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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