Manufacturing and utilities workers face growing skills gap as automation and AI reshape industrial jobs. Discover how employers can respond.
By Karen Gavenda, Director of Revenue Operations at RS Americas
As automation and AI become more embedded in industrial operations, workforce readiness needs to advance alongside technology adoption. A new RS study surveyed US manufacturing and utilities employees across the nation to better understand how automation, AI and other Industry 4.0 technologies are impacting workplace safety expectations, skills development, and employee confidence.
The report points to gaps in technical troubleshooting, equipment skills, digital literacy and AI knowledge, while cost, time and access can make training harder to complete.
For industrial employers, the priority is therefore not simply introducing new technology, but ensuring workers have the practical skills and confidence to use evolving tools effectively and safely.
“Automation and AI are transforming industries at pace, but our studies show that employees don’t yet feel fully equipped to keep up. As roles evolve, businesses need to ensure their employees have access to practical, accessible training embedded in their day-to-day.”
—Karen Gavenda, Director of Revenue Operations at RS Americas
Automation has long played a role in industrial environments, but Industry 4.0 is expanding the range of capabilities employees need. Machine-assisted tools, robotics, data analytics and digital systems are changing what workers are expected to understand and manage as part of their day-to-day roles.
Technical troubleshooting was the most commonly identified skills gap among employees (33%). Mechanical or equipment-based skills followed at 31%, with data analytics and digital literacy at 29%. Communication and team leadership were identified by 27%, while 24% cited knowledge of AI or machine-assisted tools.
Preparing a workforce for new technology isn’t solely a question of AI literacy. Employees report gaps spanning traditional equipment expertise, technical problem-solving, digital capabilities, and interpersonal skills.
For manufacturing and utility workers, technological change is also happening against a backdrop of changing workplace expectations.
Nearly six in ten (59%) employees say workplace safety expectations have become stricter over the past 12 months due to automation and productivity pressures.
The implications are particularly important in workplaces where safety, equipment knowledge and technical understanding intersect. As new technologies become embedded into operations, employees need the skills to understand, operate, and work safely alongside those systems.
This makes training part of a broader operational question, rather than simply an occasional exercise completed when a new technology is introduced. As systems continue to evolve, organizations need a workforce capable of evolving with them.
AI presents one of the clearest examples of the gap between technological adoption and workforce confidence.
According to the research, 62% of manufacturing and utilities employees are already using AI in their roles. Yet only 18% feel confident using it at work, while just 19% describe AI in the workplace as a “good thing”. In addition, three-quarters (75%) see AI as a threat to their job.
Concerns about AI also appear persistent. In RS’ previous research in 2025, 78% of manufacturing employees regarded AI as a threat to their job. The latest figures remain similar a year later at 75%.
For industrial employers, that makes confidence an important consideration alongside technical capability. Workers need to understand not only how emerging tools operate, but how they relate to the work employees already perform.
Even where workers recognize the need to develop new skills, practical barriers can make training difficult to access.
Among employees surveyed, 18% identified cost as a barrier to upskilling. A further 17% cited having no time during working hours, while 17% said training is not available in their area or during their shift. Lack of manager support was also cited by 14%.
These results point to an important consideration for organizations. Upskilling opportunities need to reflect the practical realities of workers’ roles and working patterns. That means looking beyond whether training already exists and considering if employees can realistically access it.
Employees are more likely to adopt new technologies when learning is embedded into their daily work rather than treated as an extra task. Training should focus on real-world applications that help employees immediately see how AI can make them more productive, efficient, and effective in their roles. But confidence is not just built through formal training; it’s better solidified by experience. Employees are more likely to trust AI when they see it helping solve real problems in their day-to-day work, whether that’s troubleshooting equipment, finding information faster, or improving productivity.
Some of the most valuable AI use cases come directly from employees solving everyday challenges. Organizations should create clear guardrails that allow employees to experiment safely while establishing a process to evaluate and share successful use cases across the business. When employees can learn from one another and scale proven ideas, adoption accelerates.
Training should go beyond technical instruction and help employees understand how new technologies relate to their role, safety, and long-term development. Confidence is often the biggest barrier to adoption, and it grows naturally through education, practice, and positive experiences.
As more teams work with automated systems, digital tools, and cross-functional processes, soft skills such as communication and team leadership remain essential. Organizations that invest in both technical and human skills will be best positioned to adapt and grow. Managers play a critical role in their workforce’s transformation. Companies who equip their managers with the knowledge and confidence to support their employees through new processes will likely see better adoption.
Among the manufacturing and utilities employees surveyed, technical troubleshooting was the most commonly identified skills gap at 33%, followed by mechanical or equipment-based skills at 31%, data analytics and digital literacy at 29%, soft skills at 27% and AI or machine-assisted tools knowledge at 24%.
Employees identified several practical barriers. These include the cost of training, availability of training, lack of time during working hours, training not being available for particular locations or shifts, and lack of manager support.
While 62% of manufacturing and utilities employees surveyed already use AI in their roles, only 18% feel confident using it at work and 75% see it as a threat to their job. The findings suggest that adoption and confidence are not necessarily progressing at the same pace.
The organizations that will benefit most from Industry 4.0 won’t necessarily be those that adopt technology the fastest. They’ll be the ones that help their people adapt alongside it. By investing in applied learning, responsible experimentation, and confidence building, manufacturers can build a workforce that is prepared to continuously adapt.
RS recognizes that successful transformation goes beyond implementing technology. Through technical resources, training insights, industry expertise, and partnerships across the industrial ecosystem, we help organizations prepare to confidently adopt new tools and ways of working.
About the Author:
Karen Gavenda is the Director of Revenue Operations at RS Americas, where she leads sales enablement, insights and analytics, and sales technology stack ownership to drive scalable growth. She plays a key role in advancing the company’s use of AI to improve pricing, quoting, and operational efficiency, focusing on practical, high-impact applications that enhance decision-making and customer experience. Karen earned a B.S. and MBA from Penn State University and holds several certifications, including Project Management (PMP) and Salesforce.com Administrator.
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