Across manufacturing and process industries, supply chains are operating under intense pressure. Demand and market volatility, disruptions in materials, and a persistent need to “do more with less” have made supply chain agility critical.
Manufacturers have quickly embraced automation on the shop floor, including drones, robots, and sensors optimizing production lines. Yet when it comes to supply chain planning and execution, many organizations still rely on manual analysis, human judgement and delayed decision-making cycles. This is where artificial intelligence, and especially Agentic AI, is emerging as a transformative force.
Modern supply chains are extraordinarily complex. Every decision – whether to reroute shipments, hedge against raw material price changes, or adjust production schedules – ripples through a web of suppliers, logistics partners, and markets.
Traditional approaches to automation and analytics can sometimes struggle to keep pace with this speed and scale. Far too often, planners today are saddled by outdated technology and processes which means they can spend days running reports, generating recommendations, and reconciling data before decisions reach leadership. By then, the window for action may already have closed.
Modern supply chain planning platforms have significantly accelerated the time to make decisions and now agentic AI has the opportunity to take this to the next level. Instead of waiting for people to request insights or write data queries, agents can act autonomously, analyzing, correlating, and recommending actions in near real time. They operate at the speed of business, turning insight into decision at machine speed. insight into decision at machine speed.
What Makes Agentic AI Different?
While most people associate AI in the enterprise with chatbots or assistants, Agentic AI is much more advanced. Beyond simply answering questions, AI agents are part of an intelligent system that perceives, reasons, and acts toward defined business goals. They learn from new data, adapt to changing conditions, and connect both structured and unstructured signals.
AI agents can operate with a goal-seeking mindset: determining not just what’s happening, but what should be done next and why.
For supply chain leaders, that means moving from reactive analysis to proactive decision making. For example, you can ask an AI agent: “Which items have the most urgent supply chain issues right now—and what’s driving them?” In just a moment, the agents can query multiple databases, correlate external data such as commodity price swings, and return a recommendation complete with impact analysis and confidence levels.
Agentic AI Use Cases
Agentic AI’s potential stretches across every layer of the supply chain. Some real world applications include:
Prescriptive Recommendations: Move beyond rigid “if/then” exception management. Agents can generate adaptive, open-ended recommendations based on live data, guiding planners through what to prioritize and how to act. Rather than static rules, recommendations dynamically change to meet objectives and to inject planner preferences.
Root Cause Analysis: When forecasts miss the mark or supply shortages appear, agents can trace contributing factors across demand signals, supplier performance, and market data, explaining why it happened and how to prevent recurrence. This rapid analysis cuts planning time cycles across S&OE and S&OP to support decision-driven, not calendar-driven schedules.
Support for Sales & Operations Execution (S&OE): Agents can monitor the environment, flag issues early and quickly suggest and orchestrate corrective actions to maintain service levels. Autonomous agents can ingest sales, market, weather, operations, shop-floor, transportation and more and then orchestrate decisions and actions (e.g. re-prioritize a work order, re-route a shipment) with internal and external parties.
Hedging Decisions: Too often, hedging is guided by memory or habit, regardless of how well the decision is performed. Agentic AI can leverage its memory of previous decisions, assumptions and outcomes to provide context to evaluate options and support better-informed decisions.
Process Manufacturing Optimization: In industries with multiple formulations, speed and temperature profiles, optimization can be overwhelming. Agentic AI can navigate this multi-variable complexity, testing scenarios and identifying optimal configurations in ways even seasoned planners find difficult to replicate manually.
Crucially, Agentic AI also helps reduce human decision-making fallacies that often undermine supply chain performance. People tend to overvalue recent experiences, assume past successes guarantee future success (gambler’s fallacy), or cling to outdated strategies due to prior investment (sunk-cost bias). Agentic systems, by contrast, evaluate every scenario through an objective data-backed lens. And it can learn from feedback and historical outcomes.
Agent-based simulations can also model and stress-test supply chain scenarios using probabilistic reasoning to present evidence-based scenarios. This means planners can explore multiple “what-if” scenarios instantly, understanding both potential outcomes and the probability of success, as well as the risk and value created by decisions.
Building Trust Through Explainability
For AI to drive value, it must be trusted. In particular, in manufacturing environments with deep complexity and decisions impacting safety, compliance, and profitability – explainability is non-negotiable.
It’s key to embrace a planning solution where Agentic AI emphasizes governance through human-in-the-loop controls, and every recommendation is transparent, traceable, and subject to review before execution. Decision-makers can see why a specific plan was generated, which data informed it, and how alternative actions might affect outcomes.
This combination of autonomy and accountability helps organizations adopt AI responsibly. It ensures that technology amplifies human judgment, rather than replacing it. Over time, consistent, explainable recommendations build confidence, transforming skepticism into strategic trust.
Readiness and Culture
Beyond technology, adopting the latest AI innovations requires organizational readiness. Teams must be empowered to collaborate with AI, interpreting recommendations and shaping continuous improvement. This may require skills development to achieve AI fluency, and a culture that values experimentation and learning.
To build a strong culture around AI, leaders should ask:
Are we fostering a culture that views AI as a partner in problem-solving rather than a threat to established roles?
Do our teams understand how AI decisions are made and when to challenge them?
Are we recruiting or developing talent with AI expertise?
Agentic AI is set to transform decision speed and confidence. But success starts with clarity. Leaders must define the problems to solve, and the value they want to create. It’s not about chasing hype, or deploying AI for its own sake, to see what happens. It’s about focusing intelligence where it delivers the most impact, reducing lag time, increasing resilience, and unlocking new performance frontiers.
Is your organization ready to incorporate AI into your decision-making DNA?
About the Author:
Matt Hoffman is the Vice President of Product and Industry Solutions at John Galt Solutions. Matt specializes in delivering transformational from analysis through execution across a diverse range of clients in manufacturing, distribution, and retail. Matt is committed to ensuring that processes drive solution adoption, resulting in measurable outcomes. Throughout his career, Matt has successfully led software implementations utilizing best-in-class supply chain planning systems, execution systems, and merchandising planning systems.
The post From Automation to Agency: A New Era of Supply Chain Intelligence – How Agentic AI is Redefining Value in Manufacturing Supply Chains appeared first on Logistics Viewpoints.