Build an AI AGENT for Manufacturing

Introduction

Manufacturing companies sit on mountains of knowledge. Standard operating procedures (SOPs), HR policies, work instructions, training manuals, maintenance logs, equipment specifications, and LEAN playbooks are everywhere, scattered in binders, SharePoint folders, email attachments, or worse, only in the heads of veteran employees.

Even with Industry 4.0 investments, frontline leaders and operators still spend far too much time searching for information or waiting on answers. In LEAN terms, this is waste, pure and simple.

But what if knowledge wasn’t buried in documents? What if every employee could ask a simple question, How do I reset this fault code? What’s our procedure for quality holds? What’s the right way to run a 5 Why?, and instantly get the right answer, based on company-specific information and LEAN best practices?

That’s the promise of AI Agents in manufacturing. Powered by large language models (LLMs), these digital assistants can become the ultimate knowledge coach: always available, fluent in your processes, and aligned with LEAN principles.

Here’s how to build one.

Step 1: Define the Purpose of the Agent

In LEAN, we start with purpose. Before building anything, ask: What problem are we solving?

Too often, companies chase AI for its own sake, treating it like the latest shiny object. But without a clear purpose, you’ll add complexity instead of eliminating waste.

For a manufacturing AI Agent, purposes might include:

• Supporting operators with quick access to SOPs and troubleshooting guides.
• Helping HR and supervisors answer policy questions during onboarding.
• Coaching frontline leaders on LEAN tools like A3 problem-solving, Andon responses, or Daily Management routines.
• Assisting engineers with quick retrieval of design standards or maintenance data.

Pick a starting point where the pain is real. If your operators spend 15 minutes every shift hunting for the right procedure, that’s wasted motion. If new employees leave after 90 days because onboarding is confusing, that’s lost opportunity.

The purpose should tie directly to your LEAN transformation goals—reducing waste, improving quality, or building people capability.

Step 2: Collect and Organize Knowledge

An AI Agent is only as good as the knowledge it has access to. The next step is creating a single, well-organized knowledge base.

This includes:

• SOPs and Work Instructions: The step-by-step standards for running machines and processes.
• Policies and Procedures: HR, safety, and compliance guidelines.
• Equipment Manuals: OEM documents, troubleshooting flowcharts, fault codes, and maintenance standards.
• LEAN Playbooks: Problem-solving guides, kaizen event templates, standard work definitions, CI methodology.
• Design Information: Drawings, CAD standards, tolerances, and approval processes.

Think of this as a 5S for knowledge:

• Sort: Remove duplicates, outdated documents, and “just-in-case” clutter.
• Set in Order: Structure information logically by function, process, or role.
• Shine: Make sure documents are clear and error-free.
• Standardize: Use consistent templates, naming conventions, and formatting.
• Sustain: Put controls in place to keep the knowledge base current.

This step alone creates massive value. Many companies find that simply cLEANing and centralizing documents eliminates hours of wasted time, even before AI is added.

Step 3: Build the Foundation with an LLM

Now comes the technology layer: the large language model (LLM).

Think of the LLM as the brain of the agent. It provides reasoning, context, and conversational ability. But instead of letting it “guess” answers from general internet training, you connect it directly to your curated company knowledge base.

The best way to do this is with retrieval-augmented generation (RAG). In plain terms, the LLM retrieves your documents and then generates an answer that combines natural language with company-specific accuracy.

Example: If an operator asks, “What’s our definition of standard work?” the agent won’t just pull a generic LEAN definition from a textbook. It will answer with your company’s definition, citing your training material, while still explaining it conversationally.

This step requires some technical setup, but platforms like OpenAI, Anthropic, or Azure make it accessible. Security is critical, your documents should be stored privately, not shared publicly. Many companies deploy behind firewalls or use secure APIs.

Step 4: Embed LEAN Thinking into the Agent

Here’s where you can differentiate. Don’t just feed the AI with documents—teach it to think LEAN.

That means training the agent to:

• Encourage structured problem solving (A3, 5 Whys) rather than quick fixes.
• Reinforce cultural values: respect for people, continuous improvement, focus on the customer.
• Suggest LEAN tools in context. If a leader asks, “How should I handle recurring downtime?” the agent can recommend: “Try root cause analysis with a cross-functional team, consider a 5 Why exercise or an A3.”

This transforms the AI from a passive “answer bot” into an active LEAN coach. It doesn’t just provide information, it reinforces the behaviors and systems that sustain improvement.

Step 5: Pilot and Iterate

Like any LEAN initiative, don’t try to “boil the ocean.” Start with a focused pilot.

For example:

• Deploy the AI Agent on the shop floor to support maintenance troubleshooting.
• Use it in HR to answer onboarding questions for new hires.
• Introduce it in CI workshops to guide A3 problem-solving templates.

Roll it out to a small group, gather feedback, and improve it. Treat the agent itself as a PDCA cycle:

• Plan: Define the use case.
• Do: Launch with a small group.
• Check: Gather user feedback and measure results.
• Act: Update the knowledge base and retrain the agent as needed.

You’ll quickly discover where answers are unclear, where documents are missing, or where users need more context. This is normal—and valuable. It shows you where your systems need strengthening.

Step 6: Scale and Sustain

Once the pilot proves value, it’s time to scale.

This means:

• Expanding to more functions (operations, quality, HR, engineering).
• Rolling out across plants or regions.
• Integrating with daily management: use the agent in shift huddles, Gemba walks, and kaizen events.

Sustainment is the hard part. An AI Agent is only as trustworthy as its information. Outdated SOPs or inconsistent policies will erode confidence. That’s why ownership matters—assign a process owner responsible for keeping the knowledge base fresh, just like you would with standard work.

When sustained, the AI Agent becomes part of the fabric of the LEAN Enterprise. It doesn’t replace leaders or trainers, it augments them, making knowledge and coaching available 24/7.

Conclusion

AI Agents in manufacturing aren’t science fiction. They’re practical tools, built on LEAN principles, that can reduce waste, accelerate training, and empower people.

When you combine the reasoning power of LLMs with the discipline of LEAN and the specificity of your company’s knowledge, you get more than a chatbot, you get a digital sensei. A coach that reinforces culture, answers questions, and frees people to solve higher-value problems.

The companies that act now, capturing their knowledge, embedding it into AI Agents, and aligning it with LEAN thinking, will create a true competitive advantage. Not just in productivity and quality, but in culture and capability.

LEAN taught us that excellence is built on people. AI, used the right way, can help every person access the knowledge they need to succeed. That’s how we build the future of manufacturing—smarter, faster, and more human.