Generative AI in Supply Chain: 7 Enterprise Use Cases for Procurement, Suppliers, and IT Support

Most enterprise teams already run dozens of supply chain systems, yet simple procurement or support requests can still stall.

For example, staff might wait days for a purchase approval, a supplier record, or an access grant to move forward. Your systems can support the work, though information and actions often remain divided across them. Answers end up stranded in one place while teams spend hours searching across portals, inboxes, and knowledge bases to reach them.

Generative AI can change how that work moves in supply chain settings by serving as a connective layer across the systems your teams already use.

Below, we'll go through seven use cases where generative AI can add real value across procurement, supplier management, and IT support.

What is generative AI in the supply chain?

Generative AI is technology that creates content and synthesizes data across supply chain workflows, layered over existing enterprise systems.

Enterprises commonly rely on GenAI technology to carry out tasks like:

• Drafting responses, documents, and summaries
• Pulling relevant details together from different sources
• Answering questions in plain language across connected systems

What sets generative AI apart in supply chain operations is its ability to cover a wide range of functions. One request can reach across procurement, logistics, planning, supplier management, and support without anyone needing to jump between teams.

When generative AI connects procurement, logistics, planning, finance, and customer service, it can help reduce the silos that slow work down. That reach explains why it now sits at the center of many digital transformation efforts. It supports data-driven decisions and more coordinated work across the end-to-end supply chain.

That connective role matters because supply chain data rarely lives in one place. It spreads across the core systems enterprises run every day, including:

• Enterprise resource planning (ERP)
• Warehouse management (WMS) and transportation management (TMS)
• Human resources information systems (HRIS) and IT service management (ITSM)

7 Generative AI use cases in the supply chain

Enterprises are adopting AI quickly across multiple industries. In its 2025 survey, McKinsey found that 88% of organizations now use AI regularly in at least one business function.

While AI adoption is on the rise, the value organizations gain from it depends on where and how it's implemented. Below, we'll go over seven high-value use cases of generative AI, pairing each workflow to specific outcomes for enterprises.

1. Procurement policy retrieval

When a procurement question comes up, employees may need to search across policies, reports, dashboards, and other sources for the answer. As requirements change, teams may also spend time rebuilding dashboards and preparing recurring reports by:

• Downloading recurring reports
• Merging separate data tables
• Checking information for consistency

Generative AI provides a faster path for this work. Teams can ask an AI assistant a procurement or policy question in plain language, and it can return a cited answer tailored to their role and sourced from relevant data.

Researchers at MIT built a generative AI assistant for a global pharmaceutical company. The tool consolidated more than 200 dashboards into a single conversational AI that could field procurement questions in real time. Example use cases like this are shifting traditional procurement management toward self-service analytics and faster frontline decisions.

2. Purchase order and invoice automation

Every purchase order and invoice can go through a range of manual processes to complete. While these steps are necessary, every touchpoint creates another opportunity for an error.

Generative AI paired with workflow automation can handle much of that routine work on both sides of the transaction.

On the buying side, an AI-enabled workflow can draft a purchase order directly from a supplier catalog. It can fill in item details, quantities, and delivery terms, then send the information to the appropriate approver.

On the paying side, the workflow can read an incoming invoice, extract the line items and match them to the original order.

Reducing manual entry can help decrease errors and move payments through the process faster. A shorter invoice-to-pay cycle can also ease pressure on working capital, since approved bills clear on schedule instead of stalling in a queue.

3. Supplier onboarding automation

Bringing on a new supplier usually involves moving a record through several stages, often across different teams and tools. Agentic AI can combine generative AI capabilities with automation to orchestrate much of that sequence, carrying a supplier from first contact to active status.

For example, a generative AI onboarding agent can:

• Send and collect the pre-qualification paperwork
• Read submitted documents and check them against what the record requires
• Cross-check references and flag anything that looks off for review

The same approach can summarize incoming bids and pull key clauses from supplier contracts for faster auditing. Handled this way, onboarding becomes a governed workflow that can strengthen the supplier relationship from the first interaction.

4. IT self-service for logistics teams

Generative AI paired with agentic automation can provide logistics and operations teams with a self-service channel for routine IT and access requests. Instead of filing a ticket and waiting in a help desk queue, a person can ask for help in plain language, on the same platforms where they already work.

This type of automation can handle the everyday requests that make up much of IT's volume, like access grants and password resets. A short exchange can resolve the request and let the employee get back to work sooner.

This can reduce the number of tickets reaching the help desk and the time employees spend waiting for basic support.

5. Demand planning and scenario analysis

Planning teams often need to commit to inventory and production levels before actual demand becomes clear. Generative AI can help them test more possible outcomes before they commit.

GenAI technology works well alongside a digital twin used as a virtual model of your supply chain. With this in place, tools can run multiple what-if scenarios against different conditions.

For example, a team could monitor how a port closure or a sudden demand spike affects inventory management and production planning, then adjust while there's still time.

Testing more scenarios can give planners additional context for refining demand forecasting over time. It gives planners room to optimize inventory and protect fulfillment when market trends move faster than a fixed forecast can follow.

6. Supplier risk and resilience monitoring

Early indicators of supply risk may appear gradually across delivery, regional, and financial data. Generative AI can help surface those early signals by reading across the data you already have, giving a team room to act while options are still open.

Generative AI can help to draw attention to things like:

• A supplier's delivery dates slipping over time
• Early warning signs building in a particular region
• Shifts in a supplier's financial performance

Research on generative AI and supply chain resilience suggests that benefits can narrow when a company relies on a small set of customers or suppliers.

With fewer suppliers, there are fewer options to switch to when something goes wrong. A broader supplier network can give teams more options when conditions change, supporting a more resilient risk-management strategy. 

7. Knowledge and document summarization

Supplier contracts, email threads, and policy documents are typically the data sources employees rely on the most in the supply chain. But finding the right information usually means opening file after file before anyone gets an answer.

Generative AI can automatically read data across those documents and return a short, cited summary.

For example, teams can ask what a contract says about renewal terms, and the tool can automatically pull the relevant clause and point teams to the source. The same works for a long policy or a supplier's recent messages, condensed into a decision-ready answer.

What generative AI in the supply chain can't do

A recent McKinsey report shows that many digital investments have struggled to deliver the value companies hoped for. The reality is that generative AI can carry the same risk if the right groundwork isn't set.

GenAI technology is only as valuable as the data it uses. When records are incomplete or scattered across partners, the answers provided can have gaps. As a result, teams may need to address data quality and integration issues before using generative AI for high-impact workflows.

While taking these additional steps is helpful, it's important to keep in mind that generative AI isn't always the right tool for the job. For some well-defined, predictable tasks, traditional machine learning may be a more efficient and reliable approach.

Choosing the appropriate technology is also critical for effective governance. In regulated and global operations, organizations often prioritize permissions and auditability to ensure their generative AI workflows operate within the access rules they already trust.

How to scale generative AI from pilot to production

Moving a working use case from a small deployment to full production is where most teams will spend their time.

As you evaluate existing use cases for broader deployment, the following steps can improve their chances of delivering sustained value:

• Start where your volume is: Scale your high-volume, well-scoped use cases first, where your outcomes are measurable, and your data is already available.
• Fix your data foundation before you scale it: Sort out your data quality, permissions, and system integration now, while it's easier than after a use case is running at scale.
• Define what success looks like for your enterprise: Agree on metrics like cycle time, deflection, resolution time, and hours saved to determine whether scaling is working.
• Keep your teams involved: Build human review into your higher-risk decisions so someone approves critical actions before execution.

Making supply chain work flow with agentic AI

Across the use cases discussed, generative AI plays a similar role: it understands a request in plain language, retrieves the relevant information, and drafts a response.

These tools are widely used, but they're limited in scope. They can provide relevant answers, summaries, and drafts, but employees may still need to complete the next steps. Agentic AI isn't restricted to this same scope. These tools can reason across your systems, plan actionable steps, and execute them, with human oversight built in, turning a simple answer into a resolved request from end to end.

AI provides the most value to teams when it moves work forward. An answer just informs a decision. A completed request can reduce the time teams spend routing, tracking, and following up, creating greater operating leverage.

Moveworks is an agentic AI platform built on that idea. Its AI Assistant is designed to combine search and action in a single interface, and teams can resolve procurement and IT requests across enterprise systems like Workday, ServiceNow, and Okta. The platform operates within existing permissions and policies, with enterprise-grade governance supporting actions across connected systems.

Create more impactful automation across all your supply chain operations with Moveworks for manufacturing.