AI in Logistics: From Predicting Delays to Resolving Exceptions

Connect the alert to accountable action

AI can support logistics teams by turning an operational exception into a reviewable course of action: identify the affected movement, gather the relevant constraints, propose a response and track the approved follow-up. The first useful application is often one recurring exception with a clear owner, rather than an attempt to automate the whole operation.

Consider a vehicle-transfer business. A car is ready for collection, but its assigned transporter will miss the agreed handover window. The dispatch team needs to check another carrier's capacity, the receiving location's hours and the customer's availability. Predicting the delay is useful. Resolving the consequences is the work that follows.

That distinction creates a practical way to evaluate AI: does it help the team reach a sound decision and complete the handoffs that decision requires?

Start with an exception, not a dashboard

A logistics dashboard may already show late arrivals, unassigned jobs or missing handover evidence. Another alert adds little if nobody owns the next step or has the information needed to take it.

Pick an exception that occurs often enough to evaluate and has a recognizable resolution. A missed pickup window, incomplete vehicle condition record or receiving-site closure could be suitable. Avoid combining them into one pilot before the team understands their different constraints.

Trace how the chosen exception is handled today. Identify who detects it, which systems they consult, who can approve a change and how completion is confirmed. Include phone calls and messages that sit outside the main platform. They often explain why a case appears resolved in one system while work remains elsewhere.

Build a dependable event record

The workflow needs a common understanding of the movement. For vehicle transfers, that may include the job and vehicle identifiers, collection and delivery locations, agreed windows, latest verified status, assigned resource and the time and source of each update.

Distinguish an estimate from a confirmed event. “Expected at the yard” is different from “arrived at the yard,” and neither confirms that a vehicle is ready to load. Keep those states separate in the data and in the language shown to operators.

GS1's EPCIS guidance provides a useful reference for shared event meaning: the objects involved, timing, location, business context and relevant observations. It also recommends designing event information around what the consuming business application needs to understand. Adoption of EPCIS itself depends on the operation and its partners; it is not a prerequisite for every vehicle-transfer pilot.

• GS1: EPCIS and CBV implementation guideline

Before introducing AI, resolve inconsistent identifiers, missing timestamps and unclear status ownership in the pilot's scope. An assistant cannot reliably coordinate a transfer when two systems disagree about which job is being discussed.

Use AI where interpretation is needed

Some steps should remain straightforward rules. A confirmed site closure should prevent a delivery proposal for that site and period. A resource without the required capacity should not be offered as available.

AI may help interpret an unstructured update, assemble related evidence or propose the next investigation step. An agentic design may choose among tools based on what it finds. Anthropic distinguishes that flexibility from predefined workflows and advises starting with the simplest adequate approach.

• Anthropic: Building effective agents

Combine these deliberately. Let the model summarize the situation and prepare alternatives; use authoritative scheduling and permission checks to validate them. An attractive explanation is insufficient evidence that an alternative movement is feasible.

Design the handoff from proposal to execution

The following scenario illustrates a delayed car collection. It is a proposed workflow, not a description of an existing H10 or Bridges client deployment.

If capacity cannot be confirmed, the workflow should create a follow-up for an operator. It should not turn a tentative availability message into a firm booking. If the customer must agree to a new window, that agreement is a separate dependency rather than an assumed outcome of dispatcher approval.

The approved decision may require updates to the dispatch platform, customer communication and receiving-site schedule. Record which updates succeeded. If one fails, show the incomplete state and its owner instead of announcing that the exception is resolved.

Keep control at the points that change a commitment

Define which actions the workflow can prepare and which it can execute. A first release can assemble the exception brief and draft updates while operators retain responsibility for changes to resources, schedules and customer promises.

Where execution is later enabled, grant narrowly scoped tool permissions. Check the latest operational state immediately before applying the approved change. A resource assigned elsewhere after the proposal was prepared may no longer be available.

Design repeated requests so they do not create duplicate jobs or send the same notification multiple times. Preserve an audit record linking the original event, proposal, approval and confirmed updates. Give operations a clear pause control and a manual continuation process when an integration becomes unavailable.

These controls are part of the service experience. A customer receiving contradictory arrival messages sees an unreliable business process regardless of how advanced the underlying model is.

Evaluate on a real operating slice

Use one team, one exception type and a defined set of connected systems for the first evaluation. Choose historical cases covering normal operation, missing information, conflicting updates and events received out of order.

Compare proposed responses with the decisions experienced dispatchers would make. Ask them to inspect the evidence, not just rate whether the text sounds reasonable. Record why a recommendation is rejected: wrong status, incomplete constraints, weak communication or an option the operation cannot fulfil.

Next, run alongside the existing process without automatically changing schedules. Test handovers between shifts and cases that remain open beyond a working day. A workflow that performs well during a demonstration may still lose ownership when the next team takes over.

Measure resolution without hiding incomplete work

Agree definitions before measuring improvement. Track time from verified exception to an approved decision, time from decision to confirmed operational updates, reopened cases and reviewer effort. Report unresolved cases at the agreed cutoff as well as completed ones.

Segment by exception type and operating conditions. A batch of easy cases should not conceal deteriorating performance on the cases that affect customers most. Faster approvals also do not establish lower transport costs or better asset utilization without separate evidence.

Review whether the workflow reduces calls made purely to reconstruct information, while preserving useful conversations that resolve uncertainty. The aim is a clearer operating picture and accountable follow-through, not simply fewer human interactions.

What should a logistics leader do first?

Choose a recurring exception, name its owner and map the evidence needed to resolve it. Define what counts as complete, including the customer and partner handoffs. Then decide which steps need integration, rules or AI assistance.

Bridges' Intelligent Automation & Agentic AI and Application Development services connect operational workflows and enterprise systems. Talk to us about the exception your dispatch team spends too much time coordinating.

• Intelligent Automation & Agentic AI
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• Talk to us about the exception your dispatch team spends too much time coordinating

About this article

Developed from Mohamed Elnahas's original article on AI in logistics, with a new focus on exception management. Unverified market statistics, savings estimates and product claims have been omitted. Vehicle-transfer scenarios are illustrative and are not presented as achieved client results.

• original article on AI in logistics