Expanding the skilled manufacturing workforce with AI
AI could help US manufacturers address one of their most pressing workforce challenges: finding and developing more technicians
US manufacturing is increasingly defined by sophisticated, high-precision products that rely on advanced and interconnected production technologies, automation, and capital-intensive processes.1 In this new paradigm, multiskilled technicians with expertise across multiple technical domains, such as mechanical, electrical, and control systems, have become crucial enablers of operational success.
Although they represent a relatively small share of a manufacturer’s workforce,2 technicians often play an outsized role in keeping advanced production systems running, supporting the adoption of new processes and technologies, driving efficiency and product quality, and enabling productivity across the factory floor. Demand for these technicians has grown substantially faster than demand for production occupations.3
At the same time, applicant shortages and skills gaps have made it difficult for manufacturers to secure the technician workforce they need (figure 1).4 These workforce challenges can ripple through operations, increasing downtime and constraining production capacity, operational performance, and growth. Artificial intelligence could create a new opportunity to address these challenges. By embedding expertise directly into daily work, AI can help workers, including those with less experience and others transitioning from adjacent industries, develop and apply knowledge and skills in manufacturing roles, thereby broadening the technician talent pool.
To explore this opportunity, Deloitte and The Manufacturing Institute embarked on a study in May 2026 to map the technician ecosystem across manufacturing and adjacent industries and examine how generative and agentic AI (which we collectively refer to as “AI”) could help reshape technician roles to improve business outcomes and enhance their appeal to current and future workers.
Mapping the technician ecosystem in manufacturing and adjacent industries
The manufacturing technician workforce (which we refer to as “manufacturing technicians”) encompasses a range of occupations that we grouped into three primary categories based on typical preparation pathways,5 work activities, knowledge, and transferable skills (see methodology).6
- Advanced manufacturing technicians support the design, implementation, testing, calibration, and optimization of manufacturing equipment, systems, and processes.
- Maintenance and repair technicians keep advanced equipment operating through preventative maintenance, troubleshooting, and repair.
- Quality and laboratory technicians ensure product and process quality through testing, analysis, and validation.
These categories enable a better understanding of the distinct technician roles in a manufacturing environment while also revealing the common capabilities they share with technicians in similar occupations primarily employed outside manufacturing (whom we refer to as “adjacent-industry technicians”) and could represent a future source of manufacturing talent (figure 2).7
More than two-thirds of adjacent-industry technicians are employed in construction, retail trade, other services (except public administration), wholesale trade, and government (see, "Adjacent-industry technicians represent a potential talent pool across many industries").8
Adjacent-industry technicians represent a potential talent pool across many industries
Adjacent-industry technicians are employed across a diverse set of industries and could provide a valuable talent pool for manufacturers, depending on regional industry composition and economic conditions. For example, if employment opportunities for automotive service technicians were to decline in a particular region due to shifts in the local economy, manufacturers could create opportunities for them to transition into manufacturing roles, benefiting both employers and workers.
Not all adjacent-industry technicians will be equally well positioned to transition into manufacturing, and when possible, companies can prioritize occupations that may require less reskilling. However, the similarity in work activities, capabilities, and preparation pathways—with workers commonly entering these roles through technical certificates, apprenticeships, or associate degree programs—provides a strong foundation for these workers to transition into manufacturing technician roles.
Adjacent-industry technicians share a broad foundation of knowledge and transferable skills with manufacturing technicians, but emphasize different domain knowledge, such as construction, biology, and law and government, and skills such as social perceptiveness and service orientation (figure 4).9 These shared capabilities can create an opportunity for AI to help bridge domain knowledge and skill gaps and support greater cross-category and cross-industry mobility, which could help manufacturers expand the technician talent pool.
Strong employment growth is another commonality between manufacturing technicians and their adjacent-industry counterparts (figure 5). Between 2010 and 2025, employment among manufacturing and adjacent-industry technicians grew substantially faster than production occupations.10 Further, during the most recent period, 2020 to 2025, this gap widened, and based on current industry trends, the growing need for technicians is anticipated to continue.
Deloitte and The Manufacturing Institute estimate that, between 2025 and 2030, manufacturing technician employment could grow six times faster than employment in production occupations, whereas adjacent-industry technician employment could grow five times faster.11
“Between 2025 and 2030, manufacturing technician employment could grow six times faster than employment in production occupations.”
As the demand for technicians continues to grow across industries, competition for a limited talent pool may intensify, especially if ongoing skills and applicant shortages aren’t addressed. More than 4.5 million workers were employed in manufacturing technician and adjacent-industry technician occupations across all industries in 2025.12 According to an analysis of Bureau of Labor Statistics data, employers may need to fill 2.3 million job openings across these occupations between 2025 and 2030, due to both employment growth and replacement needs from retirements, other labor force exits, and occupational transfers.13 Meeting this workforce demand will likely require a new approach that broadens and strengthens the talent pool by lowering barriers to entry, accelerating technician development and mobility, elevating the ability to perform increasingly complex work, and enhancing technician attraction and retention.
How generative and agentic AI could help meet the increasing need for technicians
AI has the potential to help manufacturers meet the growing demand for technicians while creating business value.
Lower barriers to entry and accelerate technician development: By embedding learning, guidance, expertise, and intelligent action directly into workflows, AI can help workers develop, augment, and apply new knowledge and skills in the flow of work while automating routine decisions and tasks (figure 6).14 This includes the shared and unique transferable skills that define manufacturing and adjacent-industry technicians, such as troubleshooting, equipment maintenance, and quality control analysis, as well as knowledge of production processes and electronics. As a result, barriers to entry can be reduced for less-experienced workers, technicians transitioning from adjacent industries, or manufacturing technicians moving between categories, enabling them to contribute more quickly. For example, a new manufacturing maintenance technician could capture a video of an unfamiliar equipment problem for AI to analyze and recommend troubleshooting steps within minutes, rather than waiting for remote expert support.15
Enable increasingly complex work: AI can also enable experienced technicians to take on increasingly complex and higher-value responsibilities. For instance, an advanced manufacturing technician may spend less time manually interpreting inspection data and test results and more time leading process improvement efforts.
Enhance talent attraction and retention: As AI becomes embedded in technician workflows, the opportunity extends beyond improving operational performance to fundamentally reshaping the technician experience—from onboarding and career development to mobility, autonomy, and job satisfaction (figure 7). These benefits could have far-reaching impacts on a company’s ability to attract and retain the technician workforce it needs. For example, Generation Z and millennial workers surveyed value learning and development, on-the-job experience and guidance, and opportunities to acquire new skills16—areas where AI can serve as a powerful enabler and potentially boost the appeal of technician roles (see, “Empowering manufacturing workers to do more, higher-value work with AI”). This is particularly important given that Gen Z and millennials now comprise the majority of the US workforce.17
Empowering manufacturing workers to do more, higher-value work with AI
AI could expand the capabilities of many manufacturing workers, including those in production occupations, and enable them to do more within and beyond their existing roles. For instance, a semiconductor processing technician investigating a yield issue could use AI to analyze manufacturing execution system, statistical process control, and equipment data, identify the root cause, and recommend corrective actions before escalating the issue to a maintenance technician or engineer. Similarly, a machinist experiencing recurring quality issues could use AI to analyze machine performance data and computer numerical control parameters, identify the likely issue, and recommend process adjustments.
AI could also allow manufacturing technicians to take on more advanced responsibilities. For example, a maintenance technician troubleshooting a packaging line could use AI to analyze programmable logic controller code, human-machine interface alarms, and equipment history; recommend programming changes; and simulate potential impacts before involving a controls engineer.
In both cases, AI can embed expertise directly into workflows, helping workers solve more complex problems, contribute at a higher level, and build skills in the flow of work. Especially for employees seeking growth, AI could create more engaging jobs and clearer pathways from technician roles to higher-skilled positions, while generating business value through improved responsiveness, reduced bottlenecks, and a more capable and flexible workforce.
Preparing now for the AI-enabled technician workforce
Manufacturers and their ecosystem partners should focus on answering the question: “What do we need to build now to help ensure we have the AI-enabled technician workforce we need in the next three to five years?” The answer should include a workforce plan that builds on the complementary strengths of humans and AI to improve the productivity of existing technicians, accelerate technician development, and expand access to internal and adjacent talent across the three technician categories. Manufacturers should consider five actions when building this plan.
1. Develop a prioritized AI implementation road map
Manufacturers should first develop a prioritized road map identifying where AI can generate the greatest value within the company, while considering the key factors required to achieve scale: cost, data architecture, tech stack modernization, governance, trust, and workflow transformation.18
2. Reimagine and redesign technician workflows around human–AI collaboration
Rather than layering AI onto existing systems and processes, companies should reimagine and redesign technician workflows to leverage the unique strengths of humans and AI.19 This can also help understaffed technician teams accomplish more today while longer-term hiring, training, and career pathways continue to evolve.20 In fact, according to a recent Deloitte study, companies that take this approach are twice as likely to outperform on AI-related return-on-investment expectations as those that don’t.21 Effective redesign should engage current technicians as key stakeholders and balance human outcomes with business benefits at both the task and organizational levels.22
3. Define AI-enabled technician skills and redesign training programs and career pathways
A recent Deloitte survey of company leaders found that companies are increasingly aligning and deploying workers based on tasks, skills, and outcomes rather than traditional jobs and positions.23Accordingly, manufacturers should consider defining the new skill sets AI-enabled technicians will need, building on the broad foundation of shared capabilities across the three technician categories. These skill sets include foundational AI literacy, such as prompt design, output interpretation, and validation, alongside higher-order skills such as oversight, orchestration, exception handling, and strategic and ethical decision-making.24 As AI becomes increasingly embedded in manufacturing systems, the need to validate AI-generated recommendations and safely oversee and work alongside more autonomous systems—including intelligent collaborative robots and other forms of physical AI—could further increase demand for technicians with new and more advanced skills.25
Because AI can embed knowledge and expertise directly into workflows, training programs should be designed around experiential learning based on the redesigned workflows technicians will use. AI-enabled coaching, diagnostics, and work instructions can be leveraged to support learning in the flow of work. Career pathways can be redesigned to leverage shared transferable skills and knowledge, while AI can help workers move from production roles or adjacent industries into manufacturing technician jobs, move between technician categories, and advance from technician jobs into higher-skilled technical or supervisory roles within the industry.
4. Move beyond traditional change management by embedding learning and adaptation into technician work
Creating an adaptive workforce is increasingly important: Eighty-five percent of respondents to Deloitte’s 2026 Global Human Capital Trends survey say it is important for organizations and workers to develop the ability to adapt at the speed required by today’s world, but only 7% say they are making great progress toward doing so.26 Manufacturers can move beyond traditional change management by embedding adaptation, experimentation, learning, and growth into the daily technician work experience.
Successfully integrating AI into technician roles should include building trust, encouraging experimentation, and helping workers understand how AI can augment their expertise. This includes establishing clear decision rights for when AI should recommend, act, or require human validation, while training supervisors to coach workers through adoption. It also involves being mindful of “cultural debt”—the gap that can emerge when technology adoption outpaces changes in culture, behavior, and ways of working.27 Implemented well, AI can reduce frustration, expand access to expertise, support career mobility, and give technicians greater autonomy in solving complex problems on the production floor. Together, these efforts can enable workers to continuously evolve, help avoid stalled transformations, and potentially turn technician workforce flexibility and adaptability into a competitive advantage.28
5. Strengthen partnerships to modernize technician education and workforce development
Education and workforce systems should evolve alongside technician roles. Manufacturers, educators, workforce organizations, and equipment and technology providers should establish and maintain close partnerships to help ensure that training programs reflect changing technician workflows and skill requirements (see “A workforce ecosystem partnership to accelerate AI skills through FAME”).29
Technician curriculum should be updated to include AI literacy, AI-enabled diagnostics, digital and audiovisual work instructions, data interpretation and validation, and human–AI collaboration. It should also shift toward an even stronger emphasis on transferable skills that will be important across technician categories in AI-enabled roles, such as complex problem-solving, judgment, and decision-making. These capabilities should be embedded in hands-on exercises that use actual manufacturing equipment, systems, and scenarios. Programs should also create stackable credentials and skills-based pathways that support movement between adjacent industries and among manufacturing occupations.
A workforce ecosystem partnership to accelerate AI skills through FAME
In April 2026, Google.org announced a $10 million commitment to The Manufacturing Institute to help develop AI skills for the manufacturing workforce.30 The funding will support the expansion of The Manufacturing Institute’s Federation for Advanced Manufacturing Education (FAME) program, a nationally recognized earn-and-learn model that combines classroom instruction with paid work experience. Funding will support the integration of AI into technician training programs, helping students and incumbent workers learn how to use AI-enabled tools alongside advanced manufacturing systems and equipment. The initiative also aims to strengthen pathways into manufacturing careers by aligning education, industry, and workforce development efforts around emerging skill needs. As manufacturers increasingly adopt AI, such collaborations may play an important role in preparing workers for technology-enabled manufacturing and adjacent-industry careers and building the skilled talent pipelines needed for future growth.
Together, these elements can help usher in a new era of AI-enabled technicians that could turn one of manufacturers’ greatest workforce challenges into a strategic advantage. Companies that act now to redesign work and prepare their technician workforce may be best positioned to create new value, accelerate innovation, and sustain a competitive edge.
Methodology
Data source: The methodology is based on information from the O*NET 30.3 database of the US Department of Labor, Employment and Training Administration (USDOL/ETA), used under the CC BY 4.0 license. O*NET is a trademark of USDOL/ETA. Deloitte has modified some of this information through analysis and synthesis, as described below. USDOL/ETA has not approved, endorsed, or tested these modifications. The data was accessed between May 19, 2026, and June 4, 2026.
Manufacturing technician occupations were identified using O*NET OnLine data through a multistep process. Starting with a broad cross-industry universe of Job Zone 3 occupations, we excluded skilled production roles (e.g., machinists, CNC operators, and semiconductor processing technicians) to focus on technicians who primarily support advanced manufacturing operations rather than directly perform production work. We then used the O*NET “Find Occupations Related to Multiple Detailed Work Activities” tool, beginning with high-employment, important manufacturing technician occupations such as industrial machinery mechanics and industrial engineering technologists and Technicians, to identify occupations with similar work structures based on shared and similar activities. The final list was limited to occupations primarily employed in manufacturing and validated using O*NET knowledge and transferable skills profiles. Based on the analysis, the occupations that make up each manufacturing technician category are shown below.
- Advanced manufacturing technicians: Industrial engineering technologists and technicians (17-3026), electrical and electronic engineering technologists and technicians (17-3023), mechanical engineering technologists and technicians (17-3027), electro-mechanical and mechatronics technologists and technicians (17-3024), aerospace engineering and operations technologists and technicians (17-3021), calibration technologists and technicians (17-3028)
- Maintenance and repair technicians: Industrial machinery mechanics (49-9041), maintenance and repair workers, general (49-9071), maintenance workers, machinery (49-9043), electrical and electronics repairers of commercial and industrial equipment (49-2094)
- Quality and laboratory technicians: Life, physical, and social science technicians, all other (19-4099; includes quality control analysts [19-4099.01]), chemical technicians (19-4031), food science technicians (19-4013)
To identify the typical activities of each manufacturing technician category, we analyzed O*NET detailed work activities (DWAs). Because O*NET does not publish occupation-level DWA importance scores but does publish DWAs in ranked order, from most important to least important within each occupation, the rankings were normalized and averaged across occupations. Typical activities were derived from the highest-priority DWAs common to occupations within each technician category and are presented in approximate order of importance. The typical activities for each manufacturing technician category are shown below.
Advanced manufacturing technicians
- Review technical requirements, estimate project needs, and develop work plans for engineering and production activities
- Test, inspect, and validate products, equipment, and integrated systems to ensure functionality, performance, and quality
- Analyze and interpret test results, evaluate designs and specifications, and develop technical methods and process improvements
- Install, assemble, maintain, and calibrate instrumentation, electronic equipment, and production systems
- Support the design, material selection, and implementation of industrial equipment and manufacturing systems
Maintenance and repair technicians
- Inspect, test, and diagnose equipment to identify damage, defects, wear, and performance issues
- Repair, replace, adjust, and reassemble mechanical and electrical components to restore equipment functionality
- Install replacement parts, equipment, and electrical systems to support reliable operations
- Perform preventive maintenance activities, including lubrication, cleaning, and routine equipment upkeep
- Maintain repair records and collaborate with coworkers to resolve equipment and operational problems
Quality and laboratory technicians
- Test and evaluate the quality of materials, products, and production outputs
- Analyze chemical compounds, laboratory samples, and operational data to identify issues and support decision-making
- Interpret research, testing, and operational results to assess product and process performance
- Maintain, calibrate, and prepare laboratory and technical equipment for testing and analysis
- Record, validate, and manage testing and operational data to ensure accuracy and traceability
Adjacent-industry technician occupations were identified using O*NET DWA similarity analysis, as described above, beginning with occupations in the manufacturing technician list. Occupations with shared and similar DWAs totaling a substantial portion of the base occupation’s DWA count were selected and validated through qualitative review of DWAs, knowledge, and transferable skills. Based on the analysis, the occupations similar to each manufacturing technician category are shown below:
- Similar to advanced manufacturing technicians: Photonics technicians (17-3029.08), architectural and civil drafters (17-3011)
- Similar to maintenance and repair technicians: Aircraft mechanics and service technicians (49-3011), automotive service technicians and mechanics (49-3023), computer, automated teller, and office machine repairers (49-2011), control and valve installers and repairers, except mechanical door (49-9012), farm equipment mechanics and service technicians (49-3041), medical equipment repairers (49-9062), mobile heavy equipment mechanics, except engines (49-3042), radio, cellular, and tower equipment installers and repairers (49-2021), telecommunications equipment installers and repairers, except line installers (49-2022), electric motor, power tool, and related repairers (49-2092), stationary engineers and boiler operators (51-8021), heating, air conditioning, and refrigeration mechanics and installers (49-9021)
- Similar to quality and laboratory technicians: Agricultural technicians (19-4012), forensic science technicians (19-4092)
To identify the defining knowledge and transferable skill characteristics of each technician category, O*NET knowledge and transferable skill importance scores for the occupations that make up each technician category were averaged after being downloaded from the O*NET 30.3 database.
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The authors would like to thank Anuradha Joshi for her key contributions to technician occupation analysis, analytical leadership, and framing, and Vrinda Garg for important contributions to research, data analysis, and writing support.
Deloitte advisory board:
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NAM advisory board:
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The authors would like to acknowledge the support of Clayton Wilkerson for orchestrating resources related to the report; Kimberly Prauda and Neelu Rajput for driving the marketing strategy and related assets to bring the story to life; Mariel Balaban for her leadership in public relations; and Rithu Thomas, Aparna Prusty, Harry Wedel, and Anu Augustine from the Deloitte Insights team for supporting the report’s publication.
Editorial (including production and copyediting): Rithu Thomas, Aparna Prusty, and Anu Augustine
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