AI adoption is accelerating across virtually every industry, and supply chain management is no exception. According to Gartner, 74% of supply chain practitioners identify AI as the top driver of transformation. Public First research further shows that Americans aged 25–54—the demographic that makes up the bulk of supply chain professionals—are broadly optimistic about AI in the workplace.

Yet despite this optimism, many of the deployment strategies are failing to deliver the core financial outcomes these companies need. Gartner found that only 29% of supply chain professionals feel adequately prepared for AI adoption. This gap is reflected in the tools currently in use: most AI solutions can automate one or two discrete tasks, but fall short when it comes to managing complex, end-to-end workflows. They streamline parts of the process but can’t deliver the full efficiency gains required to cut costs, increase accuracy, and free up teams to focus on higher-value work.

Introducing AI operators: From task automation to workflow ownership

This is where agentic AI comes into play, and specifically AI operators. Unlike single-task assistants, AI operators are designed to handle multi-step processes independently, functioning as true contributors rather than narrow task helpers.

For freight forwarders, AI operators can take on many of the current manual processes across their workflows, automatically flagging exceptions to human operators when necessary. Tasks like shipment data aggregation and input are completed with little requirement of human operator intervention, letting focus on the edge cases and higher-value responsibilities. The result: less time lost to repetitive operational tasks, faster workflows, and the capacity to drive better productivity while delivering better service.

The shift in human-AI collaboration

By offloading the repetitive 99% of operational work, AI operators allow supply chain managers to step into more strategic roles. Automated second reviews ensure quality, while exception management keeps oversight tight. This balanced collaboration boosts efficiency, reduces errors, and improves transparency—benefits that ripple outward to customers.

Shippers in turn enjoy lower costs, faster service, and more consistent experiences, while providers strengthen their competitive edge. Freed from the constraints of daily manual work, teams can focus on customer relationships and innovative service offerings.

Proven impact across supply chains

These gains are more than theoretical. Some AI operators demonstrate measurable impact across logistics operations. These outcomes highlight the transformative potential of AI operators: a shift from incremental automation to true operational leverage.

Building the future of supply chain management

AI operators represent the next stage of AI adoption in supply chains: autonomous systems that manage complex workflows, collaborate with human operation teams, continuously improve over time, and unlock new levels of efficiency and scalability.

For supply chain leaders, the question is no longer whether AI will reshape the industry—it’s how to harness it to stay ahead. By moving beyond assistants to autonomous contributors, companies can cut costs, boost accuracy, and deliver more value to their customers.