AI Agents in Supply Chain | IBM

Executive Summary

Artificial intelligence (AI) agents in supply chain are autonomous software systems that use data, models and reasoning to monitor conditions, mitigate risk, make decisions and act across supply chain functions in real time. AI agents operate within defined goals and constraints set by the organization. These abilities allow them to respond to change faster than systems that rely on human intervention. AI is already widely used for supply chain optimization and analytics across operations. In fact, organizations with higher investment in AI-driven supply chain operations reported revenue growth 61% greater than their peers. These systems increasingly include generative AI capabilities, such as large language models (LLMs), which allow users to interact with data and systems through natural language. Unlike traditional AI or rule-based automation built on static algorithms, AI agents operate with a degree of autonomy—or “agency.” They can perceive incoming data, reason about possible actions and act in context rather than following fixed instructions. This capability is driving broader adoption, with Gartner predicting that by 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024. AI agents are becoming a key tool for addressing complexity and uncertainty across global supply chains that span regions, partners and regulatory environments. Traditional systems often react slowly and rely on human interpretation of reports and alerts. AI agents connect data sources across functions and organizations, making decisions that reflect the broader supply chain ecosystem rather than isolated processes. These features move supply chains from reactive operations toward more adaptive and proactive systems.