Supply Chains Are Learning to Think and Act on Their Own

The supply chain is undergoing a structural transformation that goes well beyond the automation wave of the past decade. Across distribution centers, planning systems and logistics networks spanning dozens of companies, artificial intelligence (AI) is moving from a tool that assists human decisions to a system that makes them, and in some cases, executes them without waiting for a person to sign off. The shift is uneven and far from complete.

Most companies are still in the early stages of this transition, focused less on autonomy than on basic digital integration. A World Economic Forum analysis describes a three-stage progression from digitalization to AI-assisted adaptability to complete autonomy, with each stage dependent on the one before it.

In stage one, companies replace manual processes with cloud systems for real-time visibility. Stage two uses machine learning and simulation to anticipate disruptions. Few organizations have reached stage three, where AI acts autonomously in real time.

In April, Knut Alicke, senior advisor to McKinsey, compared the potential impact of AI on supply chains to the invention of the shipping container, which fundamentally changed global logistics and made previously impossible tasks routine. The analogy is useful because it positions AI not as a feature to be added, but as a change to the underlying infrastructure for how goods move and decisions are made.

Alberto Oca, McKinsey partner and co-leader of digital warehousing in North America, estimated at the time that AI could generate roughly $190 billion in value across travel and logistics and another $18 billion in direct supply chain operations, through applications ranging from automated shipping documentation to AI systems that help dispatchers manage large vehicle fleets.

The harder question is whether those gains materialize in practice. A February Boston Consulting Group (BCG) report found that most companies sit in the middle of the capability range, and that those trying to skip ahead to AI-driven automation without first fixing their planning processes tend to underperform compared to those that build incrementally.

BCG found that performance gaps between leading and lagging companies come down to four factors: clarity about what decisions the planning process is supposed to support, how well processes are designed around those decisions, the quality of the underlying data, and whether the technology actually fits the workflow. The technology itself is rarely the bottleneck.

A separate but related shift is happening inside warehouses, where AI is moving off servers and into the machines themselves. The World Economic Forum drew a distinction in January between traditional warehouse automation, which is fast and precise but rigid and hard-coded, and physical AI, which equips robots and other machines to perceive their environment in real time and adapt to it rather than simply repeating preset actions.

The practical implication is that a warehouse running physical AI does not function as a collection of automated machines but as a single coordinated system, with a central layer that manages the movement of every robot, every item and every worker simultaneously. Such a system can run simulations to test how repositioning inventory would affect throughput, predict when a product will run short before it does and send signals upstream to suppliers without human involvement.

Even sophisticated AI inside a single facility runs into limits at the edges of the organization. Supply chains involve dozens of companies, and most of them still operate on incompatible systems with no shared view of what is happening across the network.

IDC predicted in December that by 2028, half of large enterprise supply chains will have built network-level visibility beyond direct suppliers, cutting disruption response times by 25%. The enabling technology here is agentic AI, systems that can take action across company boundaries, not just flag problems for a human to address.

IDC also projected that by 2029, nearly half of the world’s largest companies will use AI agents to manage partner and channel relationships, resulting in measurable gains in revenue and satisfaction scores.

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