Generative AI's demand forecasting and inventory optimization abilities, among others, can help companies meet their goals. Learn how the technology can align with these efforts.
Many company leaders are looking to integrate AI with their business processes to gain a competitive advantage in their industry, and generative AI can potentially help optimize key supply chain processes.
Generative AI (GenAI) is a technology that is gaining popularity in various business areas, including marketing and sales. By analyzing an organization's data, GenAI can potentially improve operational efficiency and supply chain resiliency.
Many large enterprises are beginning to view GenAI less as a collection of isolated use cases and more as a broader initiative to transform their supply chain. As GenAI moves from pilot projects into production environments, it increasingly raises executive-level questions around governance, operational risk, resiliency and scale. For C-suite leaders, the focus is shifting from what GenAI can do to how reliably and responsibly it can be embedded into core supply chain operations.
However, users should be aware of concerns surrounding what's known as AI hallucinations, which could hamper GenAI's ability to improve supply chain operations.
Learn more about generative AI and its specific use cases in the supply chain.
What is generative AI in the supply chain?
On the consumer level, the GenAI process consists of inputting a command or question into a text, image or video field, which prompts the AI to generate new content. GenAI models are typically trained on large-scale data sets, and when a user inputs fresh data, the application uses the new data and its previously learned knowledge to create new content.
Users can train GenAI on data that covers every aspect of the supply chain, including inventory, logistics and demand. By analyzing the organization's information, GenAI can help improve supply chain management and resiliency.
In enterprise environments, the technology is increasingly being integrated into systems of record such as ERP, supply chain planning and supplier management platforms. As a result, GenAI outputs can influence real operational decisions, including procurement strategies, production planning and logistics execution. This tighter integration heightens the importance of data quality, oversight and trust in AI-generated insights.
9 use cases for GenAI in the supply chain
While these use cases illustrate the range of potential generative AI applications, their practical value varies based on factors such as organizational scale, data maturity and governance readiness. In large enterprises, GenAI initiatives tend to deliver the most impact when they support priorities like resilience, risk reduction, cost management and protecting revenue, rather than isolated efficiency improvements.
Before moving forward with GenAI applications in the supply chain, supply chain leaders should consider which GenAI capabilities align with company objectives and assess applicable benefits and limitations.
Here are some use cases that hold potential promise for supply chain management.
1. Demand forecasting
The rapid shift in demand at the beginning of the COVID-19 pandemic, as consumers moved their spending away from services and toward goods, led to unpredictable supply chain operations. The shift demonstrated how important it is for companies to anticipate changes in demand.
Organizations can use GenAI models on historical sales data, market trends and other factors to simulate potential supply-and-demand scenarios and improve their demand forecasting accuracy. Tracking demand patterns can help organizations mitigate disruption and avoid stocking issues.
Improved demand forecasting can be especially valuable in large supply chain environments, where sudden market shifts can quickly create financial risk and operational disruption.
GenAI that has been trained on key data such as stock levels, warehouse capacity and manufacturing time can suggest ways to optimize inventory processes, including recommending when to restock or reduce stock, which can help cut down on excess storage. Storing extra product costs companies more money, so reducing excess stock could cut down on costs.
Inventory optimization in large supply chain environments often centers on balancing product availability against carrying costs and capital efficiency, which makes this use case especially relevant as operations scale.
3. Supplier and customer communication
Frequent communication between a company and its suppliers, as well as between a company and its customers, is key to an efficient supply chain. However, making this communication as effective as possible can be challenging.
GenAI can automatically send out messages, so employees don't have to. Large language models and natural language processing can consume data from sources like market events that might affect suppliers and traffic delays involving specific shipments, then GenAI chatbots can notify suppliers about risk. GenAI chatbots can also handle some customer queries, like processing a return or tracking a delivery.
During supply chain disruptions, automated communication tools can help reduce operational risk by improving coordination with suppliers and customers.
4. Operations
New tech and fluctuating demand can lead to operational challenges, and GenAI can potentially suggest how to improve.
GenAI can assess aspects of operations, such as supplier performance and manufacturing speed, and then suggest ways to optimize procedures. These optimizations can also potentially save companies money, among other benefits.
Common business benefits associated with generative AI adoption in organizations range from productivity and cost reduction to improved decision-making, customer experience and revenue growth.
For large organizations, operational optimization driven by GenAI can contribute to both cost control and more consistent service levels across complex supply chain networks.
5. Logistics
Logistical disruptions can cause various issues. Traffic accidents can delay a shipment or extreme weather events can cause unexpected shortages, making it difficult to maintain on-time shipping schedules.
GenAI models with data such as historical weather patterns, traffic maps and fuel prices can identify routes for optimal travel and highlight potential upcoming disruptions as well as alternate routes if needed. Doing so can help shipping stay on schedule and improve customer service, since orders won't be delayed.
Improved logistics planning can help protect revenue in complex supply chain networks by supporting on-time delivery and limiting disruption-related delays.
6. Sustainability and scalability
Sustainability is currently a major focus for many organizations, and GenAI can potentially highlight areas for improvement.
Training GenAI models on a company's current material use as well as market projections for renewable materials can give insight into how to make processes more sustainable while also considering cost-effectiveness and long-term scalability.
This use case aligns with executive efforts to balance sustainability goals with long-term scalability and financial performance.
7. Analytics
GenAI can run simulations and potential what-if scenarios, assess risk and compile findings into a report.
As with all other GenAI supply chain use cases, caution is required when using the tech, as GenAI and the models that fuel it are still evolving. Current concerns include incorrect data and imperfect outputs, also known as AI hallucinations, which can prevent effective use.
For C-suite leaders, advanced analytics can support better risk assessment and decision-making, provided the underlying data and models are properly governed.
GenAI can run simulations and potential what-if scenarios, assess risk and compile findings into a report.
8. Supplier sourcing
Diversifying a supplier network can help make a company’s supply chain more resilient. However, finding suppliers and evaluating them via traditional methods can prove time-consuming.
GenAI can suggest new suppliers for companies. For example, GenAI can run automatic searches for suppliers based on market data sets. GenAI can also sort through supplier bids and analyze them based on a variety of factors, such as efficiency, risks, procurement costs and capabilities, then recommend which suppliers are a good fit for the organization.
Diversifying supplier sourcing through GenAI can help enterprises reduce dependency risk and improve supply chain resilience.
9. Predictive maintenance
A supply chain is sensitive to any type of disruption, including equipment failures. Companies can pursue predictive maintenance by continuously monitoring equipment and acting on that data. However, collecting and interpreting all the data can be difficult for teams that lack resources or data science expertise.
GenAI can potentially alleviate some of these problems. For example, GenAI can be trained on historical equipment data and fed real-time sensor data. GenAI then interprets the data and uses machine learning to predict the potential timing of equipment failure.
Once GenAI has flagged an issue, it can alert staff and suggest potential actions to take, such as a part replacement or earlier-than-scheduled maintenance.
Predictive maintenance becomes more valuable as operations scale, helping organizations reduce unplanned downtime and extend the life of critical assets.
Governance and scaling considerations for enterprise GenAI
As organizations embed generative AI more deeply into supply chain operations, governance and oversight questions tend to become more prominent. Issues such as data quality, model accuracy and the risk of AI hallucinations can have significant operational and financial implications when GenAI outputs are used at scale.
Enterprise organizations must also consider how GenAI systems are monitored and audited, and how those systems align with existing risk management and compliance efforts. Human oversight remains essential, particularly for decisions that affect supplier relationships, inventory levels and production schedules.
Addressing these governance challenges early can help organizations scale GenAI more responsibly while maintaining trust in AI-assisted supply chain decisions.
Looking ahead, generative AI is expected to play a more strategic role in enterprise supply chains as organizations move beyond experimentation and begin scaling AI-enabled capabilities. This evolution is likely to include more advanced decision-support tools and, over time, more autonomous and agentic workflows that operate within established planning and execution systems.
For enterprise leaders planning for 2026 and beyond, the challenge is often less about identifying new GenAI use cases and more about deciding where the technology can be applied safely, reliably and at scale. Organizations that pair GenAI adoption with strong data foundations, governance and human oversight will be better positioned to realize long-term value while managing operational and financial risk.
Editor's note: This article was updated in January 2026 to improve the reader experience.
Jacob Roundy is a freelance writer and editor, specializing in a variety of technology topics, including data centers and sustainability.
