Over the past few years, we’ve seen rapid advances in the use of Artificial Intelligence (AI) across nearly every aspect of daily life — and the supply chain software landscape is no exception. But where exactly is this technology being applied, and how can your organization effectively integrate AI into its supply chain systems? In this blog, we’ll explore how Agentic AI is shaping the marketplace and delivering measurable value to companies both inside their distribution centers and across their broader supply chain networks.
Before diving into specific use cases, it’s important to clarify what Agentic AI actually is. Unlike traditional or generative AI, Agentic AI is proactive — designed for autonomous action and decision-making with minimal human intervention to achieve defined goals. If Generative AI (like ChatGPT) provides the recipe for chocolate chip cookies, Agentic AI is the baker pulling the cookies out of the oven and serving them.
As you can imagine, the applications in supply chain are extensive. Existing solutions — such as Warehouse Management Systems (WMS) — are already using Agentic AI to support targeted operational improvements. Many leading WMS providers have begun releasing Agentic AI “agents” that leverage data already collected by the system to drive smarter, faster, and safer decisions.
For example, a Workforce Balancing Agent might analyze real-time transactional and order data to determine when an associate should be moved from picking to shipping to get all of the orders out of the door by their cutoff times to meet SLAs, then automatically notify that employee through their device to make the shift. This type of automation not only improves efficiency and productivity but can also enhance workplace safety and responsiveness.
On a broader scale, Supply Chain Control Towers provide a holistic view of the end-to-end supply chain — with capabilities spanning topics like demand forecasting, procurement, vendor management, and order fulfillment. Because these platforms unify and visualize data from multiple internal and external sources (e.g., weather, traffic, transportation disruptions), they can provide valuable insight into the overall health of the network. Many also incorporate Decision Intelligence capabilities powered by Agentic AI to automate responses and actions.
For instance, if a company sets a goal to contain costs and reduce waste through more efficient stock movement, the Control Tower can use real-time inventory visibility to recommend and even generate stock transfer tasks directly within a WMS through two-way integration. This kind of closed-loop automation enables organizations to respond to changes quickly and strategically — without waiting for manual intervention.
As AI continues to evolve, the opportunities to optimize supply chain systems will only grow. If your current supply chain lacks visibility or feels fragmented, implementing a Control Tower may be a powerful next step. Other AI capabilities may arrive more seamlessly, such as through your WMS’s quarterly upgrades or enhancements.
Regardless of which systems you use, understanding and leveraging AI effectively is key to unlocking greater intelligence, agility, and resilience across your supply chain. Equally critical is approaching implementation with care — validating use cases, ensuring system readiness, and establishing strong governance frameworks before advancing to higher levels of automation.
With the right strategy and guardrails in place, Agentic AI can become a true enabler of smarter, more connected, and more responsive supply chains.
—Caroline Sharp, St. Onge Company
