A service specialist receives an application that an automated workflow has flagged for review. The specialist believes the underlying classification is wrong, but the case record doesn't say whether they can correct the data, change the recommended action, or authorize an exception. The company has successfully brought a human into the process and left the most consequential question unanswered.
This is where a broad commitment to human oversight needs an operating design. A person can understand a case well and still lack the authority to resolve it. They might also have authority to make one correction while lacking permission to change the rule that produced the exception.
In a customer-facing workflow, those distinctions shape both risk and effort. A reviewer who can't make the required decision has to send the case elsewhere. A reviewer who assumes more authority than they hold can cause an unsupported action. In either situation, the customer may be left waiting while the organization reconstructs what happened.
The management problem becomes harder when reporting compresses the work into “escalated” and “resolved.” Those labels don't establish whether anyone accepted the decision task, which judgment they made, or whether a subsequent system action produced the intended result. A team can improve its routing time while leaving the difficult work untouched.
Consider a fictional application with a proof-of-address mismatch. The specialist can inspect the source document and make a permitted correction. If the classification itself is disputed, however, the workflow needs a decision from someone authorized to adjudicate that question. Until that happens, the case should remain held with a named owner and a next review time.
NIST's AI Risk Management Framework includes defining roles for human-AI configurations and oversight. My interpretation is that the practical unit of that work is a specific decision: who can make it, what evidence they need, and which actions their decision permits. A broad role description rarely provides enough detail at the moment a case changes hands. NIST AI RMF 1.0, GOVERN 3.2
For a leader, one useful test is to take a recently escalated case where a person challenged the automated recommendation and reconstruct the decision from the record. Could another qualified reviewer understand the evidence, authority, reason, and permission for the next action? If the explanation depends on finding the employee who handled it, the organization has a gap in how it preserves judgment.
This also changes what deserves attention before a workflow expands. Teams need to know whether people can challenge a recommendation, whether disputed facts receive a proper adjudication, and whether restarting the workflow can repeat an action. A green escalation metric provides little assurance on those questions.
Week 21 of AI, Not KPI develops this part of the Action System sprint. The complete issue includes the free argument and a practical diagnostic, followed by a paid workbook with a completed example, a decision deck, an operating guide, a workshop and pilot guide, and prompts. Read the complete issue on Substack: www.ainotkpi.substack.com.
