From Real‑Time Data to Real‑Time Action

How Agentic AI Turns Supply Chain Uncertainty into Business Advantage

Five industry experts share how agentic artificial intelligence is transforming supply chains by enabling autonomous decision‑making, real‑time adaptation and resilience in the face of an unpredictable future.

In today’s globalized and hyper‑connected world, supply chains face an extraordinary array of challenges. From geopolitical instability, natural disasters and labor shortages to rapidly changing customer demands and sustainability pressures, the complexity and unpredictability of supply chains have never been greater. Moreover, supply chain disruptions cost companies billions of dollars annually, impacting customer satisfaction and profitability.

Traditional supply chain management methods often rely on static planning in spreadsheets, manual interventions and siloed data. Those obstacles mean operations struggle to keep pace with fast‑moving changes. In response, a new wave of technologies—collectively referred to as agentic artificial intelligence (AI)—is emerging to meet the demand for intelligent, autonomous, and adaptive supply chains. These systems go beyond just analyzing data or generating reports. Instead, they observe, learn, decide and act independently, turning uncertainty into a competitive advantage.

To shed light on how agentic AI is revolutionizing supply chain operations, MHI Solutions invited five experts to answer key questions about this technology, its current reach and its anticipated impact on the industry. They included:

MHI Solutions: What is agentic AI, and why is it important for modern supply chains?

Zoehrer: Generative AI is about building content, such as text or images, and requires interfaces, input or interaction with humans. Examples are ChatGPT, Google Gemini or Microsoft CoPilot. Agentic AI is a more autonomous system, targeting specific tasks with minimal human intervention. Also, agentic AI can react in response to real‑time world changes/input. The models also differentiate in their literal size. Generative AI uses large language models (LLMs) that require enormous computing power. In contrast, agentic AI uses specialized language models (SLMs) that need significantly less computing power. That allows them to control autonomous vehicles, for example.

Masson: Agentic AI goes beyond traditional AI or decision support systems by acting autonomously, in accordance with predefined goals, policies and changing situational context. What sets it apart is not just its ability to analyze or make recommendations, but to actively execute decisions and continuously adjust those decisions in real time. In today’s supply chains—which are becoming increasingly dynamic, global and customer‑driven—having an AI system that can proactively take initiative without human intervention is a game-changer. It allows organizations to move from being reactive to truly adaptive, which is vital when even minor delays or disruptions can cascade across an entire network.

We’re entering a phase where speed and adaptability matter more than ever. Traditional AI might generate insights or forecasts, but it still relies heavily on human interpretation and execution. Agentic AI closes that gap by actually carrying out actions based on context and learned behavior. In fast‑paced supply chain environments, reducing that latency between insight and action can be the difference between seizing an opportunity and missing it.

MHI Solutions: How does agentic AI enable automation, boost efficiency and accelerate innovation in supply chains? What other benefits can supply chain leaders expect?

Burchett: Agentic AI represents a leap forward because it doesn’t just automate repetitive tasks—it automates complex decision‑making processes that previously required expert human judgment. Blue Yonder’s AI agents, for example, follow what we call the “SADA Loop”—they see, analyze, decide and act. Imagine a shipment delay. Traditionally, a logistics planner must identify the delay, communicate with carriers and adjust delivery schedules. Agentic AI can see the delay instantly, analyze alternatives, decide to reroute shipments autonomously and act to communicate changes—often in minutes, not hours. This level of automation reduces downtime and manual intervention, streamlining operations at scale. In terms of efficiency, agentic AI enables continuous process improvements by uncovering inefficiencies that humans might overlook. It can simulate “what‑if” scenarios, helping teams redesign workflows or adjust warehouse layouts rapidly. This capacity to innovate continuously is vital in highly competitive markets.

Masson: Agentic AI is fundamentally about turning real‑time data into real‑time action. It allows supply chain systems to respond immediately to disruptions, delays or demand changes without escalating everything to human operators. That not only frees up time for the workforce, but also significantly reduces costs associated with delays, errors or manual intervention. By automating repetitive decisions and exception handling, it increases operational efficiency across planning, fulfillment and logistics. Because it learns from outcomes—both successful and unsuccessful—it continuously refines its decision‑making logic. That feedback loop is what drives ongoing innovation and optimization.

Another benefit is scalability. As complexity grows—think multi‑tier global supply chains, labor shortages and rapid shifts in consumer behavior—it becomes impossible for even the best human teams to manage every variable in real time. Agentic AI scales decision‑making in a way that human teams simply can’t. It ensures that decisions, from tactical to strategic, are not only fast but also aligned with broader business goals. Because it works 24/7 without fatigue or oversight, it provides a level of resilience and agility that’s increasingly essential in today’s volatile markets.

Zoehrer: Agentic AI in warehouses can manage autonomous robots or inventory cycle counting. Our company applies agentic AI to warehouse analytics, design and operational improvements. It optimizes both warehouse workflows and labor allocations. I believe that in the near future, AI agents will manage warehouses and operations. Automated warehouses are complex and rely on many variables that are constantly in flux, such as different orders, seasonality or labor performance. Collectively, these variables are too numerous to be managed “perfectly” by humans. Therein lies the opportunity for agentic AI agents; they just need to be trained for those tasks.

Scholes: Agentic AI enables a new level of modularity and system commissioning that significantly enhances automation capabilities. It dynamically interprets configurations and system inputs to support true plug‑and‑play deployment across diverse material handling equipment (MHE). Where once multiple personnel were needed to set up or reconfigure work cells, agentic AI can now manage this autonomously, reducing dependency on specialized human knowledge. Moreover, it continuously refines operations through deeper analytical insights, enabling long‑term system optimization. This allows leaders to align machine usage with actual business cases, not just technical performance. Practically, this reduces downtime through predictive maintenance and decreases the labor needed for equipment upkeep.

Arora: Agentic AI drives automation by identifying issues on its own, suggesting fixes and taking action with no human input required. It keeps operations running smoothly with real‑time monitoring and on‑the‑spot inventory and material flow adjustments. That means less manual work, fewer mistakes and more streamlined processes. It also helps companies move faster by pulling insights from data in real‑time, uncovering ways to redesign workflows, tweak layouts or test new business models.

MHI Solutions: How does agentic AI boost supply chain visibility?

Arora: Agentic AI systems continuously analyze large volumes of data from diverse physical sources—such as Internet of Things (IoT) sensors, autonomous drones and material handling equipment cameras—integrating these data streams in real‑time. By doing so, they significantly enhance visibility into operational bottlenecks, inventory anomalies and shipment discrepancies. Supply chain leaders thus gain immediate and precise understanding of inventory locations, handling conditions and workflow inefficiencies, enabling more informed, proactive decision‑making.

Scholes: Agentic AI offers a new tier of operational visibility by unlocking access to real‑time, data‑driven decision making. In environments previously limited by human bandwidth, agentic AI scales decision intelligence across the entire network. This shift empowers operators and support teams with insights that were once buried in system silos. For example, a sorter might become a bottleneck without obvious cause. With agentic AI integrated into both MHE and software systems, root cause analysis and corrective actions can be initiated before the bottleneck materially impacts throughput—enhancing flow, performance and resource planning across the supply chain.

Masson: Visibility in the supply chain is evolving from passive tracking to active understanding. Agentic AI systems go well beyond showing you where something is—they help explain why a delay is happening, what impact it will have and how you should respond. It continuously ingests data from a wide variety of sources—enterprise resource planning (ERP) systems, IoT sensors, transportation feeds, warehouse robotics—and applies contextual intelligence to detect anomalies, assess risks and recommend or implement corrective actions. That level of real‑time, cross‑functional visibility enables better coordination, faster decisions and fewer surprises.

Another important aspect is that agentic AI helps eliminate data silos. In many organizations, supply chain data is fragmented across different systems and teams. Agentic AI is designed to operate horizontally, ingesting and correlating data across domains—such as procurement, logistics and warehouse operations—so that everyone in the network has a shared, real‑time understanding of what’s happening. That’s a foundational capability for companies that want to compete on speed, service and cost.

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