AI-supported decision-making in supply chain management has never been more urgent. As supply chain volatility and uncertainty reach unprecedented levels, companies that hesitate to embrace AI risk falling behind their competitors.
Supply chain design is a complex process. It requires supply chain leadership working closely with stakeholders in procurement and finance to create and implement a supply chain that drives profitable growth. As a supply chain leader, you’re tasked with improving strategic decisions while managing tight budgets. You likely see the potential of AI, but it’s tough to sort through the hype and know how to evaluate vendors that will truly help you achieve your long-term business objectives.
Meanwhile, the supply chain modelers fielding urgent requests feel like they’re drowning. Every question, whether it’s about how delays will impact costs or whether the company should shift production to other regions, requires building complex models from scratch, running hundreds of scenarios, and trying to distill insights into executive-friendly summaries. And it’s not a job just anyone can do; supply chain design is intricate and requires deep expertise to understand causality. By the time a new scenario is accurately modeled, the situation on the ground may have already changed.
But the good news is, AI excels at sorting through data and can be an indispensable partner in supply chain management.
Keep reading to learn:
- Why it’s essential to embrace AI now
- The benefits and limitations of various types of AI in supply chain management
- The role of agentic AI in improving supply chain design and management decisions
- How you can start embracing AI in a smart, practical way
The growing role of AI in supply chain management and decision-making
AI in supply chain management can analyze the complex nuances of supply chain operations — including shipping and logistics, warehouse and distribution center placement, tariff impacts, and more — to simplify and optimize planning and operations. In the face of competing priorities, it can help leaders understand their best options and recommend what changes to make to achieve specific business goals.
In supply chain modeling, AI and machine learning (ML) can enable more accurate predictions, enhance decision-making, and improve real-time optimization in supply chain management and operations. These technologies help companies anticipate disruptions, reduce costs, and make strategic decisions to improve network configuration, inventory, and transportation.
How has AI in supply chain evolved over time?
AI has been a part of supply chain management software for decades. Early on, analysts used room-sized computers to solve inventory management problems with linear programming and optimization techniques. That technology developed steadily over time, and now we have AI that is increasingly generative and autonomous.
AI technology is becoming a more common and important tool for managing global, complex, multi-tiered supply chains. As business dynamics get more complicated, more advanced technology is also available to more people. In other words, as the world has gotten more complex, AI has gotten more advanced and is even more essential to optimizing supply chains in a world rife with uncertainty.
Why is AI essential for supply chain management now?
AI is critical for successful supply chain management now for a few reasons:
- The large volume of data points and the complexity and interconnectedness of global supply chains
- The pace of change in modern economies and businesses
- The tech-readiness of global businesses: AI chips or GPUs are becoming more accessible, the cost of storage and computing is decreasing, and advanced technology is expected of today’s businesses if they want to remain competitive.
AI capabilities are essential to establishing and maintaining more adaptive, resilient supply chains.
AI can process vast amounts of data that would take humans weeks or months to sort through. It can mine for trends and patterns that human eyes wouldn’t necessarily notice. The ability to process and sort through this amount of data opens up a path to more accurate and nuanced predictions. This means that supply chain managers and modelers can identify potential supply bottlenecks before they occur, dynamically optimize logistics and distribution networks, forecast changes in demand with greater precision, and even help companies balance costs with carbon emissions. AI capabilities are essential to establishing and maintaining more adaptive, resilient supply chains.
What types of AI technology are typically used in supply chain management?
Today, when many people think of AI, they think of ChatGPT or large language models (LLMs), but LLMs are just one aspect of AI technology. This section will detail several types of AI technology typically used in supply chain management, including the benefits and limitations of each type.
| AI in Supply Chain | Description | Supply Chain Applications | Limitations |
| Machine Learning | Enables machines to learn from data without being directly programmed. Algorithms improve over time as they are exposed to more data. | Helpful for demand forecasting by analyzing historical data to predict equipment failures and maintenance needs in production facilities, improve inventory and logistics optimization, detect quality control issues, and help with risk management. | Requires high-quality and clean data, and they may struggle with more complex processes and interconnected variables. Can also be difficult to interpret without the right expertise. |
| Deep Learning | A specialized form of ML that mimics the human brain’s structure. Involves artificial neural networks with multiple layers. Can effectively process complex data like images, speech, and natural language. | Good for complex pattern recognition to help with optimization and produce predictions based on raw data, such as to optimize transportation routes based on traffic or weather and reduce excess inventory by learning from lead times or historical sales.
Also helpful for analyzing images and text to aid in tasks like quality control and document processing. |
Not immune from the data requirements and interpretability challenges of ML.
Requires substantial computational resources and time, and — on its own — is limited in supply chain scenario planning, especially across varied supply chain settings. |
| Generative AI | An advanced technology that creates new content, such as text, images, numerical data, video, or audio based on data on which it has been trained. Not a new technology, but becoming more widely adopted every day. | Great ability to classify and categorize information, analyze and modify strategies and resource allocations, summarize large volumes of data, and extract key insights and trends quickly. The benefits to supply chain management could be enormous, especially in logistics network design, global trade optimization, manufacturing, sourcing, supply chain planning, and supply chain design. | Only as powerful as the data on which it’s trained. Any blind spots in the end-to-end supply chain may produce inaccurate information.
There may also be ethical and legal considerations. One of the biggest challenges with GenAI tools in supply chain is that without the proper constraints and without mathematical optimization, they may invent false information. |
| Reinforcement Learning | A type of machine learning where a computer program tests different actions and learns to make decisions based on which actions lead to the best outcomes. It learns from the results, getting better over time at selecting the right actions to achieve set goals. | Can help companies with inventory management, dynamic pricing and revenue management, production scheduling and resource allocation, logistics and transportation, and demand forecasting and order fulfillment. | Could become unstable and potentially even collapse if not combined with other types of learning. This is because they can become overly sensitive to small changes or overconfident in predictions that turn out to be incorrect. |
| AI Agents | Autonomous or semi-autonomous software entities that use AI techniques to perceive, make decisions, take actions, and achieve goals in their digital or physical environments. | When built properly, AI agents can combine the best of machine learning, deep learning, reinforcement learning, generative AI, and mathematical optimization. | Limitations of AI agents relate to how the agents are built. Some might have limited use cases or siloed applications and depend solely on GenAI. A big limitation for AI agents can be if they are built as an afterthought and not as a foundational feature of a supply chain management solution. |
How does agentic AI improve supply chains?
Agentic AI improves supply chains in several ways: by improving decision-making, automating complex tasks, improving efficiency, proactively identifying risk, reducing emissions and waste, and making it easier for more teams to benefit from supply chain modeling. Somewhat like a network of human specialists or experts in specific fields, agentic AI relies on various specialized agents that autonomously handle specific tasks based on their unique capabilities. User prompts flow through an orchestration layer that intelligently routes requests to the right agent, or a coordinated team of agents, to deliver the requested task or action.
AI agents have been with us for a while, but their effectiveness in helping companies achieve business goals is extraordinarily promising. AI agents can act as a partner or collaborator in tech-enabled supply chain management operations and planning efforts. They’re more than just a chatbot: They can help companies run what-if scenarios to understand what might happen if a trade route is shut down because of shipping interruptions or direct material shortages. They can help manage risk, suggest several courses of action, and help organizations prepare for scenarios their supply chain modelers might not have considered.
For supply chain modelers, AI helps lessen the steep learning curve of building supply chain models, alleviates the burden of cumbersome data analysis, reduces the run-time of models, and helps them make better trade-off decisions and present crisp summaries to leaders.
For chief supply chain officers, AI can improve supply chain agility and resilience, help reduce costs, and provide insights that can contribute to a competitive advantage. AI-powered decision support can help these leaders improve everything from inventory levels to optimizing for uncertain demand.
Here’s a more detailed breakdown:
Improve supply chain scenario analysis and decision-making
Agentic AI helps businesses make smarter, more profitable decisions across their entire supply chain. It can identify opportunities competitors might miss, driving strategic growth and improving resilience. It helps supply chain organizations analyze multiple scenarios across different objectives — maximizing profit, minimizing costs, achieving sustainability goals, managing risk, reaching service targets, or finding a balance between all.
For example, AI offers predictive analytics that can help supply chain leaders get better at forecasting future expenses, market trends, and pricing patterns, reduce supply chain bottlenecks, and create efficiencies that contribute to meaningful revenue increases. This AI-supported approach ultimately drives sustainable long-term business growth.
Sophisticated supply chain modeling is also crucial to helping companies achieve increasingly aggressive sustainability goals. AI-powered supply chain models help businesses design more environmentally-friendly supply chains by allowing them to optimize emissions, reduce materials and water waste, and improve compliance alongside other strategic objectives. By incorporating emissions data on multiple levels, leaders make more informed decisions on transportation modes and logistics while maintaining efficiency and cost-effectiveness.
Automate complex supply chain modeling
Agentic AI can automate complex tasks like supply chain modeling and scenario analysis, which ultimately helps businesses focus on the most strategic initiatives. Automation is key for repetitive tasks and workflows; it helps reduce operational bottlenecks, lowers costs, and enhances overall efficiency.
According to The Hackett Group, companies that embrace AI devote 30% fewer resources to transactional activities, which allows them to reinvest those resources in higher-value activities, such as planning, analysis, and risk management.
Proactively identify and manage risk
Agentic AI can identify supply chain trends and anomalies — such as sudden drops in demand — better than humans, which is crucial for detecting risk, responding to disruptions, enhancing resilience, and protecting the bottom line.
For example, an AI agent can analyze a multitier supply chain and optimize it for competing objectives (such as cost, carbon, and service), mitigating risks and maintaining smooth operations. According to Accenture, AI agents can also make these insights more accessible to more members of the supply network, making it easier to collaborate with suppliers to understand priority risk areas and make more effective sourcing decisions.
Lower barriers to supply chain modeling
Agentic AI makes complex supply chain modeling techniques simpler and more accessible to more people. It reduces the time needed to train users and enhances productivity as it guides people through scenario comparisons, provides instant answers to queries, and enhances collaboration. This ultimately creates more transparency that helps businesses make the best trade-off decisions given their particular constraints.
What are some innovative uses of AI in supply chains?
AI is continually evolving. Two areas with several innovative use cases involve knowledge management and supply chain modeling. A knowledge management AI agent helps people make the most of their supply chain design software so they can navigate the tools more effectively and get more accurate results. A supply chain modeling AI agent helps supply chain modelers make faster, more informed decisions in supply chain planning, supply chain forecasting, supply chain network design, inventory management, and more.
Example: Manufacturing site selection
AI can play a crucial role in explaining how certain results are derived, especially in complex supply chain scenarios where multiple data sources, policies, and constraints shape outcomes.
For example, someone might wonder why a product isn’t being manufactured at a particular plant, even though it has the lowest production cost. With AI, the user can simply ask this question, and the agent will analyze all relevant datasets — including master data, policy files, transactional records, and constraint settings — to uncover the reasoning behind the decision. In this case, the AI can determine that manufacturing the product at the low-cost plant would have violated the total network risk constraint. As a result, production was strategically shifted to a different site that, while more expensive, poses less risk to the overall network.
Example: Accelerating progress toward sustainability goals
Imagine a European-based global food and beverage company with a bold strategic vision to reduce its carbon footprint — operating a mixed fleet of internal combustion, hydrogen, and electric vehicles to deliver products to customers. Today, delivery efficiency and sustainability can be optimized, but they require significant human effort to set up scenarios, run analyses, and interpret results.
Now, imagine the new world with an AI agent: It could automate this heavy lift by running and scanning through countless scenarios to recommend the best options, evaluating carbon impact and operational trade-offs with remarkable speed and precision. Instead of spending hours running analyses, users could focus on strategic decisions, guided by AI insights. This shift could unlock a new level of productivity while accelerating progress toward sustainability goals.
Example: Navigating tariffs with speed and confidence
Consider a global high-tech company that relies heavily on suppliers across Asia. They have to navigate an increasingly complex web of tariffs and shifting geopolitical landscapes. Today, analyzing the impact of tariffs is a time-consuming and manual process that limits the number of scenarios teams can realistically evaluate.
Now imagine what’s possible with an AI agent: It could rapidly assess the tariff implications of countless supplier combinations, instantly identifying cost-competitive alternatives. Beyond just crunching numbers, the AI could also explain its recommendations; highlighting not just post-tariff costs, but also risk profiles, supplier performance scores, and other strategic factors. This kind of intelligent analysis would enable organizations to act with greater speed and confidence in a world where agility is no longer optional — it’s essential.
Example: Empowering teams with supply chain design software
AI can also help people discover advanced capabilities within the supply chain software that they may not be aware of or haven’t had a chance to explore. The AI goes beyond bringing these features and functions to the surface; it also guides users step-by-step through using capabilities effectively. This reduces the training burden on other team members and ensures the user’s experience with the software never becomes a barrier to achieving the best possible results.
What are some misconceptions about AI in supply chains?
It can be difficult to sort out the facts about AI from the hype. So we’re breaking it down here.
Fact or fiction: GenAI is suited as-is for supply chain optimization
Fiction. While GenAI excels at interpreting user requests, automating workflows, and explaining outcomes, it’s not inherently designed for the precise calculations, forward planning, and complex reasoning required for supply chain optimization. These tasks are still best handled by advanced optimization techniques, knowledge graphs, and composite AI approaches that bring structure and analytical rigor to decision-making.
Coupa has introduced a unique approach to addressing user needs by combining the strengths of multiple AI techniques, unlocking significant gains in productivity and efficiency.
Fact or fiction: AI hallucinates and can’t be trusted for supply chain management
Fiction. While it’s true that some AI models may generate inaccurate or misleading responses — known as hallucinations — this risk can be significantly reduced with the right design. In supply chain management, AI systems built with guardrails such as retrieval-augmented generation (RAG), domain-specific data, and human-in-the-loop oversight can deliver accurate, reliable, and actionable insights. Trustworthy AI in supply chain isn’t about using generic models — it’s about tailoring them to the domain and validating their outputs.
Coupa has developed a patent-pending technical infrastructure specifically designed to significantly reduce hallucinations in supply chain applications.
Fact or fiction: AI is a nightmare for IT teams
Fiction. AI doesn’t have to be a nightmare for IT teams; it can be a strategic advantage. When thoughtfully integrated, AI solutions can enhance security, reduce manual workload, and improve system efficiency. Plus, when AI tools are built to align with existing infrastructure and compliance standards, they become a partner to IT, not a problem.
Coupa’s AI is designed with governance, access controls, and scalability in mind.
What does the future of AI in supply chains look like?
AI technology is evolving at what can feel like light speed. But this is actually a good thing! It means that the technology underpinning these advancements will only increase the possibilities for how AI can continue to improve supply chains.
The supply chain AI of the future could one day bring near-full automation to routine tasks and issues, freeing up supply chain leaders to spend their time, energy, and brain-power where it will matter most.
Specifically, there are a few areas where we believe AI — particularly agentic AI — is headed:
Guided workflows for supply chain modeling
AI agents will transform how supply chain modelers interact with data by providing an intuitive, natural language interface where modelers can ask complex, layered questions and receive instant, actionable insights. Instead of navigating complex optimization models or manually running scenarios, supply chain modelers can simply ask, “How will a 10% tariff impact my total cost-to-serve?” Then they’ll receive a data-backed response in seconds and be guided on what steps to take next.
Summarizing and comparing results
It’s one thing to run scenarios and another to summarize and compare the results in a meaningful, easy-to-understand way. AI agents will be able to help supply chain leaders and business stakeholders compare results across KPIs like cost, service levels, sustainability, risk, etc., and quickly assess trade-offs. The AI agent can also help explain why results change when different inputs, constraints, and model assumptions change. This will help teams identify root causes of various issues and ensure a deeper understanding of supply chain behavior and outcomes.
Auto-generating models
Imagine that when you need to generate a new supply chain model, you don’t have to spend hours or weeks finding the data you need to make a reliable model. For example, let’s say you need to forecast automobile sales. An auto-generated model will identify what data you need to make those predictions and at what level and then help you build that model. Then, once the design is complete and confirmed, it would go automatically into your planning system and integrate with other workflows.
Automated optimization solves
Like GPS for driving, AI will be the key supply chain optimization tool you won’t be able to remember how you ever survived without. Imagine being able to share your supply chain model as it is, click a button that says, “Optimize,” and then have optimization recommendations within a few minutes. More granular asks with larger datasets might take longer. But if you had a presentation in two hours and needed some preliminary results, you could prompt the AI tool with that information and it would come up with recommendations within those parameters.
How can you implement AI in your supply chains?
AI can help you optimize your entire supply chain network as it factors in supplier risks, tariffs, inventory, and transportation decisions. Implementing agentic AI like Coupa’s Navi™️ for Supply Chain Design and Planning can guide your team in building supply chain models and running scenarios to help you mitigate disruptions and maximize profits.
Implementing AI solutions in your supply chain can be complex, but when you pick the right solution, it’s worth it. When you’re evaluating AI solutions, it’s important to look beyond basic GenAI capabilities and prioritize vendors that offer ML and deep learning capabilities along with mathematical optimization techniques to ensure the AI recommendations are accurate, and that they have the necessary analytical depth for meaningful decision-making support.
As with any technology, your IT team will be essential in helping choose and implement the right AI solution. They can help you think through compatibility issues, data silos, and ensure system stability and user adoption. Compliance teams will also need to ensure AI solutions meet security and governance standards.
To effectively implement AI for your supply chain management needs, it’s vital to conduct a comprehensive assessment of vendors and prioritize solutions that unify fragmented systems.
Coupa’s Navi™️ AI Agent for Supply Chain Design and Planning takes a unique approach that goes well beyond basic Gen AI. AI is integrated across Coupa’s entire product portfolio, combining Gen AI with proprietary mathematical reasoning and optimization to address supply chain use cases at scale. Powerful, purpose-built agents guide users through model-building and decision-making to recommend scenarios, alert users when outcomes exceed critical thresholds, and create a snowball effect of improvements and optimization across the connected supply chain for maximum impact.
Adopting and implementing Coupa Navi™️ for supply chain design now will help you unlock efficiency gains, mitigate risks, and capitalize on new opportunities so your company can adapt and thrive in the face of supply chain uncertainty.