AI agents in the supply chain

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.¹ 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 2024.²

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.

This shift is reflected in several consistent ways across supply chain management:

• Autonomous actions: Agents can execute decisions within defined limits, shifting humans toward oversight and exceptions.
  
  
• Built-in adaptability: Agents assess uncertainty and adjust their actions as market conditions change, supporting ongoing risk mitigation.
  
  
• Connected functions: Agents share context across planning, sourcing, manufacturing and logistics, which helps break down silos.
  
  
• Real-time decisions: AI agents continuously monitor conditions and adjust plans, reducing reliance on static planning cycles.

In practice, AI agents are applied across demand forecasting, inventory management, production and logistics planning. They combine real-time data from enterprise systems, sensors and Internet of Things (IoT) devices with machine learning, predictive analytics, optimization and reasoning models to evaluate tradeoffs and recommend or run actions. This process allows decisions to be made closer to the point of impact, improving speed and consistency across the supply chain.

Risk mitigation is a central role of AI agents in supply chain management. Agents continuously monitor signals such as demand volatility, material shortages, operational bottlenecks, external factors and other potential disruptions. When risks emerge, they assess potential impacts, explore alternative scenarios and adjust plans in near real time. They can reroute shipments, suggest alternative suppliers or rebalance inventory as conditions evolve. As a result, supply chains are better equipped to absorb shocks from weather events, geopolitical issues or sudden changes in demand.

Another important impact is improved visibility and coordination. AI agents connect data from enterprise resource planning (ERP) systems, warehouse management, logistics platforms and external sources. By reducing silos, they make information more actionable across teams. This shared visibility supports better alignment between planning, operations and leadership and other stakeholders.

Despite their promise, AI agent initiatives are not plug-and-play solutions. Effective use depends on strong data foundations, careful system integration and clear guardrails for autonomous behavior. Human oversight remains essential, particularly for complex or high-impact decisions.

When implemented thoughtfully, AI agents can become a durable part of the supply chain operating model. Organizations that pair technical capability with clear governance and human-AI collaboration are better positioned to scale these systems responsibly over time.

AI agents change how supply chains operate at a fundamental level. Traditional supply chain management relies on periodic planning cycles and human-led decision points. Data is reviewed. Decisions are made. Actions are slow to reach execution. AI agents replace this model with continuous sensing, evaluation and response.

Supply chains become more dynamic and active rather than reactive. In fact, 62% of supply chain leaders recognize that AI agents embedded into operational workflows accelerate speed to action, hastening decision-making, recommendations and communications.¹

This shift also changes how decisions are distributed across the organization. Instead of relying on planners to interpret dashboards and manually trigger actions, AI agents can act directly within defined boundaries. Human teams spend less time on routine decisions and more time setting objectives, managing exceptions and overseeing outcomes.

AI agents also alter how different supply chain functions work together. Planning, procurement, manufacturing and logistics have traditionally operated as separate supply chain processes. AI agents can share data, context and intent across these areas in real time. This process enables more coordinated decisions and clearer tradeoffs across the end-to-end supply chain.

Another major change is how supply chain disruptions and uncertainty are treated. Traditional systems often assume stable conditions and break down when inputs quickly change. AI agents are designed to operate in environments with constant variability. They monitor signals, reassess assumptions and adjust actions as conditions shift. These abilities make adaptability a built-in feature rather than a reactive response.

Understanding AI agents is important because they introduce new operational and governance considerations. Data quality, system integration and trust become central concerns. Organizations must define rules for autonomous actions and help ensure decision-making transparency.

AI agents are increasingly applied across key supply chain functions, helping organizations act faster, make better decisions and reduce risk. These focused applications show where agentic AI is already reshaping operations today.

AI agents continuously update forecasts by using historical data and real-time signals such as market trends and promotions. This process allows organizations to quickly adjust inventory, production and replenishment plans. Large retailers, for example, are using AI agents to forecast store-level demand and dynamically aligning stock with changing customer behavior.

AI agents monitor supplier performance, pricing trends and potential risks. They can suggest alternative suppliers or providers, flag delays and proactively adjust sourcing strategies. Manufacturers deploy agentic systems to reduce supply disruptions, assess geopolitical risks and help ensure materials are available when needed.

In production, AI agents optimize schedules based on demand, material availability and capacity constraints. When unexpected events occur, they recalculate plans and suggest alternatives, reducing downtime. Manufacturers use these agents to improve output and respond faster to last-minute order changes.

AI agents manage inventory flows, storage locations and order picking priorities. By analyzing inbound and outbound demand in real time, they improve operational efficiency and reduce handling costs. AI agents are used in large fulfillment centers to streamline inventory placement, automate picking and speed up order processing.

Agents monitor shipments, carrier performance and route conditions to identify and resolve disruptions. They can recommend or execute rerouting to keep deliveries on schedule. AI-powered agents can maintain real-time visibility and adjust routes when delays or disruptions occur.

As part of risk management, AI agents continuously scan internal and external data for potential issues, including supplier reliability, transportation disruptions and weather events. They can assess the impact and propose mitigation actions, helping organizations manage uncertainty before problems escalate.

We’ve highlighted the many advantages of AI agents in the supply chain. When viewed together, it’s easy to see why agentic approaches are gaining traction across supply chain functions.

Better coordination: Agents share data and insights across supply chain functions, improving alignment and helping to break down operational silos.

Cost savings: Supply chain optimization of inventory, production and logistics helps reduce waste, overstock and stockouts, unnecessary transportation costs and emissions. These improvements support both operational efficiency and long-term sustainability goals.

Faster decision-making: AI agents evaluate data continuously and act in real time, reducing delays between insight and execution and enabling quicker responses to change.

Improved accuracy: By combining machine learning with reasoning and optimization models, AI agents reduce errors in forecasting, scheduling and planning.

Increased resilience: AI agents can detect disruptions early and adjust actions as conditions change, helping supply chains maintain operations during unexpected events.

Risk mitigation: Continuous monitoring of internal and external signals enables earlier risk detection and more proactive management of potential issues.

Scalability: Once effective decision patterns are established, AI agents can apply them quickly across multiple locations, products or regions.

Support for human teams: By handling routine tasks including analysis and execution, AI agents allow human teams to focus on strategic decisions, judgment and exception management. 76% of CSCOs say that AI agents that perform impact-based repetitive tasks at a faster pace than people will improve their overall process efficiency.¹

The role of AI agents in supply chains is poised to expand dramatically in the coming years. Today, most agents are applied to individual functions, such as demand planning or logistics. The future of supply chain management will see more AI agent orchestration. Specialized agents will work together end-to-end and even with agents in other business areas like sales, procurement and product development. This coordination will help create fully integrated, intelligent and adaptive systems. Coming uses include:

In manufacturing, AI agents will optimize workflows, production sequences and quality control. They can detect defects, trigger maintenance, adjust production parameters and communicate directly with contractors’ AI systems to help ensure that service level agreements are met. This enables production that is faster, more precise and more resilient to disruptions.

Transportation will benefit from AI agents that optimize routes, delivery schedules and fuel usage in real time. By analyzing structured and unstructured data—including weather reports, news and traffic alerts—agents can proactively reroute shipments or adjust inventory strategies. For instance, they might respond immediately to port closures or shipping delays, minimizing disruption and cost.

Tomorrow’s supply chains will feature AI agents that collaborate seamlessly across functions. Agents in demand forecasting, production, warehousing and logistics will share real-time data, coordinate decisions and dynamically adjust plans across the broader supply chain ecosystem. This level of integration will allow organizations to instantly respond to changes, optimize resources globally and align operations with strategic goals.

Future AI agents will synchronize production, logistics and planning. They will assess multiple scenarios, balancing capacity, raw materials and service level requirements to recommend optimal action plans. Forecasting will also incorporate real-time internal and external data streams, from social media sentiment to news reports. This information will help organizations anticipate demand fluctuations and instantly adjust operations.

AI agents will increasingly influence product innovation. By analyzing customer feedback, market trends and performance data, agents can suggest improvements or inspire new product designs. For example, in consumer goods, agents might recommend ingredient substitutions or new formulations based on emerging consumer preferences or material availability, accelerating innovation and boosting customer satisfaction.

AI agents will continuously adapt warehouse operations based on demand, inventory levels and real-time events. They will coordinate with robotics and automation systems to optimize storage, picking and packing. For example, agents might reposition high-demand products closer to loading docks or automate the entire fulfillment process, increasing speed and reducing errors.