The operating test is whether a warning leads to a verified resolution.
EXECUTIVE ANALYSIS | H. Max Ottesen
AI-generated conceptual editorial illustration. Fictional people and setting; not documentary evidence. Illustrated night operations room focused on one open case and a supervisor checking evidence.
A warning that nobody owns is unfinished work. For an exception-management pilot, I would start by defining what a resolved case looks like and work backward from there. Detection would be one step in the process, rather than the headline result.
This article proposes a way to evaluate an AI-supported exception queue. It does not report a deployed control tower or measured reduction in failures.
Give each warning a response
The UK Health and Safety Executive’s alarm guidance says alarms should be relevant and have a defined response. [1] It concerns process-plant alarms, so applying that principle to a logistics queue is an analogy, not direct evidence of effectiveness.
For a proposed queue, I would require the issue, supporting record, responsible person, next action and escalation condition. A case should remain open until the responsible person records what changed and verifies the relevant operating status. Clicking “done” should not erase the evidence.
Imagine a hypothetical customer document missing before a delivery. The assistant could assemble the request history and draft a follow-up. It should preserve the original messages so the reviewer can check whether a promise was actually made. NIST identifies confidently false generated content as a risk. [2] An invented commitment would turn an administrative delay into a new problem.
Count the work the queue creates
I would distinguish alerts raised, useful alerts, missed cases, duplicate cases and cases closed without sufficient evidence. Review age and recurrence alongside time to closure. Closing something quickly is not an improvement if the same unresolved issue returns on the next shift.
Protect the comparison from easy distortions. Keep the definition of a case stable. Check whether staff are splitting or merging cases differently. Include the checking time imposed on supervisors and the people asked to respond.
An alert queue may be the wrong intervention if the real constraint is unavailable repair capacity or authority to make a decision. Better prioritization can help choose what waits; it cannot make that constraint disappear.
Leave room for disagreement
Operators should be able to challenge a category, add missing evidence and explain why a suggested action would fail. Keep those corrections for review. They may reveal that a general category hides several very different operating situations.
My recommendation is to test a small number of consequential exception types first. Avoid adding warnings merely because data is available. If a rule or existing work-order system already supports the response reliably, compare the AI addition against that baseline.
Visibility is useful when it helps the right person take an appropriate action and confirm the result. The pilot should be judged on that chain. A crowded screen proves only that the screen is crowded.
SOURCES & EVIDENCE
[1] UK Health and Safety Executive (Page date 2024-10-29; accessed 2026-10-17). Alarm management. https://www.hse.gov.uk/humanfactors/topics/alarm-management.htm. Locator: Key principles of alarm management. Supports: Relevant alarms with defined responses; analogy explicitly limited. Limitations: Process-plant alarm guidance is used by analogy. It is not a warehouse-AI trial. Checked: 2026-10-17.
[2] Chloe Autio et al. / NIST (2024-07-26). Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile. https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.600-1.pdf. Locator: NIST AI 600-1, section 2.2 and action MS-2.5-003. Supports: Risk of confidently false generated summaries. Limitations: Identifies risks and suggested controls; it does not validate these proposed workflows. Checked: 2026-10-17.
HOW THE CONCLUSION WAS REACHED
A defined response is a defensible design principle from alarm management. Generated-summary risk makes source checking necessary. The conclusion that an exception queue should be evaluated through closure is executive analysis, not a demonstrated causal effect.
LIMITATIONS
No field evaluation of an AI exception queue was supplied. Process-plant guidance does not independently validate logistics triage. Capacity, incentives, case definitions and reviewer workload could dominate any result.
© 2026 H. Max Ottesen. All rights reserved.
← All articles & insights