Next Big Leap: Agentic AI in Supply Chain Automation

Published Date : 09/06/2025 

In less than three years, the rise of agentic AI has transformed the way supply chains operate, from procurement to risk assessment and planning. Discover how these autonomous systems are revolutionizing the industry. 

Keeping up with advances in artificial intelligence these days feels like chasing a bus from stop to stop, just missing it each time as it pulls away from the curb. It’s been less than three years since the public release of ChatGPT, the breakthrough that redefined the way humans interact with AI. Even more recently, having barely had time to digest that innovation, we’ve seen the emergence of agentic AI, which signals the arrival of “autonomous” systems that appear, in many cases, to dispense with the need for humans altogether.

AI agents are models that can carry out discrete tasks with minimal human intervention. They can learn from past interactions, understand the deeper context of a given scenario, and adapt strategies in pursuit of an assigned objective. Pushpinder Singh, global supply chain transformation leader with IBM Consulting, says agentic AI displays a “high-intensive, human brainpower-type of activity.” In the process, it takes over and improves upon what were previously manual activities.

It's been less than a year since agentic AI burst on the scene, and the technology is far from achieving its full potential, Singh says. “A lot has been happening in the last six to nine months. Clients are getting a good idea about how this can help.” In the supply chain, agentic AI is beginning to make its mark in procurement, Singh says. It’s helping to create and analyze contracts with suppliers — a crucial aid for manufacturers that are rethinking their sourcing strategies in light of high tariffs and other disruptions in global trade.

A second early application of agentic AI in the supply chain is assessing risk, especially among third parties. Organizations are in dire need of tools for auditing supply quality, and complying with ever-changing regulations. Massive amounts of data need to be centralized for easy access and analysis — a task that’s beyond the ability of mere humans to perform. Yet another potential role for AI agents is in supply chain planning, Singh says. No production or demand plan is ever fixed in stone, of course, but agentic AI can theoretically respond much more quickly to changing demand signals. That would better position manufacturers to handle both sudden surges and fall-offs in buying patterns, whether the result of seasonal trends, supply chain disruptions, or the unexpected popularity of a particular product. For their part, distributors and retailers could shift inventory to warehouses and stores where it’s most needed.

To create an AI agent for a specific task, the user needs to provide “constructive information,” Singh says. In the past, that would entail extensive efforts to train the models in expectation of receiving the desired output. But today’s AI models are becoming more capable of self-learning, and need a lot less data upfront to get up to speed and proceed with a given task. The downside of today’s AI technology lies in its propensity for “hallucinating” — issuing conclusions or recommendations that are demonstrably wrong. Even the newest and most powerful applications of generative AI are deficient in this way. But Singh believes hallucinations are more common in AI systems with broader application, and that the kind of task-oriented models typified by agentic AI are less likely to be guilty of such lapses. Examples include an agent that’s only responsible for making changes in a production schedule, or overseeing interactions with suppliers.

In the coming months, Singh predicts, agentic AI will undergo huge leaps in sophistication and applications for the supply chain. Thanks to the system’s ability to learn, training cycles are growing shorter, requiring in some cases only a couple of weeks to achieve a reasonable level of accuracy. With the help of agentic AI, “we’re going through a completely new supply chain operational paradigm,” Singh says. “We’ll be seeing agents by platform providers, and scenarios where agents interface with each other, and can improve drastically in terms of turnaround time.” A typical supply chain of the near future could employ hundreds of AI agents to perform multiple discrete tasks. Says Singh: “It’s going to get more exciting.” 

Frequently Asked Questions (FAQS): 

Q: What is agentic AI?

A: Agentic AI refers to autonomous systems that can perform specific tasks with minimal human intervention. These AI models can learn from past interactions, understand context, and adapt strategies to achieve assigned objectives.

Q: How is agentic AI being used in supply chain procurement?

A: Agentic AI is helping to create and analyze contracts with suppliers, which is particularly useful for manufacturers rethinking their sourcing strategies due to high tariffs and other global trade disruptions.

Q: What are the key applications of agentic AI in supply chain risk assessment?

A: Agentic AI is used to assess risks among third parties, audit supply quality, and comply with ever-changing regulations. It centralizes and analyzes massive amounts of data, tasks that are beyond human capabilities.

Q: How does agentic AI improve supply chain planning?

A: Agentic AI can respond more quickly to changing demand signals, helping manufacturers handle surges and fall-offs in buying patterns. This better positions them to manage seasonal trends, supply chain disruptions, and unexpected product popularity.

Q: What are the challenges of implementing agentic AI?

A: One major challenge is the propensity for AI to 'hallucinate' or provide incorrect conclusions. However, task-oriented models like agentic AI are less likely to have such issues compared to broader AI systems. 

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