Manufacturing is changing faster than many education programs were designed to accommodate. As automation, AI and digital technologies become more deeply embedded in production, the skills required on the factory floor are evolving just as quickly. According to Deloitte, roughly 40% of the core skills needed in advanced manufacturing change within a five-year period, increasing demand for expertise in areas such as automation, advanced electronics and data analytics.

For John Kraus, that reality underscores why manufacturers can no longer treat workforce development as someone else's responsibility. As Vice President of Operations at Jabil, a global manufacturing solutions and supply chain partner to OEMs, Kraus oversees large-scale production operations while helping build the talent pipeline needed to support increasingly automated facilities. He argues that talent development, industry-education partnerships and continuous upskilling have become strategic priorities alongside investments in AI, automation and other advanced technologies.

In this Q&A, Kraus discusses the challenges of AI adoption on the factory floor, persistent skilled labor shortages and how manufacturers are preparing workers for roles that look markedly different from those of a decade ago.

Machine Design: Are workforce shortages becoming a bottleneck to AI adoption in manufacturing?

John Kraus: There are a couple of things happening at the same time. Advanced manufacturing, semiconductor production, AI infrastructure and data center investments are all expanding simultaneously, so we’re seeing industrial growth in the United States we haven’t experienced in decades. Demand for skilled workers is growing just as quickly, but many of the people in critical manufacturing trades are aging out of the workforce. For years, there simply was not enough investment in domestic apprenticeship programs or talent development to bring up the next generation. 

Automation and AI can absolutely help close part of that gap, but they are not the complete solution. Before something can be automated, the process has to be simplified, optimized and standardized. Without those three things, automation will fail.

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Historically, manufacturing relied on people making decisions throughout the production process and compensating for variation. Automation doesn’t work that way. It requires processes that are intentionally designed for consistency from the beginning, and that takes skilled engineers and technicians who understand manufacturing and automation.

From my perspective, workforce shortages are becoming one of the biggest challenges to how quickly manufacturers can adopt AI. The technology is advancing very quickly, but technology alone doesn’t create a competitive advantage. You still need people who know how to implement it, maintain it, improve it and use the data it generates to make better decisions. Investing in technology without investing in people creates an imbalance and over time that becomes a bottleneck to innovation.

MD: How are manufacturers changing recruiting and training strategies for AI-enabled facilities?

JK: Manufacturers have realized they cannot simply wait for talent to appear. The industry has to take responsibility for investing in the workforce it needs. That starts with upskilling the people we already have while building stronger partnerships with educational institutions to develop the next generation. The value comes from making sure students are learning skills that actually translate into careers instead of waiting until they graduate and trying to retrain them from scratch.

Manufacturing is evolving much faster than many educational programs were originally designed to accommodate. That is why partnerships matter. They give educators the opportunity to come into factories, understand what is happening on the production floor and take those insights back into the classroom. For students, it means exposure to real manufacturing environments, industry-recognized certifications and hands-on experience with technologies they will actually encounter when they enter the workforce.

At Jabil, every one of our more than 30 manufacturing sites across the U.S. has some level of engagement with technical schools, trade schools or universities because we focus on developing the talent we know we’ll need in the future. We provide equipment, curriculum support, mentorship and opportunities for students to gain practical experience. Industry cannot sit back and hope someone else solves the workforce challenge. We have to actively participate in building the pipeline.

MD: What workforce skills are most in demand as factories become more automated?

JK: As advanced manufacturing becomes more automated, the skill set is expanding beyond traditional manufacturing expertise. Today we are looking for people who understand software, hardware, controls and design for automation, while also having strong manufacturing fundamentals. It is not enough to know how a process works. Engineers need to understand how to design that process so it can be automated successfully.

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AI is also changing the way decisions are made inside the factory. Manufacturing is moving from reactive decision-making toward predictive, data-driven decision-making. That creates demand for engineers and manufacturing professionals who can use AI, advanced analytics and digital tools to improve productivity, quality and overall manufacturing performance. Data literacy is becoming just as important as mechanical or manufacturing knowledge.

The workforce of the future will need a combination of technical manufacturing expertise, automation knowledge, data literacy and the ability to continuously adapt as technologies evolve. The technologies we deploy today did not exist in the same form just a few years ago and will continue changing. That is why adaptability may be one of the most valuable skills manufacturers can develop.

MD: Which education and apprenticeship models are proving effective?

JK: We have generally seen the most success partnering with smaller trade schools and junior colleges because they are often more willing to evaluate curriculum, collaborate with industry and adapt more quickly to changing workforce needs. Manufacturing technologies are evolving rapidly and institutions that can move quickly are often better positioned to prepare students for today’s factory environment.

The objective is not necessarily for schools to teach every new technology that comes along. It is to help students develop the skills, attributes and mindset they will need to adapt as technology changes. Industry has to help educational institutions understand where manufacturing is going so curriculum can evolve accordingly. That means inviting educators into factories, sharing what technologies are actually being deployed and maintaining an ongoing dialogue instead of treating education and industry as separate worlds.

MD: How is AI changing the role of design, manufacturing and automation engineers, and what new competencies will engineers need to remain effective in increasingly autonomous factories?

JK: One of the biggest shifts is that manufacturing processes can no longer be designed around people making decisions at every step. Historically, operators compensated for variation throughout production. Today’s automated factories require processes that are simplified, optimized and standardized before automation is introduced. That fundamentally changes how engineers think about process design.

As AI becomes more integrated into manufacturing, engineers will spend less time reacting to problems and more time designing systems that prevent problems before they occur. AI-driven predictive manufacturing, automated visual inspection, advanced analytics and digital manufacturing systems are changing engineering from reactive troubleshooting to proactive optimization. Engineers need to become comfortable working alongside intelligent systems rather than simply operating equipment.

MD: What practical steps can manufacturers take to upskill existing engineering teams and integrate AI tools without disrupting production, safety or institutional knowledge?

JK: The first thing I’d say is that AI implementation should not start with technology. It should start with the manufacturing process. Before introducing automation or AI, manufacturers should establish repeatable workflows and define clear operational objectives.

The second priority is investing in the people who already understand your operations. Experienced engineers and technicians have decades of institutional knowledge that cannot simply be replaced with new software. Instead, companies should help those employees build new capabilities through continuous training in automation, AI, advanced analytics and digital manufacturing while encouraging collaboration between experienced personnel and newer engineers. That's how you preserve operational knowledge while expanding technical capabilities.

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Finally, workforce development cannot stop at your own organization. Manufacturers need to work closely with trade schools, universities, and technical colleges to help shape the next generation of talent.

Manufacturing is evolving much faster than many educational programs were originally built around. If industry does not actively help define the skills and capabilities future engineers will need, we will continue facing workforce shortages that slow innovation. AI is transforming manufacturing but long-term success will still come down to having people who know how to apply those technologies effectively.

Have a question, comment or idea? Get in touch with the Machine Design editors at [email protected].

Editor’s Note: Machine Design’s WISE (Workers in Science and Engineering) hub compiles our coverage of workplace issues affecting the engineering field, in addition to contributions from equity seeking groups and subject matter experts within various subdisciplines.