01
Separate preparation from decision-making
AI can organize notes, classify requests, and draft a summary. A person should make the decisions that carry customer, safety, financial, or reputational consequence.
- Label each workflow step as prepare, recommend, approve, or send.
- Keep source material available at the point of review.
- Do not allow a generated output to hide missing information or uncertainty.
02
Use approval gates that fit the risk
Not every action needs the same review. The important work is defining a clear threshold before the workflow is live, rather than discovering it after a bad handoff.
- Require approval for pricing, schedule changes, safety guidance, and sensitive customer data.
- Route low-confidence or out-of-pattern inputs to a review queue.
- Record who approved an external commitment and what information they saw.
03
Review exceptions as process evidence
Exceptions are often the fastest way to learn where a workflow is brittle. A short, regular review helps the team improve prompts, source material, and routing rules without pretending the system is perfect.
- Review a sample of corrections and escalations each week.
- Distinguish a data-quality issue from a policy or training gap.
- Change one rule at a time and test it against known examples.