8 Logistics Companies Using AI: Real Examples (2026)

Which logistics companies are using AI?

Flexport, C.H. Robinson, DHL Supply Chain, Echo Global Logistics, Dimerco, XPO, GXO and FedEx provide examples of AI adoption across different parts of logistics. Their applications range from customs review and booking work to operational communications, linehaul planning, warehouse orchestration and trailer loading.

The useful question is not simply who uses AI. It is what work the technology performs, where it is deployed and what evidence supports the result. A system that recommends a plan, an agent that processes an order and a robot that loads a trailer solve different problems. They should not share a single adoption score.

This review brings those examples together so logistics leaders can identify patterns relevant to their own operations. The company summaries below report what the cited sources establish. The separately labeled Shipflow perspectives are our interpretation—not statements from those companies.

Eight examples at a glance

Read each status as a description of the cited evidence, not a claim that every office, shipment or workflow is automated. Source dates and links appear in the company sections.

How to read the evidence

We selected examples with identifiable operational applications and accessible primary-source reporting. Sources were reviewed on September 19, 2026. Each account is tied to its cited publication; it is not a complete inventory of the company’s current AI activity. Company-reported outcomes have not been independently audited by Shipflow for this review.

Disclosure: Dimerco is a Shipflow customer, and its example comes from Shipflow’s own case study. Frank Lin previously led AI and workflow automation at Flexport. The Flexport section relies on public product materials, not confidential information or a claim that Frank led the specific releases discussed here. Inclusion of any other company does not imply a Shipflow customer relationship or endorsement.

Read three things together: the work included, the deployment stage and the measurement. Cumulative task volume is not a completion rate; a percentage for eligible requests is not company-wide coverage; and a pilot is not a network-wide rollout. Where a source does not disclose a comparable result, we do not supply one.

Flexport: AI inside customs and shipment planning

Customs checks and shipment planning depend on connecting detailed records with current operating conditions. Flexport’s Winter 2026 release describes an AI agent that reviews past customs entries for errors and refund opportunities, alongside an optimization engine that evaluates container options using pricing, capacity, timing and utilization data.

The planning recommendations appear in the platform for approval and execution. This is a useful distinction: identifying a better option and authorizing a change are separate steps. The release documents product capabilities, but does not provide a like-for-like completion measure across all of Flexport’s operations.

C.H. Robinson: repeatable shipping tasks at scale

Quotes, incoming orders and appointments create repeated information-handling work. On April 16, 2025, C.H. Robinson reported that its generative AI agents had performed more than three million shipping tasks, including more than one million price quotes and one million processed orders.

The same announcement described established appointment automation, a voice-enabled pilot for obtaining missing carrier updates and a tracking-response model under development. Those are different deployment stages. The three-million figure is a cumulative task count as of that announcement—not three million autonomous shipments, a monthly run rate or proof that every shipment step was automated.

DHL Supply Chain: automate the communication around the work

Appointments, driver follow-ups and urgent warehouse coordination require repeated exchanges between people. In its November 11, 2025 announcement, DHL said its Supply Chain division was already using AI agents for these activities across several regions, handling both phone and email interactions.

The source establishes operational use, rather than only a planned trial. It does not establish that all DHL divisions or communications are automated, and we do not translate its qualitative benefits into a numerical productivity claim.

Echo Global Logistics: reduce inbox and data-handling friction

Echo’s March 11, 2026 market commentary describes continued development of AI tools to reduce manual information search and data entry. It discusses filtering quote and tracking emails, retrieving information for representatives and communication through calls, texts and email.

The commentary puts the scale of the problem at around 60,000 incoming quote and tracking emails per day. That is incoming workload—not a verified count of emails processed autonomously. The article is an executive account of the initiative; it does not disclose a measured touchless completion rate or uniform rollout scope.

Dimerco: touchless completion within a defined booking workflow

Dimerco deployed Shipflow’s Booking Operator for a defined, high-volume part of its booking process. The workflow brings request intake, required checks, in-scope execution and exception handling into one operating sequence.

Shipflow’s published case study reports 99%+ touchless completion among eligible requests within that deployed workflow. This does not describe Dimerco’s entire booking operation, every shipment or every office. It is a workflow-specific result, not a company-wide AI adoption score.

This is first-party customer evidence from Shipflow, not an independently audited comparison. The public case study does not disclose a measurement window or sample size. Those limits matter when comparing the figure with results from another operation.

XPO: improve network and labor decisions

For an LTL operation, efficiency depends on how freight moves through the network and how teams are staffed. XPO’s 2025 sustainability report describes AI-powered linehaul technology supporting better load utilization and fewer miles for the same tonnage.

The report also describes XPO Smart, a labor-planning and analytics suite using data science and machine learning to support managers at individual service centers. These are planning and optimization applications—not evidence of an AI agent autonomously managing every customer request. The cited passages do not supply a directly comparable workflow completion rate.

GXO: connect warehouse intelligence with execution

GXO’s June 26, 2025 launch of GXO IQ described a platform connecting operational data with inventory movement, picking, packing, shipping and staffing decisions. At launch, the company said it was already supporting GXO Direct customers in the United States.

Its August 4, 2026 results announcement reported a move from platform launch to scaled deployment. That supports describing an operating platform rollout, but not assuming coverage of every facility. We do not attribute GXO’s overall financial results to AI alone or present a common productivity uplift where these sources do not provide one.

FedEx: physical AI for trailer loading

Trailer loading combines physical effort with continual decisions about parcel placement and stability. On July 30, 2026, FedEx announced an expanded physical-AI deployment at its Hagerstown Hub in Maryland, moving beyond the earlier pilot site following development and operational testing.

The announcement describes autonomous loading integrated with wider hub processes, including trailer assignment and maintenance. This is a site-specific example of physical automation—not evidence that the entire FedEx network is autonomously loaded. The release does not provide a comparable audited ROI figure for the deployment.

What logistics leaders can learn from these deployments

The strongest lesson is not that every logistics company needs the same AI stack. These examples address different bottlenecks. Our interpretation is that a useful adoption strategy starts with an operating problem, connects intelligence to a real action and expands only when the result can be verified.

Use the following questions to turn industry examples into decisions for your own team. They are a practical framework, not a claim that every featured company follows the same deployment method.

1. 01

   Name the work—not just the technology

   Replace “adopt AI” with a specific outcome: create a valid booking record, collect a missing milestone or prepare a discrepancy for review. A forecast or draft can be useful, but it is not the same outcome as completed execution.

2. 02

   Make one scope measurable

   Choose an office, customer group or request type. Record incoming volume, eligibility, active human effort, turnaround and corrections before launch. Keep the same definitions when comparing the result afterward.

3. 03

   Design the handoffs once, clearly

   Specify where the AI reads and writes, which decisions need approval and who owns exceptions. Confirm successful system updates and provide a recovery path. These controls belong in the workflow design, not in repeated disclaimers around every benefit.

4. 04

   Expand from observed performance

   Review difficult cases alongside successful ones. Add scope when quality and operations support it—not because another company announced a larger number of agents. Released capacity, avoided spending and revenue impact should be measured separately.

Where Shipflow fits

Shipflow deploys AI Operators for logistics companies to execute repetitive operations and customer workflows across existing systems. The focus is digital work: quotations, bookings, documents and data entry, tendering, shipment tracking, invoices, carrier coordination and exceptions.

An industry example can help identify the opportunity. Your own SOPs, system access, operational ownership and baseline determine the deployment. Start with a working session around one real workflow, then agree what completion and success should look like.