By Mitch Mitchem

Is it useful?

It was the last year of my grandfather's life. My dad and I went out to his place deep in the southern part of Appalachia, on the West Virginia and Virginia border. We were at his house, the one he built with his own two hands. He was moving slower, as one does when they have a brain tumor that will end their life shortly, but he was still sharp. Still proud. He wanted to show us something.

We stepped into the shed he had built decades earlier, big, clean, hand-painted, every tool exactly where it belonged. It wasn't cluttered. It was curated. This wasn't a man who kept things "just in case." If a tool didn't help, it didn't stay.

He walked us over to a wall where three tools hung in a perfect row: a hand saw, a hammer, and a level. The first thing he said I did not fully understand at the time.

“When I am gone, the family will fight over this and that. They will all want things they think I care about. And they will be wrong.
It is these three things I find the most valuable. These three will get you through almost anything," he said. "I built this house and everything around it with them."
The saw was for shaping. Cutting things down to what they needed to be.
The hammer was for building. Joining things together in a way that holds.
And the level? "The level doesn't care how good you think you are," he said. "It tells the truth. No opinions. Just the truth."

Then he looked me in the eye and said something I'll never forget:

"Boy, don't ever keep a tool that doesn't help. If it's not useful, throw it out."

I didn't realize it then, but he wasn't just talking about carpentry. He was handing down a philosophy. One that matters now more than ever. Because here's the truth I believe but others are ignoring: Blue-collar workers will lead the AI revolution. Not the tech bros. Not the Ivy Leaguers. Not the executive suite. Not the corporate office.

Why? Because they understand tools. Because they know usefulness when they see it. And most importantly, because they don't have an ego about intelligence. They want to get shit done.

They're not trying to protect some $100,000 degree that's becoming irrelevant. They're not worried about looking smart in a meeting. They just want to solve the problem and get on with their day.

If AI saves time? They'll use it. If AI makes the job safer or faster? They'll use it. If AI lets them get home to their families an hour earlier? They'll use it. No need for jargon. No need for permission. Just give them the right tool and get out of the way.

“And here’s a brutal truth: once they understand the tool, they are, from our work in the field, over 200% better at using AI than their white-collar colleagues. Let that sink in”

My grandfather didn't need a résumé. He fought hand-to-hand in the Philippines during World War Two. He survived hell on Earth. He built a house. Raised a family. Left behind a philosophy that's more relevant now than ever.

And it starts with three simple questions:

Can it shape?
Can it build?
Can it get me to the truth?

If AI can do that, it belongs in the shed.

The “Research” Says Otherwise (But the Research is Wrong)

Here's what the experts will tell you about AI adoption in America: White-collar workers are three times more likely to use AI frequently than blue-collar workers. Only 9% of blue-collar workers use AI regularly, compared to 27% of their office counterparts. The data seems clear—blue-collar workers are being left behind in the AI revolution.

But I've spent the last few years training over 40,000 people in AI implementation, many of them blue-collar workers in construction, factories, and now oil and gas. And this week, I stood in front of 150 blue-collar workers and watched something the research said shouldn't happen.

They loved it.

Not tolerated it. Not grudgingly accepted it. Loved it.

Because when you show a blue-collar worker—the people who build and manage every part of our valuable infrastructure—how AI can solve a real problem, how it can make their job safer, faster, or easier, they don't care about the technology. They care about the result.

The research is measuring the wrong thing. It's counting adoption in environments where AI is presented as abstract productivity enhancement, where workers are expected to figure it out on their own, where the focus is on the technology instead of the problem it solves.

But my grandfather's philosophy cuts through all that noise: "Don't ever keep a tool that doesn't help."

Blue-collar workers aren't rejecting AI. They're rejecting useless implementations of AI. And they're absolutely right to do so.

The Saw: Cutting Through the Misconceptions

Let me tell you what really happens when you put AI in the hands of someone who understands tools.

Last month, I worked with a group of utility workers. These are the people who keep your lights on, your water running, your internet connected. They work in dangerous conditions, often alone, always under pressure to get things right the first time.

The research would tell you these workers are "digitally disadvantaged." That they lack the "digital literacy" to adopt AI technologies. That they're resistant to change.

The research is wrong.

When we showed them how AI could help them diagnose equipment problems faster, they didn't ask about algorithms or machine learning models. They asked: "Will this help me find the problem before it becomes dangerous?" When the answer was yes, they were in. The "ah-ha" moments arrived fast and stuck.

When we demonstrated how AI could help them write better incident reports, turning their field observations into clear, detailed documentation—they didn't worry about whether using AI was "cheating." They asked: "Will this help me communicate what happened so it doesn't happen again?" When the answer was yes, they were in.

When we showed them how AI could help them plan their routes more efficiently, avoiding traffic and weather delays, they didn't debate the ethics of artificial intelligence. They asked: "Will this get me home to my family on time?" When the answer was yes, they were in.

This is the saw at work, cutting through the misconceptions, the jargon, the academic debates about AI adoption. Blue-collar workers don't need to understand how AI works any more than they need to understand internal combustion engines to drive a truck. They need to understand what it does and whether it helps.

The National Bureau of Economic Research found that blue-collar workers in production and operations roles have a 22.1% AI adoption rate. But here's what the research doesn't tell you: that number jumps dramatically when you remove the barriers and provide proper training.

The problem isn't the workers. It's how we're introducing the tools.

The Hammer: Building Understanding Through Real Problems

The difference between successful AI adoption and failure isn't about intelligence or education level. It's about approach. It's about training. This is why most of you will fail at a DIY AI strategy. You need to know how to train for it.

“It’s the best damn. hammer I have ever had.” - Roby Mitchem, 1977

White-collar workers often encounter AI through productivity tools—writing assistants, data analysis, presentation software. These applications are designed for knowledge work, for people who spend their days manipulating information rather than physical objects.

Blue-collar workers encounter AI differently. For them, AI needs to solve tangible problems: predicting when equipment will fail, optimizing supply chains, improving safety protocols, streamlining communication with supervisors and customers. Helping them take the simple solution and put it into big "corporate speak" so their boss feels like they get it.

The hammer builds by joining things together in a way that holds. In AI training, this means connecting the technology to real-world applications that matter to the worker's daily experience.

The key is starting with the problem, not the technology.

When we trained those 150 blue-collar workers this week, we didn't begin with a lecture about large language models or neural networks. We started with my grandfather's philosophy: "If it doesn't solve a problem, it belongs in the trash."

Then we showed them seven ways AI could solve their problems:

Always Make It Useful
Use the Prompt Chain
Talk to AI Like a Teammate
Be Direct
Solve Real Shit
Stay in Control

Problem-Solving Focus: We constantly returned to the central question: How can this help you solve problems faster, better, or cheaper?

This approach works because it respects what blue-collar workers already know: tools are only as good as their application to real problems.

The Level: The Truth About What's Really Happening

The level doesn't care how good you think you are. It tells the truth. No ego. No opinions. Just reality.

So here's the truth about AI adoption that the research isn't capturing:

The gap between blue-collar and white-collar AI adoption isn't about capability or interest. It's about access, training quality, and organizational support. When we remove those barriers—when we provide proper training, address real problems, and maintain human agency—blue-collar workers don't just adopt AI. They excel with it.

The research shows that only 24% of production and frontline organizations have taken action on AI, compared to higher rates in knowledge-work industries. But this organizational lag doesn't reflect individual worker enthusiasm. It reflects management assumptions about their workforce's capabilities. It reflects the ignorance of leadership.

Those assumptions are wrong.

Our clients are different. They put AI first. In our training programs, we've consistently found that blue-collar workers are often more enthusiastic about AI applications than their white-collar counterparts. Why? Because they're not protecting an identity built around being "smart" or "educated." They're focused on getting results.

A recent study by the National Fire Protection Association found that 46% of industry professionals plan to adopt more digital tools in their daily operations, a 9% increase from the previous year. This growth is driven by practical applications: predictive analytics for safety monitoring, AI-powered equipment diagnostics, automated reporting systems.

These workers aren't adopting AI because it's trendy or because it makes them look innovative. They're adopting it because it works.

The truth is that blue-collar workers have been using AI longer than most white-collar workers realize. The GPS systems in their trucks use AI for route optimization. The diagnostic tools in their equipment use AI for pattern recognition. The safety monitoring systems in their workplaces use AI for risk assessment.

They just don't call it AI. They call it "the tool that helps me do my job better."

The Real Barriers (And How to Remove Them)

The research identifies several barriers to blue-collar AI adoption: limited digital literacy, lack of access to technology, insufficient training resources, and organizational resistance to change.

These barriers are built on the lie of a blue-collar worker being less intelligent. My dad fought this for years as a meter reader, then pump station worker for the water company, yet he rose, after 40 years, to end his career as a COO. Because he knew that the laborer is most likely smarter in ways the college grad can't imagine. And here's a brutal truth: once they understand the tool, they are, from our work in the field, over 200% better at using AI than their white-collar colleagues. Let that sink in.

So how does it truly work?

When we trained those utility workers, we didn't give them a watered-down version of AI. We gave them the same powerful tools that white-collar workers use, but we presented them in the context of their actual work challenges. When we worked with manufacturing teams, we didn't assume they couldn't handle complex AI applications. We showed them how those applications could prevent equipment failures, reduce waste, and improve safety. When we trained construction crews, we didn't talk down to them about technology. We demonstrated how AI could help them estimate materials more accurately, schedule work more efficiently, and communicate more effectively with clients.

The results speak for themselves: enthusiasm, rapid adoption, and measurable improvements in productivity and safety.

Meanwhile, when we work with corporate people, we get massive resistance, skepticism, anger, and people who are defensive about no longer being the smartest entity in the room.

The Training That Actually Works

After training over 40,000 people in AI implementation, I've learned that successful blue-collar AI adoption follows a predictable pattern. It's not about the technology; it's about the approach.

Our approach isn't built on theory. It's built on results.

We focus on what matters most to frontline teams—real problems, not abstract ideas. Instead of overwhelming workers with jargon or complex systems, we center our efforts around relevance, clarity, and confidence.
We begin where the pain is felt. Not with possibilities or hype, but with operational bottlenecks, safety challenges, and inefficiencies that impact daily performance.
We speak their language. If a tool can't be explained clearly or used intuitively on Day One, it doesn't belong in the workflow.
We prioritize momentum. Our sessions are designed for fast wins, because when people see immediate value, they lean in.
We empower, not replace. Workers aren't being sidelined. They're being equipped. AI becomes a trusted extension of their judgment, not a substitute for it.
We address what others ignore. From job security to trust in automation, we surface concerns early and guide teams through them with clarity and care.
We stay real-world. Everything we do is tied to scenarios that matter. No fluff. No filler.
And we don't vanish after the session. Real enablement means sticking around to support application, iteration, and evolution on the ground.

This is why our programs don't just inform, they transform. If you want your teams to not just understand AI but own it, we're ready when you are.

The Future Belongs to the Tool Users

My grandfather was my hero. I can barely speak of him without an overwhelming emotional reaction. I learned so much in the limited time we had together.

He love America. He loved his family. He believed in simple ideals. Who knew a coal miner with a 8th grade education could teach us all so much about the truths of life, but he did.

He built his house in the late 1940s after the war. He built it with hand tools, careful planning, neighbors, other family, and an understanding that every tool had to earn its place in the shed.

Today's AI tools are more sophisticated than anything he could have imagined, but the principle remains the same: if it doesn't help, it doesn't belong.

Blue-collar workers understand this principle in their bones. They've been evaluating tools their entire careers, keeping what works and discarding what doesn't. They don't need permission to use AI, and they don't need validation from experts.

Because that's what tool users do. They find what works, and they use it.

The AI revolution isn't coming to blue-collar America. It's already here, in the hands of people who understand that the best tool is the one that helps you get the job done.

He would approve.

Mitch Mitchem is the founder of HIVE, a company specializing in AI implementation and enablement training. He has trained over 40,000 professionals in AI adoption across industries, with particular expertise in blue-collar workforce development. His upcoming books release in the fall of 2025.

The Saw, The Hammer, The Level: Why Blue-Collar Workers Could Lead the AI Revolution