1. Measure repetitive work first
For five days, log repetitive tasks such as copying inquiries, confirming appointments or compiling reports. Record frequency, owner and required inputs. Not every repetitive task deserves automation.
2. Map the edge cases
Consider a website-to-CRM lead workflow. What happens when a phone number is missing, a contact already exists, an API fails, or a request arrives twice? Specify a safe outcome for each case before development.
3. Separate rules from AI
Deterministic checks work best for well-defined validation and confirmations. AI can assist with interpreting free-text requests, but sensitive decisions should include human approval.
4. Test and monitor
Use test records. Log the trigger, outcome, and any exception. Measure completion rates and the human effort required to resolve failures.
5. Demand concrete deliverables
A useful audit documents the workflow, permissions, exception handling, acceptance tests and escalation owners—not just a chatbot demonstration.