AI in the Supply Chain: Use Cases and ROI for IT and Operations Leaders

A warehouse team needs access to a system to keep work moving. The request goes to IT, but resolving it means checking multiple systems, finding the right owner, and sorting through a queue of other issues. By the time access is restored, the delay has already affected the people and processes downstream.

These moments add up. 

Your supply chain may run on sophisticated technology, but getting work done can still depend on manual follow-ups, disconnected info, and people stepping in to bridge the gaps between systems. 

AI is changing how those everyday issues get handled. When it can understand what someone needs, pull the right info together, and help move work forward, teams spend more time keeping operations on track. 

Below, we’ll look at practical AI use cases across the supply chain, the ROI that operations and IT leaders can expect, and how to evaluate where it can have the greatest impact.

What AI in the supply chain means for operations teams 

At its simplest, AI in the supply chain can use data from across your systems to sense what’s happening, predict what could happen next, recommend a response, and take action. That can happen anywhere from planning and sourcing to logistics, risk management, and day-to-day execution.

The technology can take different forms:

• Machine learning: Spots patterns to help improve predictions 
• Generative AI: Helps people find and make sense of information
• AI agents: Help carry work across systems 

But the teams running supply chain operations still have to get answers, resolve issues, and navigate the systems that keep work moving. Artificial intelligence can also support the people behind those processes. 

Where AI adds value across supply chain operations 

When deciding where AI can make the biggest difference, look beyond the movement of products from supplier to customer. There’s another side of the operation that can have just as much impact: the people coordinating that flow. 

In the sections below, we’ll explore both sides of that equation — improving how supply chain teams plan and move goods, and making it easier for employees to get the information and support they need for daily operations. 

Planning, sourcing, and logistics 

A demand spike, supplier delay, or missed delivery can quickly throw other decisions off course. AI can help supply chain teams spot those changes sooner and respond with more context.

Common applications include:

• Demand sensing to catch shifts in buying patterns as they emerge 
• Inventory optimization to identify where stock is building up or running short 
• Supplier risk modeling to surface warning signs before they disrupt supply 
• Dynamic routing and exception handling to adjust plans when deliveries hit unexpected problems 

McKinsey research on distribution operations found that AI could reduce inventory by roughly 20–30% and logistics costs by 5–20% (though results vary by use case).  

Supporting the operations workforce 

A planner waiting on IT or a logistics worker chasing down access can lose hours to a problem that has little to do with their actual job. Fragmented tools and slow internal support create a productivity cost that’s easy to overlook. 

That lost time adds up for IT employees, who lose about 3 hours and 18 minutes per incident. 

Agentic AI can help reduce those lost hours by handling requests across the systems employees rely on, such as:

• ITSM platform (ServiceNow): Diagnose an issue, find the right fix, and resolve the request 
• HRIS tools (Workday): Find employee info, answer questions, and complete updates 
• Collaboration tools (Slack/Teams): Bring actions and answers into the tools staff use already
• Across systems: Coordinate the necessary steps and approvals to finish a request 

Let’s say an employee loses access to a transportation management system and asks for help in Teams. An AI service desk agent can verify the employee’s permissions, troubleshoot the issue, then restore and confirm that access is back. The employee can stay focused on their work while that happens instead of handling each step manually. 

What the ROI really looks like 

The ROI story is more nuanced than “AI saves time, so the business saves money.” While AI has helped improve individual productivity for many employees, that may not always translate directly to a positive effect on revenue.

That gap is where execution matters. A pilot that helps one person work faster can be useful, but the impact is easier to scale when AI is built into the operational workflow teams use every day and measured against the outcomes those workflows affect.

For supply chain operations, that means tracking KPIs like:

• Resolution time for employee and operational requests 
• Ticket and case deflection as more routine work gets handled automatically
• Inventory turns as planning and inventory decisions improve 
• Hours returned to operations teams when people spend less time on manual tasks and system hopping 

The goal is to connect AI activity to changes that the operation can actually see. 

The barriers slowing enterprise adoption

An AI use case can work well in one system and stall when the task depends on info that’s somewhere else. A supplier update might sit in one platform, inventory data in another, and the action needed to respond could require approval from another team.

That kind of fragmentation is usually what slows AI down as it moves beyond a pilot. 

In the sections below, we’ll look at two barriers that become harder to ignore at scale: integration and data quality, and governance and explainability. As those workflows expand, the people around them need to adapt as well. Employees need to know where AI fits into their work, when human judgment is needed, and who owns the outcome when AI takes action. 

Skills are part of it too. Teams need enough fluency with AI tools to judge when an agent can handle the next step and when a person should step in.

Integration and data quality 

A supplier delay lands in procurement. The inventory team has the latest stock levels, while open orders are sitting in another system. An AI agent might be able to find all three, but unless those systems connect, someone still has to piece together what the delay means and what to do about it. 

For supply chain teams, those connections determine how far an agent can take a task. With reliable access to data across the systems involved, an agent could connect a supplier issue to affected inventory and orders, then help move the response forward. 

Governance, security, and explainability 

An agent can flag a supplier risk, recommend moving an order to another vendor, and even act on that recommendation. But the team needs to know what data it used, what exactly it changed (and has access to change), and why it made the recommendation. 

Those boundaries become more important as AI moves from surfacing info to taking action. Yet only 21% of surveyed companies have a mature model for governing autonomous AI agents. 

A few practical safeguards can help supply chain teams manage that shift:

• Permissioned access: Limit what an agent can see and change based on role and task.
• Audit trails: Record the agent’s actions, so teams can trace what happened when a decision needs review. 
• Explainable recommendations: Show the info behind a recommendation, giving employees context to evaluate higher-risk decisions before an agent acts.

How to evaluate AI in the supply chain

A supply chain tool can give a useful answer and still leave someone to handle the work that follows. When evaluating options, look at whether the technology can move from information to action without creating more handoffs: 

• Data readiness: Can it work with the current data behind day-to-day decisions?
• Secure, permissioned integrations: Can it access the right systems and respect existing permissions?
• Explainability: Can employees see the context behind recommendations?
• Multichannel delivery: Can employees use it where they already work?
• Measurable time to value: Can you track faster resolution, fewer manual steps, or time saved?

The difference becomes clear when you look at how much work the AI can do. A scripted chatbot handles predefined requests (like an order status update), while more advanced conversational AI can draw on live enterprise data and complete multistep actions across systems (e.g., resolving a supplier issue and updating teams on actions taken). 

When deciding where to begin, look for work that forces people to make frequent decisions while moving between systems or handling repetitive requests. Think adjusting inventory after a demand shift or checking a supplier issue against open orders. 

Start there, measure what changes, then use those results to guide the next workflow you bring into the fold.

Making AI in the supply chain work for your operations teams

Supply chain performance depends on two things working together: moving goods efficiently and giving the people behind those decisions the support and information they need to do so. AI can help with both when it connects search, action, and support across the systems operations teams are using already. 

Moveworks is designed to bring those capabilities together through an AI Assistant with an agentic Reasoning Engine, so employees can find info and complete multistep requests across enterprise systems.

If you’re ready for fewer handoffs for routine issues and more capacity for the planning and decisions that keep operations moving, see what this approach can look like for your supply chain teams. 

Explore Moveworks AI help desk today.