AI-powered supply – Blue Yonder CEO Duncan Angove says many companies are treating AI as a way to swap planners for agents—when the real shift should be about redesigning supply-chain systems so data and decisions can flow across disconnected functions.
For Duncan Angove, the most dangerous AI pitch isn’t the promise of smarter agents. It’s the belief that an AI upgrade can be layered onto a supply chain the way someone swaps in a newer app.
“If I hear too often is, how many planners or logistics coordinators can we replace with agents? That’s the wrong unit,” the Blue Yonder CEO told Newsweek.
Angove’s argument is blunt: the “unit of transformation is not the user.” It’s the system and the outcomes it delivers.
Blue Yonder, a supply chain software company, surveyed 678 senior supply chain professionals across North America and Europe for its latest “Supply Chain Compass” research. All respondents worked at organizations with at least $500 million in annual revenue in retail, manufacturing or logistics.
When those leaders were asked what mattered most, the priorities looked familiar. Respondents ranked improving efficiency and productivity as their top organizational priority. followed by faster and better decision-making. increasing profitability. managing supply chain cost and becoming more resilient to risks and challenges. Developing a more AI-driven supply chain ranked lower as a standalone priority.
Angove said that doesn’t mean AI is secondary. It suggests supply chain leaders are likely to judge AI by whether it improves the business outcomes they already care about: faster decisions, fewer disruptions, lower costs, better service and less waste.
The value, he argued, becomes clearest when AI can coordinate decisions across a network that is usually disconnected.
“AI delivers real-time insights, decision-making, and coordination across a complex, multi-enterprise network, providing value beyond what current systems can offer,” Angove said.
The trouble is what happens when companies try to plug AI into the same patchwork they’ve been running for years.
“Historically, supply chains have been fragmented, with each node siloed from the others,” Angove said. “This design isn’t viable for an AI-powered supply chain, which requires each system to communicate with one another and cascade data through the chain to inform decision-making at each function.”
He compared the risk to factories that embraced electricity but kept the old steam-era layout. “When factories first adopted electricity. many simply replaced the steam engine with an electric motor. but kept the same belts. pulleys. layouts. and workflows. ” Angove said. “They treated electricity like a slightly better steam engine. And productivity barely improved.”.
“The danger with AI in supply chains is the same. We simply bolt intelligence onto yesterday’s workflows instead of reimagining how supply chains should operate,” he added.
That “bolt on” approach, in Angove’s telling, is also why some AI rollouts end up delivering limited results.
Companies often get adoption wrong, he said, by adding another tool to each function instead of connecting functions themselves.
“Applying agents to supply chains cannot be built from a lab alone,” Angove said. “Supply chain operations can be very messy, even at the global level. And operational truth is often hidden inside tacit workflows and tribal knowledge.”
When AI is added to fragmented processes, he said, the outcome is predictable: “You end up with smarter silos, not a smarter supply chain.”
The supply chain isn’t a theoretical software environment. Angove described it as warehouses, factories, transportation networks, suppliers, stores, inventory levels, labor constraints and customer commitments—decisions that have immediate operational consequences.
He pointed to the outcomes respondents said they already expect from AI, including better planning and predictability, better risk management, faster decision-making, increased productivity, better execution and faster responsiveness.
But he argued that AI built for supply chains has to respect the constraints of the environment where decisions carry real-world impact.
“Supply chain is not a generic reasoning problem,” Angove said. “It’s a deeply operational environment with hard constraints. real-time execution. physical consequences. extreme scale. and thousands of interconnected decisions happening continuously across warehouses. transportation networks. suppliers. stores. and factories.”.
Angove expects companies to match the AI tool to the decision in front of them. “Companies need AI tools that leverage frontier models where broad reasoning is needed. and specialized models where operational precision. speed. and cost are concerned. ” he said. “This hybrid approach will be the future of AI-powered supply chains.”.
The long-term test, he said, won’t be whether a company has adopted agents. It will be whether AI helps the supply chain respond faster when demand shifts, inventory tightens or suppliers fall behind.
“The ones that get left behind will be the ones using AI to do the same old things slightly faster,” Angove said. “Agentic technology brings us closer than ever to a truly autonomous supply chain, but it requires us to elevate our thinking beyond silos and users to the systems level.”
For the 678 leaders surveyed. the priorities were clear—and so was the tension: AI adoption is being evaluated against everyday operational goals like efficiency. cost and resilience. even as the most transformative potential depends on dismantling the disconnection that has defined supply chains for years.
Blue Yonder Duncan Angove AI in supply chain agentic AI supply chain automation Supply Chain Compass North America Europe logistics warehouses real-time decision-making