1. Start with a measurable operating problem
Choose a workflow where repetitive work, information access or exception detection is creating real friction.
Avoid selecting a use case only because the demo looks impressive.
- Time spent
- Response speed
- Backlog
- Error rate
- Information access
- Exception visibility
2. Confirm the data is usable
Identify the data, documents and systems the AI needs. Validate quality, ownership and access before building the workflow.
Poor data can make automation faster but not better.
- System data
- Documents
- Reports
- Permissions
- Data quality
- Retention
3. Define the human control model
Decide what AI can recommend, what it can draft, what it can execute and where a person must approve.
Document exception and escalation handling.
- Approvals
- Permissions
- Review
- Escalation
- Audit trail
4. Integrate with the workflow
AI should sit inside the process employees already use whenever possible rather than becoming another disconnected destination.
Design the handoff between systems, people and AI deliberately.
- Trigger
- Input
- AI task
- Human review
- System update
- Exception path
5. Pilot with a narrow group
Use a controlled pilot to validate output quality, adoption and operating impact before expanding.
Capture failure modes as carefully as successful results.
- Pilot users
- Test data
- Acceptance criteria
- Issue log
- Feedback
6. Measure before scaling
Compare the pilot to the baseline. Scale only when the workflow is producing reliable value and controls are working.
Continue monitoring after expansion.
- Hours saved
- Cycle time
- Quality
- Exceptions
- User adoption
- Operating impact