The manufacturing skills gap is one of the most pressing challenges facing industrial operations today. By 2028, Deloitte predicts 2.4 million unfilled jobs—threatening up to $1 trillion in lost output.

Three forces are driving this growing talent shortage:

  • A Wave of Retirements: Experienced professionals are exiting the workforce, taking decades of knowledge with them.
  • High Turnover: About 21% of Millennials report switching jobs within the last year—a stark contrast to previous generations like the Baby Boomers and Gen X.
  • Technology Outpacing Adoption: As new systems are introduced, the skills required to operate them continue to evolve and upskilling is required for successful adoption.

This leaves manufacturers in a difficult position.

Even as AI and automation technologies promise performance gains, many plants lack the skilled personnel needed to fully implement or manage them. Without the right people and the right skillsets, even the most advanced tools can fall short.

Retirements Are Widening the Manufacturing Skills Gap

The wave of retirements is draining the industry of vital expertise. For decades, these professionals have fine-tuned complex processes through hands-on experience.

But this knowledge is at risk. Labor participation in manufacturing has been declining for over 20 years. Recruitment struggles and outdated perceptions about industrial work only make matters worse.

Without effective knowledge transfer strategies, manufacturers face:

  • Lower productivity from less experienced operators
  • Risk of resurfacing historical operating struggles
  • Reduced innovation due to a loss of deep process understanding

Preserving and passing on expert knowledge is no longer optional. It’s essential to remain competitive.

AI-Powered Training: Rethinking Operator and Engineer Onboarding

AI is transforming how new front-line employees learn complex industrial systems. Experience that was once acquired over the course of a decades-long career is now being developed rapidly through simulation environments. Advanced AI tools are being used to create these simulation environments replicating real world scenarios specific to a particular unit within a specific plant. These simulations offer advantages like:

  • Safe, hands-on training without operational risk
  • Faster onboarding and shorter learning curves
  • Interactive environments that make optimization more intuitive

Unlike traditional training methods—manuals, static videos, or passive shadowing—simulation-based learning immerses operators in real-time decision-making. This approach turns theoretical knowledge into practical experience, accelerating the time it takes for new hires to become effective contributors.

Breaking Down Silos: How AI Democratizes Expertise

Industrial companies are broadening the way they think about AI. What was once used as a point solution to specific use cases (e.g. predictive maintenance) is becoming a way of thinking and working to unlock and scale operational knowledge across the organization. 

Closed Loop AI Optimization (AIO) is a concrete example of AI driving a fundamental shift in the way companies think about running their production facilities. It models complex process relationships by learning directly from years of historical operating data—without relying on rigid simulation assumptions. This AI doesn’t just replicate past decisions; it learns how expert operators would respond under hundreds of millions of possible conditions.

This approach creates a living system of institutional knowledge—one that’s continuously updated and accessible to all team members. Operators and engineers can now test what-if scenarios, explore disturbances, and validate how the AI controller would respond—all in a risk-free, offline environment. This builds trust, accelerates onboarding, and reduces reliance on a few seasoned experts.

At the same time, emerging generative AI technologies are making insights from complex process models more accessible. They interpret data-driven outputs and controller behavior in plain, context-aware language. When combined with Closed Loop AI Optimization, generative AI can translate the “why” behind decisions—making advanced process control understandable to a broader audience, regardless of technical background.

Together, these AI systems break down silos between disciplines and experience levels—giving everyone the tools to contribute to smarter, faster, and safer decisions.

Preserving Tribal Knowledge Through AI

One of the biggest risks of the skills gap is the loss of tribal knowledge—insights learned over years of operating a specific plant or process.

AI systems trained on historical operational data can capture this expertise. These models often identify patterns or correlations once known only to veteran operators. By embedding this knowledge into the system, manufacturers ensure that critical insights are retained and accessible to new personnel.

This not only safeguards performance consistency but also empowers new operators to question outdated assumptions and discover better ways of doing things.

Earning Operator Trust in AI Tools

On paper, leveraging AI to close the manufacturing skills gap seems like a clear win. But in reality, adoption is often slow. One of the biggest barriers? Trust.

In industries where even small errors can lead to millions in lost margins—or compromise safety and reliability—operators are understandably cautious about handing decision-making power to AI. This is especially true for experienced operators who’ve built deep, intuitive knowledge over decades on the plant floor.

To build trust, process manufacturers must take deliberate steps:

  • Engage operators early in testing and training
  • Communicate transparently about the role of AI
  • Provide hands-on learning opportunities

Big West Oil did just that. They involved their operators in feedback loops, making them active participants in AI model development. This collaborative approach allowed veteran personnel to experience the system’s capabilities firsthand and contribute valuable insights to its development process, fostering a sense of ownership and partnership rather than imposition.

Another key success factor was explainability. When operators understood how AI made its decisions, they were more likely to trust it. As operators witnessed the AI system successfully optimizing complex processes and preventing potential operational issues through closed loop AI optimization, their confidence developed organically.

By focusing on augmentation—not replacement—Big West Oil helped their teams see AI as a partner that enhances their skills, not a threat to their jobs.

Reshaping Workforce Development for the Next Generation

AI is not just solving today’s skills gap—it’s reshaping how industrial teams are trained and developed. This shift couldn’t come at a more crucial time as we face projected labor shortages across manufacturing sectors.

These advancements deliver tangible results. Boston Consulting Group research shows AI can help reduce manufacturing costs by 14%. These gains help offset labor shortages while improving ROI on workforce development efforts.

These benefits extend across the heavy process industries, including mining and cement manufacturing. The manufacturers who thrive will be those who view AI not just as process automation, but as a powerful ally in building the highly skilled workforce required for tomorrow’s industrial landscape.

AI Will Empower, Not Replace in the Manufacturing Sector

The skills gap is not just about hiring—it’s about knowledge. And the real power of AI lies in enabling people to do more with that knowledge.

The value of AI in process optimization isn’t in replacing people—it’s in unlocking their full potential. It accelerates learning, supports better decision-making, and enhances performance at every level.

Plants that recognize AI as a people-first tool—not just an automation solution—will be the ones that close the skills gap and build a competitive workforce for the future.

Explore What’s Possible with Imubit

Leading manufacturers are looking to technology providers for guidance on leveraging AI effectively to address both business and workforce challenges. Imubit partners with industry-leading organizations implementing their Closed Loop AI Optimization (AIO) solution to bolster their bottom line through real-time process optimization and upskilling their workforce in the process. 

Imubit’s Industrial AI Platform:

  • Transforms institutional knowledge into a dynamic model that mirrors how your plant truly behaves
  • Enables AI-powered simulation environments, allowing new operators to safely explore how the controller responds to disturbances
  • Builds operator confidence and trust, with explainable recommendations and outcomes they can understand and validate
  • Supports cross-functional teams—from control engineers to operations—without needing deep APC/RTO expertise
  • Shortens time-to-value, with implementation timelines under 6 months versus 18–24 months for traditional tools

By making expert-level decision logic visible, accessible, and testable, Imubit bridges the gap between experienced operators and the next generation—helping teams grow stronger, faster.

Ready to explore how Closed Loop AIO can help your team learn and perform better?

Schedule a demo to see it in action.