How to transform global supply chain operations with agentic AI

Enter “agentic AI” — the latest advancement in artificial intelligence (AI) that enables autonomous decision-making and task execution, unlocking capabilities such as proactive inventory management and ultimately boosting efficiency and resilience in supply chain operations. This innovative approach represents a transformative shift in how large language models (LLMs) are leveraged to address goal-oriented tasks. Unlike generative AI (GenAI), which relies on human prompts and focuses on isolated tasks, agentic AI operates independently, identifying needs and executing processes seamlessly.

To date, AI in the supply chain is helping leading-edge organizations improve operation management tasks, such as demand forecasting, supply planning, inventory management, logistics optimization and predictive maintenance. Looking ahead, agentic AI will expand these capabilities, bolstering resilience by helping organizations predict and mitigate future supply disruptions with limited human intervention. AI agents for supply chain will also automate routine processes, enhance collaboration across stakeholders, provide actionable insights and independently act on those insights, ultimately transforming supply chain management into a more agile and responsive system.

As with all AI initiatives, however, a successful implementation requires organizations to build a solid foundation that integrates a robust data architecture with sound management practices and a continuous focus on reskilling team members. Addressing these foundational issues is crucial for maximizing the technology’s potential.

What is the difference between GenAI and agentic AI?

Within the last year, an increasing number of companies have adopted GenAI, which uses LLMs to generate content for task-oriented requests that don’t require ongoing adjustments or real-time decision-making. While agentic AI also relies on an LLM, it primarily uses the LLM as a central coordination module or a decision-making tool that interprets complex, goal-oriented requests and then autonomously plans and controls the execution of internal or external tools to achieve the desired outcome. The overarching focus is on autonomy, with the LLM taking ownership of understanding requests, planning actions, executing tasks and optimizing processes, with limited human oversight.

For example, imagine that a global manufacturing company is facing frequent disruptions in its supply chain due to fluctuating demand and supplier delays. In this example, the agentic AI system, leveraging its LLM as a coordination module, would autonomously analyze real-time data from various sources, including inventory levels, supplier performance and market trends. It would then generate a comprehensive plan that adjusts procurement schedules, reallocates resources and communicates with suppliers to achieve timely deliveries.

Throughout this process, the LLM would continuously monitor performance metrics and make real-time adjustments to optimize operations, all while providing human managers with insights and recommendations for oversight. This autonomous capability not only enhances efficiency but also empowers the company to respond to changing market conditions, ultimately driving greater resilience in its supply chain.

Agentic AI goes beyond predicting outcomes to offer guidance in facilitating the desired action within defined parameters. This new advancement will offer a significant advantage for supply chain managers, who often need to adjust in real-time to accommodate everchanging shifts in supply and demand. With the response already initiated by agentic AI, their primary task will be monitoring to see if the desired outcome was achieved.

Leading organizations foresee major improvements as agentic AI is deployed across several key supply chain areas, among them:

• Demand forecasting: By continuously analyzing real-time data from various sources, such as Internet of Things (IoT) devices, social media and market trends, agentic AI can integrate this information into forecasting models. This sets the stage for more accurate and timely forecasts that reflect current and imminent conditions, not just historical data.

• Logistics optimization: The ability of agentic AI to simulate various logistics scenarios and conduct what-if analyses will enable it to assess the impact of different variables, such as changes in demand and potential supply chain disruptions, on future logistics performance. Organizations can leverage this capability to support better strategic planning and more effective risk management.

• Inventory management: Agentic AI can dynamically monitor inventory levels by analyzing real-time data from sales trends, seasonality, market conditions and other sources. In addition to adjusting stock levels in real-time to meet demand, it can also automate the replenishment process, reducing the need for manual intervention and minimizing the risk of human error. Acting as a collaborative agent, AI can also facilitate communication and data sharing between businesses and suppliers, leading to improved coordination and more accurate inventory management across the supply chain.

• Predictive maintenance: Equipment failure and malfunctions plague even the most efficient supply chains. Using data from IoT sensors, equipment logs and environmental conditions, agentic AI can identify potential issues before they escalate into costly failures. The ability to provide predictive maintenance enables it to streamline automated repair scheduling and parts procurement, minimizing downtime.

• Supply chain resilience: By simulating various supplier scenarios and conducting what-if analyses, agentic AI can assess how changes in supplier performance and market conditions will impact future supply chain performance. Armed with this insight, organizations can plan strategically and better manage risk as they plan for the future. In addition, integrating supplier relationship insights with other business functions, such as procurement, logistics and production planning, supports a more holistic approach to supply chain management, enhancing organizational resilience.

Is agentic AI the next big thing in supply chains?

As agentic AI continues to rapidly evolve, we expect to see more organizations deploy this technology in their supply chains over the next 12–18 months. To validate the business case for this new technology, supply chain leaders must show how it will enhance key performance indicators (KPIs) and metrics. This includes demonstrating improvements in forecasting accuracy and making pre-emptive adjustments to operations to avoid downtime, or rerouting cargo to avoid disruption.

In addition, supply chain executives will also need to keep in mind that agentic AI may not fully replace existing resources, even as tasks are automated. While the technology can help deliver greater autonomy and efficiency in the supply chain, it will demand integration with existing workstreams and people, which includes the teams tasked with providing oversight. The human element will still be critical going forward, particularly as organizations seek to identify issues related to bias and hallucinations that have accompanied GenAI rollouts in the past.

Early adopters of the technology will also need to demonstrate how agentic AI can integrate with other AI-driven solutions to deliver a holistic view of the supply chain, improving forecasting accuracy, operational efficiency and risk management. This will empower them to create more resilient supply chains, equipping organizations to succeed in an increasingly volatile global landscape.