What AI Agents for Manufacturing Can Actually Do to Connect ERP, Procurement, and IT Into Coordinated Action

You already have the data. Your ERP tracks orders and inventory, your MES captures production activity, and your IT systems manage the technology your workforce depends on. 

But when a request crosses those systems, you can still end up chasing information, checking status, and figuring out the next step manually. 

That coordination gets harder as data spreads across systems, teams, and workflows. Today, 70% of manufacturers still enter at least some data manually, while 53% say data coming from different systems or formats is a top challenge. 

Agentic AI can offer a way to connect those systems so that they work better together. Rather than just surfacing info, AI agents can reason across context and follow your policies before taking action across enterprise systems. 

Below, we’ll look at what AI agents for manufacturing are, where they can make an impact, and how they connect with your existing architecture and systems. We’ll also explore what governance looks like and how to choose the right first use case.

What are AI agents for manufacturing?

AI agents are software that can observe data across systems, reason in production context, and coordinate a governed action. In practical terms, they can take a goal, gather the info they need, and determine the next step across your connected systems. 

Say a parts shortage puts an upcoming production run at risk. An agent could check your ERP for inventory and open orders, review the production schedule, and prepare a procurement request without making you piece the information together yourself. 

You decide how much autonomy to give it. Routine tasks can run automatically, while high-impact decisions stay with the people responsible for the outcome.

Why manufacturers are turning to AI agents now 

U.S. manufacturers are facing a workforce problem that technology alone can’t solve — a shrinking labor pool that threatens to leave half of the 3.8 million open manufacturing jobs unfilled.  

That puts more pressure on the people already keeping production moving. 

AI agents can take on repetitive coordination and information-gathering work, giving your teams more time to focus on decisions that need careful judgment and experience. 

Manufacturers are already exploring what that looks like in practice. After 78% of manufacturing executives saw returns on their generative AI investments, 56% now report actively using AI agents as well. 

The goal isn’t to replace your workforce. It’s to extend what your teams can accomplish by putting the right info and actions within reach when they need them.  

Where AI agents create value across the connected enterprise 

A supplier delay can quickly become a production planning problem. Teams may need to check supplier updates, inventory levels, production schedules, and purchase orders in different systems before deciding how to respond. 

AI agents can handle much of that coordination in manufacturing environments. With governed access to ERP and other systems, an agent can gather relevant context, determine which actions are permitted, and execute or route the next step across suppliers, plants, logistics, and planning. 

That same pattern applies to the four areas below. You can use agents to coordinate work that already spans the systems and teams running your operation. 

On the shop floor: predictive maintenance and quality 

A predictive maintenance alert tells you something may be wrong. An AI agent can connect the alert with the systems that contain relevant context to help figure out what to do about it. 

If a machine shows signs of potential failure, the agent can:

• Pull its maintenance and repair history
• Check parts availability and upcoming production
• Prepare a work order within your approval rules 

Quality workflows can work the same way. When an inspection flags an issue, an agent can connect the result to the affected batch, machine, and previous cases. It can then prepare a contained response and route it to the appropriate person for review. 

In the supply chain: procurement and inventory 

When a supply issue puts production at risk, procurement needs to see more than what’s sitting in the warehouse. An AI agent can bring inventory, supplier info, open orders, and production requirements together to help determine the next move. 

Let’s say a supplier misses a delivery date. An agent can:

• Check current inventory against upcoming production needs
• Review supplier and open-order data for alternatives 
• Trigger an approved procurement workflow 

That means the procurement response accounts for what the plant actually needs. What your ERP says is available is only part of that picture. 

The value of that coordination can add up quickly. Estimates show agentic AI can accelerate some enterprise processes by 30–50%. 

Across ERP and production planning 

Production plans have to keep up when orders change, capacity shifts, or materials aren’t available. An AI agent can connect those variables in your ERP and planning systems, helping your team spot conflicts and decide what needs to change. 

When a rush order comes in while a production line is already near capacity, an AI agent might:

• Check the order against available capacity and materials 
• Identify conflicts with existing production commitments 
• Recommend or prepare an updated schedule within approved rules 

This is where production planning is heading. More than 40% of manufacturers with a production scheduling system are projected to upgrade it with AI-driven capabilities in 2026, supporting more autonomous processes. 

In IT and HR support for the workforce 

For a distributed manufacturing workforce, getting a simple question answered or access request completed can mean navigating multiple systems and support teams. 

AI agents can handle many common requests across IT and HR, giving employees faster resolution and your HR and IT teams more capacity for work that needs their expertise.  

For example, when a new employee starts at a plant, an AI agent can:

• Check their role and location in your HR system
• Request the approved equipment and application access
• Confirm when everything is ready 

The need to streamline that work is growing. About 409,000 U.S. manufacturing positions were unfilled as of August 2025, adding pressure to already stretched teams.

How AI agents connect your existing systems 

Adding an AI agent for manufacturing doesn’t mean ripping out the systems you already use. Your MES, ERP, ITSM, and identity platforms still do their jobs. The agent connects them, so a request can move across systems without someone coordinating every step manually. 

Permissioned integrations and APIs give the agent access to the info and actions it needs, while your existing permissions determine what it can see and do. 

Example: A technician requests access to a production application. The AI agent can verify their role and existing access, check the request against your policies, and submit the approved change through the ITSM.

That connected model is gaining traction as agentic AI moves toward broader enterprise orchestration, with 45% of G2000 companies expected to adopt agentic AI-driven channel and partner orchestration by 2029.

The architecture behind agentic manufacturing 

For an AI agent to work reliably, three pieces need to work together: data and context, reasoning and orchestration, and governance. Each plays a different role in helping an agent understand what’s happening, decide what to do, and act within defined boundaries. 

The flow is “sensing → planning → execution.”

Your systems provide the signals and context. The agent uses them to determine the next step, while governance sets the limits on what it can do. 

And not every signal needs to pass through a language model. Purpose-built models can continue handling high-frequency equipment data, machine vision, and other specialized workloads, with agents using those outputs when they need broader context. 

The data and context layer 

Before an agent can decide what to do, it needs a clear view of what’s happening. That can come from several parts of your manufacturing stack:

• Sensors and historians: Equipment and process signals 
• MES and ERP: Production, orders, materials, and inventory
• CMMS and quality systems: Maintenance, inspections, and past issues 
• Asset hierarchy and production context: The relationships between equipment, lines, and processes 

Getting that context right matters. An autonomous agent might miss important details if asset records don’t match, maintenance histories are incomplete, or data is spread through disconnected sources. 

Reliable data is a prerequisite for useful agentic AI.  

The reasoning and orchestration layer 

Once an agent has the context it needs, it has to work out what to do with it. That means:

• Interpreting signals: Putting incoming data in the context of the production issue
• Choosing approved tools: Only using the systems and actions it’s allowed to access 
• Checking confidence: Knowing when there’s enough certainty to act and when to ask for help
• Coordinating the next step: Sending the work to the right system or team

Orchestration keeps a record of what the agent recommended and whether it led to action. That gives you visibility into what happened after the agent made its decision. 

Governance, permissions, and auditability 

When an AI agent can take action across your enterprise systems, you need clear rules for what it can do and when a person needs to step in, including:

• Decision rights: Define which decisions an agent can make independently 
• Role-based approvals: Require the right people to approve higher-impact actions 
• Audit trails: Record what the agent did, why it did it, and what happened next 

The need for clear guardrails is becoming more urgent as agents move into production, but only 21% of organizations report having a mature governance model for agentic AI.

For manufacturing, that makes governance especially important as agents take on more work. Clear boundaries let you expand agent autonomy while maintaining accountability and visibility.

Why manufacturing AI pilots stall, and how to scale them 

An AI workflow that works in one corner of your operation can be much harder to scale across plants, teams, and systems. Many organizations still struggle to move AI beyond individual use cases and into repeatable, enterprise-wide workflows.

That’s usually due to practical reasons like:

• Unreliable data: Incomplete or inconsistent info limits what agents can do.
• Unclear ownership: No one knows who takes over when an agent surfaces an issue. 
• Broad permissions: Giving an agent too much access creates unnecessary risk. 
• Assumed benefits: A promising pilot doesn’t automatically lead to measurable business value. 

A strategic approach is to start small and build from there:

1. Choose a frequent, measurable workflow with a clear business outcome (e.g., routing maintenance requests based on equipment alerts).
2. Start with reliable data so the agent has the right context (e.g., validated asset records and maintenance history).
3. Assign a clear owner for decisions the agent can’t make on its own (e.g., a maintenance planner reviews higher-risk recommendations).
4. Control the agent’s actions with defined permissions and approval rules (e.g., allow it to prepare a work order but require approval before submission).
5. Measure the outcome before expanding the workflow (e.g., track resolution time, manual effort, and completed actions).

Choosing your first AI agent use case 

Start with a workflow your teams already know inside and out. The strongest candidates happen often and have a measurable outcome, so you can tell whether the agent is actually improving the work. 

You’ll also want a clear owner and reliable data behind the workflow. Keep the agent’s actions well-defined too, with permissions that match what you’re comfortable letting it handle. 

A few practical places to start include:

• Maintenance troubleshooting 
• Work-order drafting 
• Quality-case triage 
• IT and HR request resolution 

Maybe that looks like an agent that can check maintenance history and relevant documentation, identify the next diagnostic step, and draft a work order for review when a technician reports an equipment issue. 

Before you launch, establish your baseline. When you know that maintenance requests take two hours to triage today, for example, you can compare the agent’s performance against real operating conditions and see whether it’s improving the workflow.

Building agentic AI into your manufacturing operations 

Your manufacturing operation already has the systems and data needed to run the business. The challenge is getting work to move across those systems when a request touches IT, HR, operations, or more than one team. 

Moveworks can help by bringing search and action together, while giving your team a way to build and govern agents for the workflows they know best.

The Moveworks agentic AI platform includes:

• AI Assistant: Give employees a single place to find info and get work done.
• Agent Studio: Build and customize agents for your manufacturing workflows, with autonomy calibrated to your governance rules.
• AI Agent Marketplace: Extend your capabilities with prebuilt AI agents. 

A leading automaker used Moveworks to resolve 70,000 employee requests autonomously in its first year, decreasing mean time to resolution by 99%, down to just 11.4 minutes. 

All it takes to start is a workflow where the value is easy to see and the boundaries are clear. From there, you can expand into more complex workflows as your teams build confidence in what AI agents can handle. 

See how Moveworks can help bring agentic AI to your manufacturing operation.