggering event, required fields, checks, expected action and confirmation of success. Specify what happens when data is missing, a duplicate arrives or a connected service fails to respond.
Keep sensitive or ambiguous decisions subject to human approval. Document which permissions the automation needs and how to revoke them. This framing prevents manual effort from simply moving to the correction of invisible errors.
03
Test and monitor
Run a pilot on a limited volume. Test a normal case, a duplicate, an input error and an outage of the connected service. Each run should leave a record showing what succeeded, failed or needs intervention.
Provide a useful alert, an owner for resolution and a way to fall back to manual handling if the workflow stops. Automation without monitoring can look reliable while requests go unanswered.
04
Measure the result
Compare processing time, manual rework, errors and pending requests before and after the pilot. Include maintenance time and tool costs: the number of automated actions alone does not establish the value created.
- Start with a stable, observable workflow.
- Define exceptions before launch.
- Monitor failures and keep a fallback.
- Measure work actually avoided after maintenance.
