In a time of economic uncertainty, labour shortages, and complete redressing of traditional global supply chain relations, companies worldwide are being forced to reimagine their supply chains. As new technologies support transitions and improve efficiency, businesses are not only discovering the benefits of AI-driven automation, but are starting to leverage agentic AI to improve operations and make workplaces more satisfying for their staff.
Agentic AI is a cutting-edge form of artificial intelligence that is poised to become a game-changer across the global supply chain landscape. Operating autonomously to achieve defined goals while adapting to real-time data and changing conditions, agentic AI offers more than speed and accuracy. It unlocks a new level of strategic decision-making, collaboration, and human empowerment.
What is Agentic AI?
Unlike traditional automation, which executes pre-programmed tasks, agentic AI behaves like an agent, or another member of staff that is capable of making decisions, learning from outcomes, and interacting with humans to drive toward a goal. These AI agents aren’t bound to rigid, predefined workflows, but are designed to adapt to the shifting and nuanced real-world conditions that characterise modern supply chains.
In the context of Source-to-Pay (S2P) operations, agentic AI can be used in the form of multiple specialized agents tasked with specific tasks such as sourcing, contracting, purchasing, and payments. Their activities streamline into creating a more responsive, scalable supply chain where employees can shift their focus from repetitive tasks to more strategic activities. Invoice matching, contract compliance checks, and basic supplier communications can all be handled by AI agents thus freeing up human teams to focus on relationship-building, negotiations, and innovation.
A procurement manager could, for example, use agentic AI to comb through thousands of line items to identify underperforming suppliers or upcoming contract renewals. By asking the AI agent for a prioritised action list, complete with negotiation levers based on past supplier behaviour, they are able to enter into negotiations fully briefed and informed without spending hours on research.
Agentic AI can transform several areas of the supply chain
Businesses are discovering that intelligent agents can support them across the supply chain. Starting with supplier negotiations. AI agents analyse pricing trends, historical purchase data, and vendor performance to generate negotiation strategies that human teams can then action. Some teams may even use AI to simulate negotiation outcomes. Agentic AI is also supporting inventory management, helping to adjust stock levels dynamically, based on real-time demand, supplier delays, or transportation bottlenecks. In particular, for US retailers, this means avoiding both overstock penalties and stockouts driven by sudden price hikes caused by new tariffs.
AI is also helping monitor thousands of contracts for key dates, performance clauses, and risks. Automatic flagging if a supplier isn’t meeting KPIs or if a better deal is available can help teams move quickly to protect the bottom line or improve margins Finally, as companies in the US and worldwide navigate complex changing tariffs, federal, state, and international regulations AI agents to track compliance and audit trails may prove invaluable.
Training and empowering the workforce
Rather than posing a threat, agentic AI enhances human performance. As these intelligent agents interact with employees, they offer real-time feedback, suggest best practices, and provide context-specific guidance. In effect, they serve as on-the-job trainers for both seasoned professionals and new hires. For instance, in a high-volume purchasing environment, an AI agent might flag that a junior buyer is overpaying on a common part and recommend an alternate supplier or negotiation strategy. Over time, these alerts compound into real skill development, supporting employee training. Some companies are even embedding agentic AI into their internal learning systems, creating personalised training paths based on job performance, knowledge gaps, and career aspirations.
The challenges ahead
As powerful as agentic AI is, companies face real barriers to adoption. Many supply chains still suffer from fragmented or outdated data systems that impact data quality. For AI agents to be effective, clean, accessible, and well-governed data is essential. In addition to this, training agents to make business-appropriate decisions requires a deep understanding of industry nuance. AI developers must work closely with operations teams to design models that align with risk tolerance and business objectives. Finally, because supply chains are dynamic, AI models must be retrained frequently to stay relevant. That means building pipelines for feedback loops, human oversight, and ethical decision-making.
The ethics of Agentic AI
Accountability poses a significant ethical concern and represents a barrier for adoption. As AI lacks moral reasoning, it is necessary to define where accountability for its actions lie. Another key concern is transparency. Too often AI systems are perceived as “black boxes” and their reasoning is difficult to explain, additional transparency showing that the agents are operating in alignment with ethical and legal standards is required.
The human-AI alliance
Narrative around AI is often polarized—either seen as a job killer or a magic bullet. Agentic AI tells a different story. It’s not about replacing people, but amplifying them. It handles the tedious tasks so people can do what they do best: build relationships, solve problems, and make judgment calls.
For businesses, the road to fully realising agentic AI’s potential requires not only investment in tech, but also in people, data, and culture. Those who make that investment stand to gain a powerful edge in global competitiveness, operational resilience, and workforce engagement.