For the past few years, transportation executives have had to manage through one disruption after another: excess capacity, collapsing rates, driver shortages, rising fuel costs, fraud, cargo theft, shifting regulations, and increasingly demanding customers.

What struck me during the transportation management discussion I moderated at the Descartes Innovation Forum was how quickly another issue has moved to the center of the conversation: technology, and particularly AI, is becoming part of the operating model rather than a separate innovation initiative.

The freight market itself remains complicated. Demand remains relatively soft, yet capacity has tightened significantly as trucking companies and drivers have exited the system. That creates an unusual dynamic in which rates can increase without a corresponding demand surge. It also changes the shipper conversation from simply negotiating lower rates to ensuring access to dependable capacity and managing greater pricing uncertainty.

For shippers, the response increasingly starts upstream. Better forecasting, inventory optimization, dedicated transportation, and network planning can reduce exposure to the spot market and prevent costly expedites. The objective is to make an emergency become an inconvenience. That is a useful way to think about transportation management because some of the largest transportation costs are created long before anyone tenders a load.

Technology is also becoming an increasingly important tool for protecting margins. One example discussed involved a repetitive process performed approximately 750,000 times each month. When converted into labor, the activity consumes roughly 1,500 employee hours every day. Automating work at that scale is not a marginal productivity improvement. It changes the economics of the operation.

Agentic AI was therefore not discussed as something sitting five years over the horizon. The conversation included active use cases involving workflow automation, voice agents, email automation, decision support, and software development. The challenge increasingly becomes deciding what should be automated, where humans should remain in the loop, and how quickly organizations can absorb the rate of technological change.

One of the most striking examples involved a proprietary transportation management system containing more than 30 million lines of code and accumulated over approximately 20 years of development and acquisitions. A 12-person team was given six weeks to recreate the system using AI-native development methods and reportedly replicated the core system in that period.

Whether every organization can reproduce that result is almost beside the point. The more important message is that assumptions about software development timelines, technical debt, and what constitutes a realistic transformation project may need to be reconsidered.

At the same time, the discussion was hardly techno-utopian. Fraud, cargo theft, cybersecurity, insurance exposure, driver qualification, and litigation all figured prominently. Transportation may be becoming more automated, but the consequences of a bad decision remain very physical.

That tension may define the next stage of transportation technology.

AI can increasingly do the work. The harder questions will be deciding which work we want it to do, which decisions still require human judgment, and how quickly our organizations can adapt to what is suddenly possible.

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The AI discussion in logistics is quickly moving beyond copilots, chatbots, and better search. At the Descartes Innovation Forum today, Descartes demonstrated something considerably more interesting: agents working across multiple logistics applications and organizations to execute parts of a representative end-to-end supply chain process.

That distinction matters.

Descartes described one of the persistent problems in logistics as the “swivel chair tax.” A shipment may move physically from origin to destination, but the information required to manage it still moves through transportation systems, compliance applications, carrier portals, email inboxes, messaging platforms, spreadsheets, and people. The applications themselves are often perfectly capable. The problem is everything that happens between them.

Descartes’ emerging answer is what it calls its Agent Control Plane. In the keynote demonstration, an order moved through four representative companies and 15 Descartes products, with 28 individual process steps occurring behind the scenes. Agents performed tasks ranging from compliance checks and information gathering to disruption detection and replanning, while the underlying Descartes applications continued to perform the operational work they were built to do.

But the most important part of the demonstration may have been what the agents did not do. At several points, humans remained responsible for consequential decisions. When a compliance issue appeared, the agent gathered the relevant information and escalated the decision. Later, when a disruption created an opportunity to rebook transportation at an 8% savings, the alternative carrier carried a trust score of just 32. The agent surfaced the option; the human rejected it.

That is a much more realistic model of logistics automation than the idea of simply turning operations over to autonomous AI. Descartes summarized the philosophy particularly well: autonomy is a dial, not a switch.

This is where agentic AI starts becoming operationally interesting. Consider what normally happens when an international shipment is disrupted. Someone discovers the change, someone determines which shipments are affected, and other people begin checking capacity, appointments, customer commitments, carrier options, and downstream consequences. Emails and phone calls start moving among multiple organizations, and by the time the problem is fully understood, hours may have passed.

In the Descartes demonstration, agents detected the disruption, evaluated its downstream impact, investigated alternatives, and coordinated information across the participating companies. Instead of simply alerting the shipper that something had gone wrong, the system could potentially deliver something much more valuable: the problem and the proposed resolution together. That represents a meaningful change in the role of supply chain software.

For decades, enterprise applications have largely waited for people to operate them. Agentic systems introduce the possibility that applications can increasingly initiate work themselves—within defined permissions and with humans inserted at the appropriate decision points. Descartes also emphasized that this is not merely a future concept. The company said it has already executed approximately 3.25 million agent operations and is opening an early-access program for the Agent Control Plane.

Just as important, Descartes is building governance around the model. Agents have identities, actions are logged and attributable, and activity can be reviewed and replayed. That may ultimately prove as important as the AI itself because enterprises will want different levels of human oversight depending on the decision, risk, and business context. They may be very willing to let agents do the investigative work, coordinate routine activities, react to predefined conditions, and bring humans the relatively small number of decisions that actually require judgment.

That was my biggest takeaway from the keynote.

The next generation of logistics automation may not be about removing humans from the process.

It may be about removing humans from all the work they never needed to be doing in the first place.

The post Descartes Takes Agentic AI Into the Logistics Workflow at the Descartes Innovation Forum appeared first on Logistics Viewpoints.

Real-time logistics data has become a foundational capability for global supply chains, but data alone does not resolve a disruption. The operational value emerges when visibility is connected to prediction, prioritization, and the workflows required to change an outcome.

project44’s Movement platform is moving in that direction. The company combines a large multimodal transportation data network with predictive ETAs, analytics, alerts, and increasingly autonomous workflows. Its role is primarily execution-focused: it sits around existing planning and transportation systems and provides current operational context about what is actually happening across the freight network.

That position gives project44 a useful foundation for decision intelligence. A shipment event can be evaluated against order commitments, customer priorities, facility conditions, and downstream risk before an operator is asked to intervene. The more accurately the platform can distinguish consequential exceptions from routine variability, the more effectively it can automate communications, recommendations, and bounded execution tasks.

The challenge for visibility providers moving into decision and action is proving that the automation layer creates measurable operational improvement. Buyers should test data quality, coverage, prediction accuracy, workflow integration, and governance around autonomous actions rather than assuming that more real-time data automatically produces better decisions.

project44 is represented in the Logistics Viewpoints Supply Chain Decision Intelligence MarketMap and Autonomous Exception Management MarketMap. The pairing captures the company’s evolution from visibility toward a broader real-time intelligence and exception-management role.

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