1. Choose a specific business moment
Begin with a user trying to make a decision or complete a task. “Use AI in customer service” is broad. “Draft a response using approved order and policy data for an agent to review” is testable.
2. Define the trusted context
Identify the systems and documents the workflow may use, how current they are and which source wins when records disagree. Retrieval quality and permissions are part of the product.
3. Bound what AI can do
Separate suggestion, decision and action. High-impact actions should have validation and appropriate human approval. Record the source, generated result, reviewer and final action where auditability matters.
- What may the model read?
- What may it propose?
- What may it execute?
- When must a person intervene?
4. Evaluate the workflow, not only the model
Use representative scenarios and failure cases. Measure task quality, correction effort, escalation rate and end-to-end time. A fluent answer is not enough if the workflow creates extra review or hides uncertainty.
Operational readiness
Production AI needs ownership, monitoring, fallback behavior, change control and a plan for model or source-data changes.
AI outputs should be reviewed in proportion to their business, legal, financial and safety impact.