Use AI to surface dispatch exceptions, not to own them
Dispatch exception handling looks automatable because the inputs are structured enough to summarize: late trucks, missing documents, weather changes, dock delays, and customer escalation emails. The risk is that the real decision is not the summary. It is the tradeoff between service level, driver safety, carrier relationship, customer promise, and margin. McKinsey supply chain insights is useful here because supply-chain resilience work keeps pointing back to visibility, planning discipline, and operating response rather than pure automation. AI can tighten the intake layer, but it should not make the final reroute decision until the boundaries are explicit.
McKinsey State of AI 2025 is also relevant because the value comes when companies redesign workflows, not when they paste AI onto a fragile process. For dispatch, the redesigned workflow should let AI classify the exception, collect route and customer context, suggest options, and route the decision to a named dispatcher when safety, contractual, or customer-impact thresholds are met.
Do not automate the judgment layer until controls are proven
NIST AI Risk Management Framework gives the right operating sequence: map the context, measure the risk, manage controls, and govern the system over time. For dispatch exceptions, that means mapping which events are safe for auto-recommendation, which require supervisor approval, which require customer confirmation, and which should never execute without a human dispatcher. The first automation gate should be recommendation quality, not autonomous execution.
IBM Institute for Business Value AI capabilities research reinforces the capability point. AI performance depends on data, operating model, adoption, and measurement. If the transportation-management system has stale carrier status, incomplete customer rules, or unclear ownership, AI will make bad decisions faster. The early KPI should be exception resolution quality, not headcount removal.
Build the dispatcher-in-the-loop workflow first
Start with one lane, one dispatch team, and one exception category. Track time to classify the exception, missing-context rate, percentage of recommendations accepted by dispatchers, customer escalation rate, and post-resolution corrections. That evidence tells you where AI can move from drafting options to executing low-risk steps.
Use a QuickStart AI Audit to map source systems and decision rights before build. Then use the AI Opportunity Score to compare dispatch exceptions against safer first workflows.
