Few hundred autonomous trucks by 2027 as AI reshapes freight

By the end of 2027, a few hundred autonomous trucks could operate across the southern United States, Gartner senior director Nathan Lease said. The deployment would put driverless equipment into regular freight service, but it would not materially change national truck capacity.

That limit matters because software can change dispatch and procurement work before driverless fleets change linehaul supply. The nearer-term commercial gains sit in exception handling, carrier selection and network planning.

What must change beyond visibility?

For decades, transportation managers asked systems where freight was, often after calls and manual tracking. GPS, telematics and real-time transportation platforms shortened the search and put shipments on live maps; Amazon’s delivery expectations turned fast, visible shipments into an everyday requirement.

Location alone now falls short. Shippers expect systems to flag problems earlier, rank them by consequence, handle carrier follow-ups and adjust delivery schedules. AI, more precise analytics and automation extend freight management beyond a moving dot on a map.

“Visibility in and of itself is just a means to an end, because you need visibility to start taking actions,” Lease said. “But just knowing that the truck is going to be late doesn’t do anything. The real question is: Can you act on that information?”

The operating standard now reaches SKU level. Lease said companies want to know what sits inside each container or trailer, how upstream delays affect downstream commitments, and which decisions require changes to production planning or customer service. Technology now supports procurement, carrier management, routing and planning, while autonomous trucks move from pilots toward deployment.

Can AI identify questions managers miss?

MIT Center for Transportation & Logistics research scientist Angi Acocella interviewed about 20 transportation practitioners for a white paper on digital technologies in transportation. She found that most companies using AI still rely on it to monitor operations and answer specific questions.

She expects AI agents to suggest what managers should examine first, including questions they did not know to ask. That shifts the tool from a query system toward an exception detector, though managers should not mistake anticipation for authority.

Acocella expects people to retain final decisions when large sums of money or carrier relationships are involved. Her caveat is central: autonomous issue detection should expand, not outsource accountability for consequential freight decisions.

What will driverless trucks change?

Autonomous trucks have spent years in testing and pilot programs, and Lease expects 2027 to bring a more visible presence on U.S. highways. Early expansion will concentrate across the southern half of the country.

Waabi, Aurora, Kodiak, PlusAI and Gatik are preparing to expand fleets. Lease sees a few hundred autonomous trucks operating in the southern U.S. by end-2027, still a small share of the national fleet, but more driverless equipment in regular freight operations than shippers see today.

The larger network effect comes later. As deployment reaches more routes, autonomous capacity may become another option in freight-network planning, rather than an immediate expansion of national linehaul capacity.

Can AI reduce network noise?

Managers oversee hundreds or thousands of shipments, and most do not need human intervention. “Most of the time just 5% to 10% of the shipments actually need your attention,” St. Onge Co. systems project manager Brandon Hamilton said. AI can identify possible issues and rank them above routine network noise.

That lets planners focus on late loads and carrier problems rather than shipments moving to plan. Hamilton expects the benefit to grow with freight volume: AI could let companies handle more loads without matching staffing growth.

The source gives no productivity ratio or staffing benchmark, so the scale case remains a direction of travel, not a measured operating result.

Where can automation add value?

Predictive analytics already work well on high-volume, consistent freight lanes. The harder opportunity is lower-volume, irregular traffic, where exceptions, week-to-week volatility and one-off pricing and capacity decisions increase manual work.

Acocella said 80% of lanes are irregular and low-volume, yet those lanes represent 20% of volume and spend. Autonomous procurement could help managers decide how much effort to put into pricing, whether to use spot capacity and whether a lane belongs in a procurement event, narrowing options before human intervention.

For shippers and forwarders, that could reduce manual pricing and sourcing outside the core network. The source does not quantify the labor savings or name the tools that would deliver them.

Can procurement react before reports?

Procurement teams have relied on reports that reveal carrier failures after the fact. Lease expects advanced analytics and workflow automation to shorten that lag, with the potential to save manager time and money. Transporeon and Leaf Logistics are building both capabilities into transportation procurement.

If a carrier’s tender acceptance falls below a company-defined threshold, the system can open a procurement event and issue an RFP for affected lanes instead of waiting for a weekly report. That gives shippers an earlier chance to rebid affected lanes and gives carriers faster notice that performance has triggered sourcing action.

Lease expects systems to catch falling acceptance or rising spot-market costs days or weeks earlier, then trigger sourcing action. No acceptance threshold, implementation timetable or savings figure appears in the source, so those commercial gains remain unproven.

Can predictions prevent disruption?

Weather events, civil unrest and other disruptions can force freight reroutes and inventory changes. Hamilton says predictive analytics now helps managers decide earlier, though the technology made its biggest gains five or six years ago and has improved in smaller increments as it matured.

In a hurricane scenario, AI could combine a developing storm with transport and inventory data, then recommend another warehouse, such as one in Louisiana or Georgia. The advice gives teams time to redirect freight and revise stocking plans before disruption spreads.

He expects predictions to become more accurate, but the source offers no error rate or named case study. For shippers, AI can support a decision, not replace one; freight managers still choose the reroute and inventory response.

By end-2027, the measurable test will be whether a few hundred southern U.S. autonomous trucks and exception-focused software produce repeatable gains without transferring final decisions about money and carrier relationships to software.