Start with the work, not the model
AI and automation should be tied to a specific task, decision, handoff, or response pattern. The best starting point is the work people repeat often, where the rules and review points can be written down.
- Define the workflow before choosing the tool.
- Identify what data the AI or automation needs to read or update.
- Confirm which steps need human review.
- Set a fallback path when the workflow cannot complete.
Prepare data and access
AI fails quickly when it has poor data, excessive access, or no operating rules. Before launch, decide what information can be used, who can trigger the workflow, and what should never happen without approval.
- Review customer, financial, operational, and staff data access.
- Separate draft, approval, and final action steps.
- Log outputs and changes where the business needs traceability.
- Limit permissions to the work the system is meant to perform.
Own it after launch
AI and automation need care after release. The business should know who reviews performance, fixes errors, updates prompts or rules, and responds when the workflow produces the wrong result.
- Assign an owner for monitoring and changes.
- Review failure cases and user feedback after launch.
- Keep instructions, integrations, and access settings documented.
- Decide when the workflow should be paused or changed.