Blue Yonder pushes supply chain AI toward autonomous operations

The supply chain sector has perhaps more to gain from the artificial intelligence age than any other sector.

Supply chains are subject to many different variables — weather, delays, legal restrictions — that traditional planning systems cannot always manage effectively. That’s why Blue Yonder Inc., a supply chain management company, has started using AI agents and models to manage its operations with greater speed and flexibility than older systems.

“For the first time in the industry’s history, we have end-to-end supply chain operations and solutions built on one common data model,” said Chris Burchett (pictured), senior vice president of generative AI at Blue Yonder. “Our agents are a huge part of that now, with the warehouse ops agent, logistics ops agent and inventory ops agent — these agents are now beginning to work alongside humans. They’ve grown from monitoring and analytics to actually more autonomous actions as well.”

Burchett spoke with theCUBE’s Dave Vellante for the “AI and the Autonomous Supply Chain” interview series on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed how AI is transforming supply chain operations and Blue Yonder’s new partnership with Nvidia Corp. (* Disclosure below.)

Blue Yonder adopts an ‘owned intelligence’ strategy

At ICON 2026, Blue Yonder announced a partnership with Nvidia Corp., as well as updates centered on frictionless adoption. Although Blue Yonder was an early adopter of AI, Burchett believes that Nvidia’s models will take Blue Yonder’s organization to the next level.

“We’ve selected the Nvidia Nemotron models,” he said. “We did this because Nvidia has made a huge investment in these open-weight models that allow us to have a partner who we know is going to build enterprise-ready, enterprise-grade models. When we tested them, we found them to be on par with all of the other models in the same weight class, so to speak.”

In employing Nvidia’s products, Blue Yonder hopes to close the gap between insight and action. What that means is that if a problem arises — say, there are allocation shorts in the warehouse — an AI agent can prioritize what needs to be done and take autonomous action. The company’s other goal is to save on tokens, which can rack up over time, by having its own, proprietary models — what Burchett describes as an “owned intelligence” strategy.

“What we realized is if we train a model, we can have a much smaller model that will be faster, better, cheaper,” Burchett said. “We very much believe that the frontier models are key for our success, and that’s a big part of what we want to continue to get good at. But we believe that for the types of operations that are happening in the inner loop of the agent and have to be performed over and over … we [needed] a model that was small, light and efficient.”

Blue Yonder’s platform has also gone cloud-native, which Burchett said is not mutually exclusive with AI-native. He sees them as complementary, since cloud-native systems give users a way to keep their data all in one place — making it easier for AI models to access it.

“We’ve now made it possible through agentic technology to onboard customers from their on-prem solutions onto this cognitive platform in really just a fraction of the amount of time,” Burchett said. “We’re talking days instead of weeks or months. We even had customers on stage showing the benefit of that in just 72 hours.”

Ensuring end-to-end governance in supply chain operations

The final piece of an AI operating system is governance, and Blue Yonder has been rigorous about its adoption practices. Because supply chains cross regions with different laws, having a human in the loop is critical for ensuring that the data stays exactly where it should be, Burchett explained.

“We keep the data in the same region,” he said. “It never leaves. We’ve been very sensitive about how we treat data. We only train on synthetic data and not on customer data. And when we run these models and run these operations, they’re all customer isolated. We’re not ever crossing the streams of one model instance being used by multiple customers.”

As Blue Yonder looks toward a future defined by AI, Burchett is hopeful that agents will reach their full potential. He envisions the new supply chain as a collaboration between autonomous agents and users, solving a greater range of problems than has been possible up until now.

“We’ll start to see users get comfortable with those agents taking action,” he said. “And what will also happen during this time is agents will start to interact more with each other to solve end-to-end supply chain problems. We have this working in our labs, but we don’t really have a lot of it in customer deployments today. Within a domain, like warehousing or transportation, you’ll see agents more autonomous and working and building trust with humans.”

Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of the “AI and the Autonomous Supply Chain” interview series:

(* Disclosure: Blue Yonder sponsored this segment of theCUBE. Neither Blue Yonder nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE