Insights
Who Should Approve an AI-Recommended Action?
DeepSAS Editorial ·
The person who owns the operational consequence should approve an AI-recommended action, within a clearly defined authority limit. Model confidence is not permission. Before connecting a recommendation to a live workflow, decide who can accept it, who can stop it, and what evidence they need.
Separate the recommendation from the permission
An AI system might suggest a response, classify a request, or propose a configuration change. Those outputs do not all carry the same consequence. Sending an internal draft for review is different from sending a message to a customer or changing a production system.
NIST's AI RMF Playbook calls for documented risk-management roles and clear lines of communication in GOVERN 2.1. The approval design below is our editorial recommendation, informed by that principle; it is not a NIST certification or prescribed checklist. NIST AI RMF Playbook: GOVERN 2.1, roles and responsibilities.
Write an approval rule before enabling the action
- Name the action precisely: what changes, in which system, and for whom?
- Identify the accountable business owner and a trained backup, rather than an unnamed 'human in the loop'.
- Set the boundary: permitted scope, required evidence, and conditions that require escalation.
- Give the approver a real stop option. If the evidence is missing or the case is outside scope, pause rather than default to approval.
- Record the recommendation, the evidence consulted, the decision, and the person or approved policy that authorized execution.
Match the control to the consequence
| Illustrative action | Suggested approval pattern |
|---|---|
| Prepare an internal draft | The recipient checks the draft before using it. |
| Send a customer-facing response | A responsible service owner approves exceptions and sensitive commitments. |
| Change a production setting | Use the existing change authority, testing evidence, and recovery procedure. |
These are illustrative patterns, not universal risk classifications. A routine-looking action may become consequential because of its audience, data, timing, or cumulative effects. Use the organization's established policies and specialist advice where needed.
Test whether oversight is usable
Hypothetical example: an assistant proposes changing the routing of support requests. A reviewer sees only '92% confidence'. That is insufficient to judge the operational effect. A more useful review shows affected queues, sample cases, expected exceptions, and how to restore the previous routing.
Before launch, rehearse a rejected recommendation and an unavailable approver. If the workflow cannot handle either, the approval step is a label rather than an operational control. Review the arrangement after meaningful changes to the model, data, or workflow.
References & further reading
Company editorial prepared with AI assistance. Sources and technical checks do not constitute independent expert review.