Before automating, document the trigger, inputs, systems, decision rules, approvals, exceptions, and owner. Then test duplicates, incorrect outputs, unavailable services, and recovery.
1. Define the boundaries
Name the event that starts the workflow and the exact state that counts as finished. Specify required inputs and where the result is stored.
List the exceptions people handle today. These often reveal the real complexity of the work.
2. Verify the connections
Check API access, rate limits, authentication, and the ability to read or write each required field. Use a test environment when one is available.
Confirm how duplicate records, changed permissions, and deleted data will be handled. Avoid treating a successful connection as proof of a complete workflow.
Request a call3. Decide where AI belongs
Use rules for predictable validation and calculations. Use AI for the interpretation step that benefits from it, then validate the output before it changes another system.
Set a review path for uncertain or consequential results. Make clear who can approve and how a rejected output is corrected.
4. Design for failure
Test repeated requests, partial completion, provider outages, and delayed responses. A retry should not send a second invoice or create another customer record.
Maintain a durable record of work and a way to see what needs attention. Explain failures to the user in terms they can act on.
5. Measure and maintain
Compare handling time, correction rates, and completion against a baseline. Include review time and operating costs in the comparison.
Assign an owner for monitoring, vendor changes, access reviews, and documentation. Use the form below to review a workflow with Redko.
Explore AI workflow automation or more Redko guides.