1. Look for repeated operational friction
Useful candidates often appear where people copy information between systems, chase routine approvals, reconcile the same records or repeatedly answer status questions. Frequency alone is not enough; the delay or error must matter to the operation.
2. Score value and feasibility separately
Estimate time saved, avoidable errors, cycle-time improvement and control benefits. Then assess data quality, rule stability, system access and exception complexity. A high-value workflow may still need process cleanup before automation.
3. Design the exception path first
Automation is dependable when it knows when to stop. Define validation rules, retry logic, alert ownership, audit history and the human decision required for each important exception.
- What can be processed automatically?
- What requires review?
- Who receives the exception?
- How is the resolution recorded?
4. Measure the complete workflow
Track end-to-end cycle time and exception rate, not only bot activity. An automation that moves a bottleneck downstream has not improved the operating result.
Start small, design for scale
Choose a bounded workflow with visible value, instrument it properly and use the result to improve the next automation candidate.