Part 1: How We’re Integrating AI in Manufacturing and Building the Foundation for Industry 4.0

Artificial intelligence in a manufacturing environment sounds like it should be easy enough to implement, like switching a flip – install software and connect machines, then suddenly the factory is “smart.” But, in reality, meaningful AI integration is a long-term, strategic journey built on data, infrastructure, and clear business objectives. 

This blog kicks off a follow-along series documenting how our teams are integrating AI into our manufacturing operations. Rather than focusing on hype, we’re sharing the real, behind-the-scenes work required to build a connected factory aligned with Industry 4.0principles. 

For this first installment, Craig Mayrer, a member of our leadership team who oversees our AI and data automation initiatives, weighs in on where this journey began, what we’re working toward, and how we’re approaching AI adoption across our machining operations. 

AI in Manufacturing vs. Industry 4.0: Setting the Context

AI is often used interchangeably with Industry 4.0,  but they are not the same thing. 

Industry 4.0 represents the broader vision of a digitally connected factory, where machines, systems, and people are linked through reliable data. AI is a powerful component of that vision, but it only works when the foundational pieces are in place. 

Recent advances in AI are accelerating what’s possible, but success still depends on years of intentional groundwork. 

When Did Our AI Journey Begin?

Our path toward AI-enabled manufacturing began well before today’s surge in AI tools. 

“When I first came on board in 2019, there was already a strong desire to automate and leverage machine data,” Craig explains. 

At that time, early testing was underway to extract data from machines using third-party solutions. While those initial efforts were limited, they marked the starting point of a longer journey. 

From there, progress came in deliberate stages: 

• Hardware upgrades to enable machine data tagging 
• Creating methods to reliably extract and share machine data 
• Developing systems to store, organize, and contextualize that data 

Each year has built on the last. These steps weren’t about AI models yet – they were about creating the data infrastructure required to support AI in the future. 

“AI is a component of the greater Industry 4.0 vision. The recent advancements are truly accelerating it,  but only because the groundwork is already in place.”

What Do We Want AI to Do in Our Manufacturing Operations?

Rather than deploying AI for its own sake, our focus is on clear, measurable outcomes within machining operations. 

Craig outlined several key areas where AI can deliver the most value: 

Key AI Focus Areas in Machining Operations

• Leverage the data we’ve been building.
  Use AI to uncover intersections and patterns that are not easily identified through traditional SPC or legacy analysis tools. 
• Define tangible, goal-driven use cases.
  Identify specific objectives where AI agents can actively support operations. 
• Drive continuous process improvement, including: 
  • Yield improvement 
  • Reduced cycle times 
  • Optimized tool utilization 

Each of these areas has multiple contributing variables, many of which are still being developed and refined. AI is the solution for evaluating these variables together rather than in isolation. 

Where Are We Starting with AI Integration?

AI integration isn’t happening in a single place or at a single pace. 

All current efforts are focused on machining operations, but not all departments are at the same stage of readiness. 

A Phased, Department-Specific Approach

• Advanced-stage department
  One department is leading the way by finding a partner to utilize a subset of historical data to build an AI model aligned with defined business objectives. If successful, this approach can be expanded to other processes and departments. 
• Early-stage departments
  Some departments have only recently begun automated data collection, with limited historical data available for analysis. 
• Foundational-stage departments
  Other areas are still in the hardware upgrade phase, laying the groundwork needed before data automation, and eventually AI, can be introduced. 

This staggered approach ensures that each area progresses at the right pace, without forcing AI into environments that aren’t ready to support it. 

What Do We Aim to Accomplish in the Next Year?

Over the next 12 months, our AI and Industry 4.0 roadmap is focused on strengthening both capability and consistency. 

Key goals include: 

• Advancing a partnership toward a functional AI model that supports defined business objectives 
• Continuing to build reliable, high-quality data from recently automated machining lines 

• Expanding hardware upgrades to support additional manufacturing processes 
• Refining data collection methods by identifying and closing gaps in existing data sets 

Each of these steps moves us closer to deploying AI agents that can meaningfully improve operational performance. 

Why We’re Sharing This Journey

Many manufacturing companies discuss AI adoption. Fewer talk openly about the years of preparation required to make it work. 

This series is about transparency, about sharing how AI integration actually happens inside a manufacturing facility committed to Industry 4.0 and a connected factory model. 

In upcoming posts, we’ll dive deeper into projects, lessons learned, and how data-driven decision-making is reshaping our operations. 

Interested in how manufacturing companies are leveraging AI and Industry 4.0? Follow along as we document the journey from data foundation to intelligent operations.

Frequently Asked Questions about AI in Manufacturing (FAQs)

How are artificial intelligence and Industry 4.0 different?

Industry 4.0 refers to the broader concept of a connected, data-driven factory where machines, systems, and people are digitally integrated. AI is one component of Industry 4.0 and it relies on this connected infrastructure to analyze data, identify patterns, and support decision-making. 

Why can’t manufacturers implement AI immediately?

AI models require clean, reliable, and well-structured data. Many manufacturing environments must first invest in hardware upgrades, data automation, and data storage systems before AI can be successfully deployed. 

How long does it take to get a foundation for AI up and running in a manufacturing environment?

Building the foundation can take several years. In our case, the journey began years ago and has progressed incrementally through hardware upgrades, data collection, and system integration to reach today’s AI-ready state. 

What manufacturing processes benefit most from AI?

AI is particularly effective in machining operations where large volumes of process data are available. Common benefits include improved yield, reduced cycle times, optimized tool usage, and better process consistency.