Why AI Agents Are the Key to an Agile Supply Chain

If you’re leading supply chains today, you know the pressure to be fast, flexible, and resilient. It’s a lot to manage, but you don’t have to do it alone. AI agents are stepping in to help you meet the challenges. 

These intelligent systems are changing the way businesses manage inventory, respond to disruptions, and make real-time decisions across global operations. By automating complex processes and continuously learning from data, AI agents offer you the intelligence and agility to keep your supply chain moving. 

How AI agents are used in supply chain  

AI agents are increasingly being integrated into supply chain systems to help organizations manage complexity, streamline operations, and adapt to rapid changes. These intelligent software programs operate autonomously or semi-autonomously, using real-time data and predefined goals to make decisions and carry out tasks across various stages of the supply chain. 

Rather than relying solely on human intervention, AI agents continuously monitor conditions, communicate with other systems, and respond dynamically to shifting demands or disruptions. In a typical supply chain environment, AI agents can be embedded across multiple functions, ranging from planning and procurement to logistics and fulfillment. They interact with both digital and physical components of the supply chain, gathering data from sensors, databases, and APIs to stay aware of current conditions. 

By acting as responsive decision-makers within a networked ecosystem, AI agents help supply chains operate with greater coordination, speed, and accuracy. As supply chains become more global and data-driven, AI agents offer a flexible and scalable way to handle growing operational demands.

In the rest of this article, we’ll explore how AI agents are essential for modern logistics. We’ll look at how they work, where they can add the most value, and how you can start using them to make your supply chain ready for the future.

How AI agents work

In the supply chain industry, AI agents work by continuously monitoring data across operations, such as inventory levels, shipping routes, supplier updates, and market demand, and making decisions that help optimize performance. 

To do this, they follow a perception–decision–action loop. Here what that looks like: 

1. Gathering data: They collect real-time information about their environment from sources like IoT sensors, ERP systems, and logistics platforms. 
2. Analyzing information: Next, they analyze this data using predefined rules, machine learning models, or optimization algorithms to determine the best course of action. 
3. Taking action: Once they’ve made a decision, they act on it, such as adjusting inventory orders, rerouting deliveries, or alerting a human operator. 
4. Adapting continuously: They then repeat this cycle to respond dynamically as conditions change.

In supply chain environments, AI agents are often specialized to handle specific functions, such as demand forecasting, warehouse coordination, or transportation management. Some may be simple rule-based systems that reorder stock when thresholds are met, while others are more advanced, using predictive analytics or reinforcement learning to optimize complex trade-offs like cost versus speed. These agents can also communicate with one another in multi-agent systems, such as coordinating between warehousing and logistics to ensure end-to-end alignment. 

By automating decision-making and adapting in real time, AI agents help supply chains stay agile, reduce waste, and respond more effectively to disruptions.

How AI agents are solving the challenges of traditional supply chain 

Traditional supply chains are often burdened by inefficiencies, delays, and limited visibility—all of which can lead to lost revenue, poor customer satisfaction, and wasted resources. AI agents offer intelligent, automated solutions to many of these long-standing issues, helping organizations modernize their operations and respond to change with greater speed and accuracy. 

Here are some key challenges that AI agents are helping to solve:

Lack of real-time visibility

Traditional supply chains often operate with delayed or incomplete data, making it difficult to detect disruptions or make timely decisions. AI agents continuously monitor inputs from sensors, inventory systems, logistics platforms, and external data sources, providing real-time insights across the entire supply chain. This level of visibility allows businesses to react quickly to inventory shortages, shipping delays, or shifts in demand.

Inefficient demand forecasting

Manual or spreadsheet-based forecasting methods rely heavily on historical data and lack the sophistication to factor in complex, real-world variables. AI agents enhance forecasting accuracy by analyzing large data sets, identifying trends, and adapting to changing consumer behavior. This leads to better stock levels, reduced waste, and fewer out-of-stock situations.

Siloed operations

Departments within traditional supply chains often function independently, with limited communication between sourcing, manufacturing, and logistics. AI agents can break down these silos by acting as intermediaries that share data, coordinate tasks, and align decision-making across teams. The result is a more synchronized and responsive supply chain.

Slow response to disruptions

When unexpected disruptions occur—like a supplier delay or port closure—traditional supply chains can struggle to adjust quickly, leading to production downtime or missed deliveries. AI agents use predictive analytics and scenario modeling to anticipate risks and propose alternative actions, such as rerouting shipments or reallocating inventory. This agility helps businesses maintain continuity and minimize losses.

Manual, repetitive processes

Many traditional supply chain tasks like order processing, invoice matching, or report generation are still done manually, consuming time and prone to error. AI agents can automate these workflows, reducing administrative overhead and freeing staff to focus on higher-value activities. Automation also improves consistency and reduces the risk of human error.

Limited scalability

Scaling traditional supply chains often requires adding more people and systems, which increases complexity and cost. AI agents make it possible to scale operations intelligently by handling higher volumes of data, automating decision-making, and optimizing resources dynamically. This allows businesses to grow without dramatically increasing operational overhead.

Benefits of using AI agents in supply chain 

As supply chains grow more complex, fast-moving, and data-intensive, businesses are turning to AI agents to gain better control, speed, and resilience across operations. These intelligent systems help manage and optimize various supply chain functions by making decisions, automating tasks, and learning from data. 

By embedding AI agents into their infrastructure, companies can unlock new levels of efficiency and adaptability that traditional tools and manual processes struggle to match.

Real-time decision-making

AI agents can analyze live data and make instant decisions without waiting for human input. This ability allows supply chains to react quickly to disruptions, shifting demand, or logistical changes, which ensures smoother operations and less downtime.

Increased efficiency and automation

By taking over repetitive tasks like order processing, routing, and inventory checks, AI agents reduce manual workload and improve operational speed. They streamline workflows, lower labor costs, and help organizations focus human resources on higher-value tasks.

Improved forecasting and planning

AI agents can detect patterns and trends in vast amounts of historical and real-time data. This improves demand forecasting, production scheduling, and inventory planning, leading to better resource allocation and fewer stockouts or overstocks.

Better demand planning and inventory management

By continuously analyzing sales trends, seasonal patterns, and external market signals, AI agents provide more accurate demand forecasts. This enables organizations to maintain optimal inventory levels, reduce excess stock, and avoid shortages that disrupt the supply chain.

Enhanced agility and responsiveness

Because AI agents continuously monitor and respond to changing conditions, they help supply chains stay agile. Whether adjusting delivery routes due to weather or switching suppliers in response to a disruption, AI agents support fast, flexible decision-making.

Streamlined logistics and transportation

AI agents can optimize shipping routes, delivery schedules, and freight loads by factoring in variables like fuel costs, traffic, and weather. This reduces transit time, lowers shipping expenses, and improves customer satisfaction with more reliable deliveries.

Better risk detection and mitigation

AI agents can identify early warning signs of supply chain disruptions—such as supplier delays, geopolitical risks, or equipment failures—and recommend proactive solutions. This leads to stronger risk management and fewer costly surprises.

More effective supplier coordination

With the ability to monitor supplier performance, track shipments, and automate communications, AI agents strengthen collaboration across the supply chain. This leads to faster issue resolution, stronger relationships, and better alignment on goals and timelines.

Greater coordination and visibility

In multi-agent systems, AI agents can share information and coordinate across departments, partners, and logistics providers. This enhances end-to-end visibility, improves communication, and supports more integrated supply chain strategies.

Scalability across operations

As businesses grow, AI agents can scale with them, enabling companies to handle increased data volume, expanded logistics networks, and more complex decision trees without additional strain. This makes them a cost-effective solution for evolving supply chain demands.

Specific use cases of AI agents in supply chain 

Many of the world’s most successful companies are turning to AI to improve supply chain visibility, speed, and resilience. From optimizing inventory and warehouse operations to streamlining logistics and supplier relationships, AI is helping industry leaders make smarter, faster decisions across their global supply chains. 

Here are a few standout examples of AI agents in the supply chain:

Walmart

Walmart is using AI to enhance nearly every layer of its supply chain. In its high-tech refrigerated warehouses, AI helps manage perishable inventory with greater precision by predicting demand and improving product flow. The company also uses generative AI for supplier negotiations and dynamic inventory forecasting based on local demand patterns.

Target

Target is applying AI to sharpen inventory forecasting, optimize supply chain logistics, and improve store operations. Its “Store Companion” generative AI tool helps employees streamline daily tasks, while AI-driven systems support efficient demand planning and order fulfillment through sortation centers.

UPS

UPS relies on AI to optimize delivery routes through its ORION (On-Road Integrated Optimization and Navigation) system, saving fuel and reducing delays. The logistics giant also uses AI and machine learning to match transportation capacity with shifting demand, improving flexibility and cost efficiency.

Procter & Gamble (P&G)

P&G uses AI for dynamic routing, sourcing optimization, and predictive supply chain planning. By incorporating AI into both logistics and product innovation, P&G has accelerated productivity and lowered costs across global operations.

DHL

DHL has integrated AI into its warehouse operations to improve efficiency and accuracy. The company uses AI-powered robots, such as DHLBots, capable of sorting over 1,000 parcels per hour with 99 percent accuracy, reducing missorting and the need for secondary sorting. DHL has also implemented generative AI tools for data cleansing and analysis, enabling faster and more accurate logistics solution designs.

Maersk

Maersk utilizes AI to streamline its global shipping operations. The company has developed AI-powered platforms to simplify complex customs processes, enhancing efficiency in international trade. Maersk also employs AI for predictive analytics, enabling the company to anticipate customer demand patterns and optimize service offerings, thereby improving customer satisfaction and loyalty.

Shein

Chinese fast-fashion giant Shein employs AI-powered algorithms to swiftly adapt to customer demand. By analyzing purchasing patterns and trends, Shein adjusts its supply chain in real-time, enabling the company to list up to 600,000 items on its platform and serve customers worldwide. This approach not only accelerates product availability but also reduces inventory waste by predicting demand more accurately.

Coles

Australian retailer Coles has adopted AI and machine learning to forecast consumer demand for beer, wine, and spirits in its liquor stores. By factoring in data from events, weather patterns, promotions, and past sales, the system enhances supply chain efficiency by alerting suppliers in advance. This AI-driven forecasting and automated ordering platform aims to ensure adequate stock, particularly around key events like New Year’s Eve and Christmas.

Amazon

Amazon has integrated AI across its supply chain to enhance efficiency and reduce costs. The company has deployed over 750,000 robots in its fulfillment centers, performing tasks such as sorting, transporting, and packing packages. These robots, powered by advanced AI systems, have led to a 25 percent reduction in order fulfillment costs and are projected to save $10 billion annually by 2030. Additionally, Amazon uses AI for predictive inventory management, tailoring stock levels to regional demand and minimizing out-of-stock occurrences.

The future of AI in supply chain 

Over the next two years, AI agents are expected to play an increasingly strategic role in supply chain operations. Rather than being limited to isolated tasks like routing or forecasting, these agents will evolve into more autonomous, collaborative systems capable of end-to-end decision-making across sourcing, production, logistics, and demand planning. 

As supply chains become more digitized and data-rich, AI agents will be better equipped to anticipate disruptions, simulate alternate scenarios, and recommend real-time corrective actions, transforming supply chains from reactive to proactive networks.

We can also expect to see tighter integration between AI agents and emerging technologies like digital twins, edge computing, and blockchain. AI agents will be embedded at key supply chain nodes, using sensor and transactional data to optimize performance and transparency at the local level while contributing to global decision-making. 

Collaboration among multi-agent systems will become more advanced, enabling intelligent coordination between suppliers, manufacturers, and distributors without constant human intervention. The near future of AI in supply chain will bring faster decisions, fewer disruptions, and more agile, self-optimizing networks.

Reinvent your supply chain with Domo AI

AI agents are reshaping the future of supply chain management by bringing intelligence, speed, and automation to traditionally complex and fragmented processes. From enhancing visibility and forecasting to streamlining logistics and boosting resilience, AI-driven systems empower businesses to make faster, smarter decisions at every stage of the supply chain. As this technology evolves, organizations that embrace AI agents now will be better positioned to adapt to disruptions, meet customer demands, and drive continuous improvement.

Domo helps companies unlock the full potential of AI with tools that bring data, people, and systems together in real time. Explore how Domo’s AI capabilities can transform your supply chain at domo.com/ai.