Agentic AI poised to take supply chain decision-making to the next level

Editor’s note: A version of this article first appeared in the Straight Talk newsletter on May 4. To sign up for the weekly newsletter and receive news like this before others, click here.

Artificial intelligence has been shaping modern supply chain for many years, evolving from rule-based automation to predictive and generative capabilities. But a new frontier is emerging—Agentic AI—and it is moving the AI discussion into overdrive.

Agentic AI moves the artificial intelligence movement from insights and recommendations to actual decision-making and execution. According to Pushpinder Singh, global supply chain transformation leader at IBM Consulting, Agentic AI isn’t just smarter—it’s active, adaptive, and poised to transform the enterprise at a foundational level.

What is Agentic AI?

To understand Agentic AI, it’s helpful to trace the evolution of AI:

• Traditional AI: Built on predefined rules, with humans responsible for final decisions.
• Machine learning: Learns from previous decisions and provides recommendations.
• Generative AI: Adds capabilities to generate content—emails, reports, images—but still requires human action.
• Agentic AI: All of the above, plus the ability to perceive, reason, act, and learn autonomously.

“Agentic AI is not just a ghost,” Singh explains. “It can perceive environments, decide an execute … when we talk about an agent, we are not talking about it in isolation.”

Importantly, though, is the reason a company is seeking to build an Agentic AI system. A company needs to answer the why question: “Why are we building it and what’s the purpose of the agent,” Singh says.

This concept of agents that interact with systems independently and evolve with each interaction is rapidly gaining traction, particularly in high-complexity environments like the supply chain. These systems can, for instance, identify a shortage of product between Houston and Chicago, evaluate demand for that product, and reallocate inventory accordingly without human intervention.

The 5 components of Agentic AI

Singh outlined a five-part framework that defines a functioning AI agent:

1. LLM core. The language processing and reasoning engine.
2. Planning capability. Ability to decompose tasks into smaller steps.
3. Integration engine. Connects with enterprise systems to execute decisions.
4. Memory. Retains knowledge from past instructions and decisions.
5. Feedback loop. Learns from outcomes to improve future performance.

“It’s a package,” Singh says. “You need all five for it to really be agentic.”

Without system integration, for example, an AI can’t act—only suggest. But with integration and feedback mechanisms in place, agents can carry out tasks like sending replenishment orders, notifying suppliers, or rerouting shipments, all while learning from each decision.

Where Agentic AI fits in the supply chain

Early adoption is happening in areas with high data volumes, subjectivity, or human fatigue—procurement, customer service, and logistics operations among them. Singh noted that businesses are currently deploying agents in limited-use cases with a human-in-the-loop approach, ensuring safety while building trust in outcomes.

Adoption timeline and strategic considerations

According to Singh, enterprises could see tangible benefits in 3–6 months after deploying AI agents in targeted scenarios. But he advises organizations to think bigger: “When a corporation is thinking about making a big decision, they need to look at what the landscape will look like in the two- to five-year horizon,” he says.

With many enterprises juggling 20 to 40 systems, Agentic AI’s ability to connect, reason, and act across silos offers significant promise. The key is identifying the right entry points and having a clear business case.

Singh also recommends working with experienced partners who can bring design thinking into the process. “Engage with a partner that can help you understand these nuances and do some design thinking—what kind of value can it bring for you so you are not relearning something that you already have a playbook for,” he says.

What’s next: Agent-to-agent collaboration

The future Singh envisions is not just about smart agents operating independently—it’s about agents collaborating. Singh believes we’ll begin to see ecosystems of agents working together across procurement, logistics, inventory management, and more.

This distributed decision-making could radically reduce the burden on human operators. A person might have 10,000 interactions in the supply chain, Singh notes, and while they can’t see all of them, the agents will.

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