Supply chains networks span manufacturers, warehouses, shipping lines, trucking fleets, retailers and customers. Recent disruptions ranging from Covid-19 and extreme weather events to geopolitical conflicts in the Middle East and Eastern Europe have exposed the limitations of traditional planning tools. In response, companies are increasingly deploying AI to improve visibility, predict disruptions and, in some cases, make operational decisions without human intervention.
Increasingly, however, the ambition extends beyond prediction and analysis. Some now believe AI can actively manage and optimise supply chains in real time, learning from outcomes and continuously improving performance.
Few organisations have embraced that vision with greater intent than Minnesota-based logistics giant CH Robinson. Founded in 1905, the company now boasts 75,000 customers and 450,000 contract carriers, managing around 37 million shipments, or $23bn worth of freight annually.
Last month, the company launched Lean AI Engineer, which builds on its existing Lean AI Planner to create what executives describe as an “agentic supply chain” – an AI ecosystem capable of continuously learning, adapting and acting across one of the world’s most complex logistics networks.
Jordan Kass, CH Robinson vice-president of managed solutions, tells Computer Weekly that the Lean AI Engineer now effectively closes the loop: “It will run continuously, improve the operation it’s running and heal itself when something breaks – without an alert or a human noticing a problem first. The Lean AI Planner executes in real time while the Lean AI Engineer studies the results, identifies patterns, adapts logic and influences future decisions.”
Kass explains that the company oversees a network connecting trucking operators, ocean shipping companies, airlines, rail and road freight providers spanning manufacturing, distribution, retail and customer delivery centres. Add in a constantly shifting mix of carbon emissions requirements, customs rules and regulatory obligations across hundreds of jurisdictions and the complexity deepens even further. It is precisely the sort of sprawling, interconnected network that would be almost impossible for humans alone to continuously optimise.
Kass says the technology effectively ends the need for separate supply chain intelligence and orchestration tools. “It’s what businesses with complex logistics have wanted for decades.”
The technology now handles 92% of fourth-party logistics shipments globally across trucking, ocean, air and rail, from the moment an order is created through tendering, routing, delivery, exceptions and carrier payment.
“Now we’ve reached the point where our customers have an agentic supply chain – an entire AI ecosystem that continuously thinks, learns, adapts and acts,” CH Robinson CTO Mike Neill tells Computer Weekly.
AI scaling human talent
Sounding a voice of reason amid growing concerns AI is coming for people’s jobs, Kass stresses that CH Robinson’s approach is ultimately about scaling human talent. “This level of premium logistics service has traditionally depended on talented people to manage complexity, make smart decisions day to day and intervene during disruption,” he says. “The problem was that talent didn’t scale.”
The company has changed this by encoding expertise – some 120 years’ worth – into the technology itself. This means shippers can access the same expertise consistently across every shipment, regardless of who is available, what time zone they operate in or how dramatically shipping volumes grow or spike.
“Their team and our team can focus on strategic priorities and driving the best business results,” adds Kass.
In a sign of what many enterprises – not only logistics companies – may look like in years to come, CH Robinson currently employs some 450 data scientists and software engineers.
Inflated expectations
Not everyone believes the path forward will be straightforward. Thomas O’Connor, Gartner vice-president for logistics and planning for APAC, says AI is currently at the “peak of inflated expectations”. He believes technology leaders across logistics and other industries are under growing pressure from the C-suite to deploy AI, even as many organisations continue to struggle with their definitions.
“What kind of AI are people talking about?,” he adds. “There’s a desire for productivity improvements, yet a lack of clarity in terms of outcomes.”
Chief supply chain officers…need to work with different ecosystem partners to ensure data provided into the data pool is accurate and safe
Thomas O’Connor, Gartner
Echoing remarks from CH Robinson’s Kass, O’Connor says supply chain organisations need to embrace a two-track approach focused on both “exploitation” and “exploration”, with the latter representing the most dramatic shift.
He says the challenges facing technology leaders in logistics and supply chain are not fundamentally different from those confronting other sectors.
“Chief supply chain officers need to have clarity in terms of data,” O’Connor says. This means understanding ownership, accurately identifying input sources and knowing where data is actually coming from. “They need to work with different ecosystem partners to ensure data provided into the data pool is accurate and safe.”
Achieving all of this demands robust data governance frameworks.
Managing uncertainty
Deloitte Asia Pacific CEO Rob Hillard says AI is already transforming the supply chain and logistics sector, where there is a growing need to better manage what he terms “ambiguous exception management”.
This has become a major priority for supply chain leaders as Covid-19, natural disasters and geopolitical conflicts have introduced unprecedented uncertainty into global logistics networks. Hillard says AI is expected to empower smaller manufacturers through lower-risk, data-driven experimentation, including product launches and expansion into markets directly aligned with supply chain realities and costs. He points to additive manufacturing and 3D printing as examples likely to benefit.
“This could allow smaller businesses to create specialist products and distribute them more effectively,” he adds.
Hillard says that digital leaders across supply chain ecosystems must also ensure systems can integrate and communicate if efficiencies are to be realised. At the same time, AI is creating a major push towards integration and interoperability across supply chains, manufacturers, logistics providers and technology platforms.
100 trillion data points
CH Robinson says Lean AI Engineer can assess an entire supply chain in 25 to 30 minutes and determine improvements before performance is affected, compared with traditional supply chain assessments that can take up to four weeks and often focus on what has happened rather than what should happen next.
While Lean AI Engineer delivers intelligence, Lean AI Planner manages shipments through hundreds of interconnected AI agents and in turn feeds more data back to Lean AI Engineer to develop even smarter refinements. As with all AI, success depends on managing and contextualising massive amounts of data. The company claims to now be managing 100 trillion data points across its global network.
Kass explains that its Lean AI systems are able to understand customers’ supply chains from the inside out as they leverage data end-to-end across every step of the shipping process, above and beyond parts visible to disparate tools. The company’s 450 technology specialists play a key role in capturing and organising historical data reaching back to its earliest digital systems, while simultaneously collecting massive amounts of information about each client’s business and operating environment, eliminating generic or theoretical assumptions.
One early-adopter customer realised annual savings of more than $1m by shifting from a variable shipping schedule to a weekly model. Another found that having one pickup serve three delivery locations cut loads by 81% and generated savings of around 40%.
The robots are coming
While robotics have long been integral to manufacturing and supply ecosystems, expect to see a sharp uplift in innovation and capability as AI seeps into this evolving industrial DNA.
“Production lines are increasingly being integrated with robots,” Hillard notes. He cites Deloitte’s State of AI survey 2026 report, which observed that 2025 was the year physical AI – the merger of physical systems with AI – emerged from the realms of science fiction into mainstream business consciousness.
Notably, while only 5% of surveyed organisations believe physical AI is transforming their industry today, more than 40% expect it will transform their industry within the next three years.
Meanwhile, robots themselves are becoming vastly more intelligent and physically capable, in no small part due to advances in China, while the evolution of IoT and communications networks – from 5G and eventually 6G through to ubiquitous satellite connectivity – is opening new possibilities for smarter supply chains, logistics and manufacturing. This has seen the emergence of new players and innovations across AI, digital twins, robotics and industrial automation.
Nvidia has emerged as a major force through its investments in physical AI, digital twins and industrial simulation platforms, while Siemens, Schneider Electric, ABB and a growing ecosystem of startups are developing technologies that connect AI-driven decision making with real-world operations.
Meanwhile, software giants including SAP, Oracle, Microsoft and Salesforce are embedding generative and agentic AI capabilities into supply chain platforms. And specialists like Kinaxis, Blue Yonder, o9 Solutions, Manhattan Associates and Coupa now target everything from demand forecasting and inventory optimisation through to procurement, warehouse operations and transport logistics.
If the first wave of AI in the supply chain was about analysing data, the next is most certainly all about acting on it at unprecedented scale with humans and machines working closely together to learn from the past, optimise the present, and, if to not actually predict, at least be better prepared for the future.
If CH Robinson’s experience is anything to go by, the future may have already arrived.
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