Agentic AI systems are becoming more ingrained in supply chain management; so much so that by 2030, 50% of cross-functional supply chain management (SCM) solutions will use intelligent agents to autonomously execute decisions in the ecosystem, according to Gartner..
This technology provides a virtual workforce of AI agents that can assist, offload and augment human work or traditional applications. Agentic AI systems are expected to deliver more adaptable software, capable of completing a wide variety of tasks in complex supply chain environments, dramatically increasing AI’s potential to execute and deliver results.
“Supply chain technology investments need to improve efficiency or contribute to profit growth in today’s climate of uncertainty,” said Kaitlynn Sommers, senior director analyst in Gartner's supply chain practice, in a statement. “Agentic AI has the potential to do both by providing a new means to enhance resource efficiency, automate complex tasks, and introduce new business models across supply chains.”
In the first stage of agentic AI, AI agents emerge as task-specific intelligent assistants designed to aid humans. An example would be a procurement agent that is tasked to autonomously purchase supplies based on inventory stock levels, projected demand, and market conditions.
Often cross-functional SCM solutions designed to close the technology gap between planning and execution reflect the complexity of the supply chain environment and work against maintaining the flexibility of the supply chain technology stack. In contrast, intelligent agents will evolve to manage greater levels of complexity and provide increased autonomy and interconnectivity, making decisions and taking action as directed by the organization.
Sommers added: “Agentic AI represents a revolution from robotic process automation (RPA) as the AI agents will autonomously complete tasks without relying on explicit inputs or predefined outcomes. Agents will continuously learn from real-time data and adapt to evolving conditions and complex demands. Chief supply chain officers (CSCOs) at the forefront of this next wave in the evolution of generative AI (GenAI) can achieve maximum value.”
For supply chain chief officers exploring agentic AI, Gartner has three recommendations:
- Prioritize Use Cases: Seek out opportunities to integrate agentic AI into workflows that demand significant scalability, efficiency, and adaptability. Start small on use cases in which high-quality data is accessible, behavior is verifiable, and a high return on investment is anticipated.
- Integrate AI Agents into Workflows: Consider AI agents as primary digital collaborators, assigning tasks to them to enhance productivity. Reassess collaboration models, workflows, and team dynamics to fully exploit AI agents' capabilities in identifying and acting upon secondary events that might escape human attention.
- Define Operational Parameters: Set clear guidelines to ensure that agentic AI functions within a specified scope and set of capabilities. This is crucial to ensure the AI agents don’t take incorrect actions that could result in negative consequences.
