en.Wedoany.com Reported - C.H. Robinson has pioneered the launch of artificial intelligence technology to operate shippers' global supply chains and continuously evaluate and improve performance, marking the first such release in this field. The global logistics provider built the system to serve its 4PL Managed Solutions clients. The newly launched Lean AI Engineer will work in tandem with the Lean AI Planner, introduced in 2025, to create an interconnected system that uniquely enhances supply chain performance during operation.
The technology autonomously handles 92% of global 4PL freight movements, covering truck, ocean, air, and rail. It manages shipments from order creation, including tendering, route planning, delivery, exception handling, and carrier payment. The Lean AI Engineer can assess the entire supply chain within 25 to 30 minutes and identify improvement opportunities before performance is impacted, replacing traditional supply chain evaluations that typically take up to four weeks and only review past events. The Lean AI Engineer provides intelligence, while the Lean AI Planner manages freight movements through hundreds of interconnected AI agents. Execution results feed data back to the Lean AI Engineer to develop smarter optimization plans.
Jordan Kass, President of C.H. Robinson's Managed Solutions division, stated that the system operates continuously, improving ongoing operations and self-correcting when failures occur, without requiring alerts or human intervention to first identify issues. The Lean AI Planner executes tasks in real time, while the Lean AI Engineer studies results, identifies patterns, and adjusts logic. Jordan explained that the technology eliminates the need for standalone supply chain intelligence and orchestration tools.
By scaling logistics expertise through technology, traditional premium logistics services rely on top talent to manage complexity, make decisions, and intervene during disruptions. Jordan added that the problem is that talent cannot be scaled; by encoding expertise into the technology itself, shippers gain unlimited talent and expertise, consistently applied to every shipment regardless of who is available in any time zone or how much volume grows or surges. Teams can focus on strategic priorities to drive optimal business outcomes.
Success depends on the data and contextual information the system can access. With 450 in-house software engineers and data scientists, a proprietary context layer is built by systematically capturing institutional knowledge from workflows. This data comes from experienced freight experts and continuously feeds the model. The technology leverages data from all steps of end-to-end transportation, rather than fragmented views from different tools. It is trained on the unique context of orchestrating freight, including shipment details, processes, pickup and delivery locations, carriers, routes, and risk tolerance. Jordan noted that this is how the Lean AI Engineer knows which improvements are suitable, rather than offering generic or theoretical suggestions. For example, if an auto parts manufacturer ships cross-border five days a week to a just-in-time assembly line, the system will not recommend saving costs by shipping once a week. The advanced AI considers more variables than human analysis or typical software analysis, and improvement suggestions are prioritized and actionable for users.
At launch, the Lean AI Engineer will identify optimization and hidden savings opportunities for businesses. One early adopter learned that switching from a variable shipping schedule to a weekly schedule could reduce load counts by 17% across 20 locations, saving over $1 million annually. Another client restructured freight movements so that one pickup served three different delivery points, reducing total load counts by 81% and saving 40% in costs. The Lean AI Engineer will roll out to more clients, beginning to evaluate other factors such as carrier performance. By continuously monitoring carrier behavior across lanes, modes, and clients, it will identify leading indicators of performance decline and recommend corrective actions before service failures occur.
Arun Rajan, Chief Strategy and Innovation Officer at C.H. Robinson, noted that supply chains are often not lacking information but suffer from a gap between knowing and doing. Technologies positioned above or outside the supply chain can aggregate data, coordinate signals, and make recommendations, but rely on others to execute signals and understand whether actions are effective. Arun explained that the technology bridges this gap by providing 24/7 premium service through a unified system, unmatched by any other company.
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