Set AI risk boundaries by workflow

What this solves

Teams either over-trust AI or avoid using it because nobody has defined what authority AI has at each step of the workflow.

• People disagree about whether AI can draft, send, classify, approve, escalate, discount, advise, or close out work.
• Review expectations change by person, customer, value, channel, or service line.
• The first workflow feels risky because the boundary between assistance and authority is vague.

What AI is good at here

• Classifying workflow steps by support type: summarize, draft, classify, route, recommend, action, or human only.
• Producing a first-pass authority table for human review across normal, exception, and high-risk scenarios.
• Highlighting steps that need approval before rollout because they affect money, care, safety, legal obligations, customer trust, or reputation.

What AI should not own

• Humans must set final risk tolerance, accountability, escalation rules, and customer-facing authority.
• AI should not define its own authority, expand scope silently, or move from draft to action without an explicit human-approved rule.

Best first implementation

• Choose one workflow and list each trigger, input, decision, output, customer touchpoint, system update, and exception path.
• Mark each step as AI can summarize, AI can draft, AI can classify, AI can recommend, human must approve, or human only.
• Review the authority table with the workflow owner and test it on real examples before any customer-facing rollout.

What good looks like

• The team knows exactly what AI is and is not allowed to do in that workflow.
• Review becomes faster because the approval standard is explicit.
• Rollout trust improves because higher-risk steps remain visibly human-owned.

Common mistakes

• Using one broad AI policy for every workflow instead of authority rules tied to actual steps.
• Skipping boundaries because the first use case feels simple, then discovering edge cases after customers or staff are affected.