AI will infiltrate the industrial workforce in 2026—let’s apply it to training the next generation, not replacing them

A silent crisis is shaking the very foundations of modern society.

The industrial workforce responsible for building the global economy is at risk of crumbling. The people charged with keeping our power grids online, factories humming, utilities reliable, and supply chains moving uninterrupted are retiring at a fast clip. Sure, this may seem like the natural cycle of things as mass retirement opens the door to at least 3.8 million jobs. But it hides a deeply troubling reality: tacit knowledge, along with practical skills refined over decades of hands-on work, is at risk of leaving with them. 

While technologies from artificial intelligence to robotics to computer vision are transforming industrial operations, we’re dangerously close as a society to losing the ability to diagnose a failing motor by sound, read analog engineering drawings, or understand the quirks of a 60-year-old machine that predates Disco. 

This kind of expertise is rarely written down in one place and always valuable, especially when there’s a mechanical issue or system-level disruption. Meanwhile, generative AI is making information feel instantly available. 

The tension here is real and consequential. The question facing junior industrial professionals across industries, from heavy manufacturing to utilities to supply chain: If software can answer questions in seconds, why spend years learning by doing (and, in some cases, failing)?

When it comes to industrial operations, the answer is actually quite simple. We can’t afford to lose earned knowledge or train a workforce that uses AI without understanding the system it supports from soup to nuts. 

The opportunity with investing in AI is to preserve the knowledge needed to keep lights on, factories humming, and society moving, and apply it at scale. Success requires keeping pace with gen AI advancements while adapting to macro factors and global challenges that come in waves. This opens the door wider for AI working with humans (and vice versa) to build resilience into essential industries powering the world’s economy for decades to come.

AI’s Elevated Role: Not On Autopilot

Industry runs on machinery and management making the right calls. Consistently. Confidently. But it’s not that simple.

Across the industrial economy, it’s common for a small group of experienced workers to serve as keepers of an outsized amount of knowledge. They know which vibration or clanking noise spells trouble, which workaround keeps production going during a shortage, and which drawing accurately reflects the latest hardware installments in the field.

At the same time, many companies still operate using a patchwork of small group expertise, spreadsheets, and fragmented databases requiring manual collation. When one system goes down or an expert retires (or, frankly, is out sick), it’s nearly impossible to answer simple questions like: what parts do we have, which assets matter most, or where is money being wasted?

These aren’t small businesses or Mom and Pop shops. Manufacturing giants, automobile OEMs, fleet management companies, utilities, and defense contractors are among the collection of expertise-dependent organizations primed for AI support. Every organization is different but they encounter the same critical problem that AI can help solve: data is everywhere, it’s fragmented or siloed, and organizing it requires plumbing every system and file repository to combine relevant information. Humans can collate and organize data collections in weeks or months with a dedicated effort. Today’s AI, meanwhile, can organize data deluges in minutes or hours.

Trade Painstaking Decisions for Decision Intelligence

The other driving factor: Industrial work is full of tradeoffs. Factory managers, technicians, floor mechanics, and engineers are constantly faced with dilemmas: fix or replace, act now or wait, cut costs or reduce risk, maximize uptime or meet sustainability goals. These decisions affect millions of assets and must be made under regulatory scrutiny, often with incomplete information. AI helps people make better decisions, not turn on autopilot and zone out.

AI is good at pulling together signals from various sources and making sense of them in a way that humans understand immediately, such as maintenance history, sensor data, demand forecasts, market conditions, and environmental risks. When used well, AI can help teams plan, predict, and prioritize. AI backstops human judgment. With the available tech, neither human or machine should be left to their own devices.

This ability to support decision-making goes beyond convenience or cost efficiency. It’s a powerful industrial asset as power grids, utilities, and manufacturers face unprecedented demands from electrification, data center growth and expansion, and full-scale automation. AI can help spot problems earlier, justify investment choices, and safely extend the life of aging equipment. That is not automation for its own sake. It is about keeping essential systems reliable.

AI: A Workforce Equalizer for Trade and Technical Work

Younger workers (18-35) are often criticized for relying too much on technology, or expected to do so when a system falters or machinery requires maintenance. In reality, they want tools that help them do meaningful work safely and efficiently.  

Younger workers are also among the first groups to fully embrace that AI advances insanely fast. The tech available today is good enough to accurately reflect seasoned experience, shorten learning curves, and close talent gaps with near-instant, but verified and context-rich data gleaned from real-world work. 

Why that matters: AI can demolish the barrier to entry to industrial jobs without neutering the skills required to do the job. Younger pros benefit from AI’s ability to dramatically reduce time spent mining for information or wrestling with fragmented systems. AI actually renders jobs more technical and more rewarding. Both appealing to younger workforce members.

Industrial roles from field service manager to HVAC technician to factory shift worker keep the world running, yet they are often misrepresented as tech-agnostic or low-skill. In reality, they require deep expertise and a variety of skill sets. Across the industrial economy, AI is poised to accelerate skills training ten-fold and open the door for a new generation of industrial pros to step in—and here’s the important bit—without sacrificing quality, let alone imploding the entire system.

We’re seeing vocational programs at community college enrollment numbers tick up, increasing 16% in 2025 compared to last year. This is a signal that Gen Z is open-minded and ready to take on blue collar work in favor of desk jobs. It’s also evidence that AI is not only serving as an equalizer, but actively reshaping and advancing blue collar’s next generation.

Embrace AI as a Workforce Asset, Or Lose Everything

While industrial AI is just beginning to enter mainstream conversations thanks to, for example, $61B in data center contracts in 2025 alone and a buzzy race to collect GPUs for full-scale AI deployments in the physical world, the window to act is already closing. I estimate we have 1-2 years left to capture decades of industrial knowledge in AI applications and front-edge tech platforms supported by AI on the backend, or we lose it. Everything.

The industrial economy operates in the real world, with communities around the globe relying on it for jobs, electricity, and much more. People building AI to meet unprecedented demand need to ship practical tools that respect human experience, support better decisions, and make complex systems easier to understand—whether you’ve been on the job for four weeks or 40 years. Industrial operations are deeply technical, nuanced, and complex. AI alone can’t do the work at scale. Systems need industry-rich context and informed prompts from human counterparts to produce outcomes that solve problems and stand the test of time.

AI can (and should) be applied to industrial operations in an authoritative, but supporting role across sectors and specific use cases. 

But industry must adopt innovation that preserves nuance, predictive maintenance inclinations, and incident-specific experience only possible from years of hands-on work. That’s how we add resilience to global operations. To do this in hours and days, not months, we need both AI and people. I’m optimistic that AI won’t hollow out the industrial workforce. In fact, incorporating AI at scale to support a younger workforce may be the only way to sustain it.

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