Artificial intelligence (AI) is being adopted faster than any technology in modern history. Within two years of widespread availability, 39.4 percent of American adults were using generative AI tools, a pace that outstripped both the personal computer and the Internet at comparable stages (Deming, 2024). Yet the defense industrial base (DIB), the network of manufacturers that produces everything from fighter jet components to submarine hull plates, has no structured workforce training program to match this adoption curve. The technology is arriving. The people who need to use it are not ready.
That disconnect is the central finding of a 32-source analysis conducted by the Center for Regional Economic Competitiveness (CREC) under a cooperative agreement with the Department of Defense (DoD) Manufacturing Technology (ManTech) Program Office. The research draws on peer-reviewed studies, policy documents, industry analyses, and workforce data to answer a straightforward question: what does AI actually mean for the defense industrial base over the next five years? The answer is more nuanced, and more urgent, than most of the conversation around AI in defense would suggest.
The Workforce Problem Is the AI Problem
The bottleneck in defense manufacturing is not access to AI. It is the availability of workers who can use it. Manufacturing labor productivity has been nearly flat since the mid-2000s despite decades of automation investment. The missing ingredient is not more technology but more skilled humans: process engineers, electricians, robotics specialists, maintenance technicians, and quality inspectors. Deloitte projects 3.8 million net new manufacturing jobs by 2033. The question is not whether humans are needed but whether enough of them will be available with the right skills.
AI tools produce a median 25 percent productivity improvement when used effectively (Sadun, 2025), but that word effectively carries enormous weight. Without structured training and integration support, adoption produces uneven results and worker frustration rather than sustained gains. The Computer Numerical Control (CNC) revolution of the 1970s and 1980s provides the relevant precedent. Plants that adopted CNC were 75 percent more likely to use problem-solving teams, twice as likely to offer technical training, and held regular shop floor meetings at substantially higher rates than non-adopters (Deming, 2024). Automation changed the nature of skilled work but did not eliminate the need for it. AI will be no different.
AI Is Hollowing Out the Talent Pipeline
One of the most consequential findings in the research is a dynamic that almost no one in defense manufacturing is discussing: AI is absorbing the routine tasks that junior workers need to develop into senior experts. Anthropic’s own internal study found that while engineer productivity gains grew from approximately 20 percent to 50 percent year over year, senior engineers reported growing concern about skill atrophy among junior staff. Junior engineers stopped asking questions of mentors because AI answered faster (Orrell, 2025).
In defense manufacturing, this pattern is particularly dangerous. Senior machinists, welders, quality inspectors, and maintenance technicians acquire their expertise through years of hands-on practice that begins with routine work. If AI absorbs those tasks before workers have the chance to learn from them, organizations gain short-term productivity at the cost of a long-term senior talent shortage. The pipeline narrows even as current output rises.
The Technology Is Not Ready for What Defense Demands
Enthusiasm for AI in manufacturing often outpaces what the technology can reliably deliver. AI-powered Automated Optical Inspection (AOI) systems currently achieve 60 to 70 percent accuracy, a rate that is insufficient for defense applications where tolerances are measured in thousandths of an inch. On the agentic AI front, the best-performing architecture for manufacturing decision support, a Retrieval-Augmented Generation (RAG) system, achieved 77.89 percent accuracy compared to 52.37 percent for a baseline Large Language Model (LLM). Better, but not defense-grade.
The implication is straightforward: human-in-the-loop oversight will remain essential in defense manufacturing for the foreseeable future. Planning for AI in this sector means planning for augmentation, not autonomy. Workforce development strategies that assume AI will replace human judgment are building on a foundation that does not yet exist.
Implementation Matters More Than the Technology
The same AI technology can produce opposite outcomes depending on how it is deployed. Where AI is implemented primarily for surveillance and performance monitoring, workers report reduced autonomy, lower morale, and increased turnover. Where workers are involved early in design and deployment, 96 percent reported increased job satisfaction when freed from monotonous tasks to focus on higher-level work. Collaborative robots (cobots) can increase worker productivity by up to 85 percent when paired with proper training (International Federation of Robotics). For defense manufacturers, the management decision matters more than the technology decision. Procurement of AI systems without parallel investment in organizational change is a misallocation.
The System Is Not Built for This Speed
AI capabilities evolve on timelines measured in months. Defense acquisition spans years. Secretary of Defense Pete Hegseth has publicly stated that the current acquisition system is archaic and must shift from decade-long development cycles to rapid iteration. Small and mid-size defense suppliers cannot wait for multi-year contract modifications to adopt AI tools that may be obsolete by the time approval comes through.
Meanwhile, the data needed to make smarter workforce investments now exists. CREC and RTI International, under the ManTech cooperative agreement, produced the first national dataset measuring the Critical Technology Area (CTA) workforce in aerospace and defense manufacturing, delivered in December 2025. This dataset maps supply and demand for skills in AI, autonomous systems, additive manufacturing, and other priority technology areas across the top 30 aerospace manufacturers. The data confirms significant gaps between CTA skill supply and employer demand. Yet no federal workforce investment program currently uses it. The approximately $6 billion the federal government spends annually on workforce development through the Workforce Innovation and Opportunity Act (WIOA), the Perkins Act, and grants from the Economic Development Administration (EDA) and the National Institute of Standards and Technology (NIST) is allocated without reference to defense-specific workforce intelligence.
The Untapped Pipeline
Approximately 200,000 service members transition out of the military each year with technical training, leadership experience, security clearances, and familiarity with defense systems and culture. They represent the single most qualified talent pipeline available for defense manufacturing. Yet persistent mismatches between military occupational specialties and civilian job classifications, inadequate credentialing bridges, and employer unfamiliarity with military skill sets prevent this pipeline from flowing at scale. Community colleges, which should function as the primary bridge between military training and civilian manufacturing careers, vary widely in their alignment with local defense labor markets. The best performers, such as Dallas College, Wake Tech, and Miami Dade, maintain active employer relationships and anticipate skill demand. Most do not (Fuller, HBS).
AI is a necessary but insufficient condition for a competitive defense industrial base. The binding constraints are workforce readiness, organizational capacity to integrate new tools, acquisition pathways that match the pace of technology change, and analytical infrastructure that connects federal investment to measurable outcomes. Technology alone will not resolve any of them.
Deliberate action looks like this: structured AI training for the existing manufacturing workforce. Redesigned development pathways that ensure junior workers still build expertise even as AI absorbs routine tasks. Acquisition reform that matches technology timelines. And federal investment guided by actual workforce data rather than assumptions about where the gaps are. The data exists. The talent pipeline exists. The question is whether the institutions responsible for the defense industrial base will use them before the window closes.
Sources
Deming, D. (2024). “The Rapid Adoption of Generative AI.” NBER Working Paper.
Deming, D. (2024). “How Computers Turned Machinists Into Problem-Solvers.”
Fuller, J. (HBS). “Why the Skills Gap Persists.” Harvard Business School.
Linder, B. (2026). “AI Won’t Save Manufacturing.” Forbes.
Orrell, B. (2025). “What Anthropic’s Internal Study Suggests About the Future of Work.” American Enterprise Institute.
RTI International / CREC / ManTech (2025). “Measuring the Size and Dynamics of the CTA Workforce.”
Sadun, R. (2025). “Reskilling the Workforce With AI.” Harvard Business School.
“Agentic AI for Smart Manufacturing” (2025). SSRN.
“AI and Job Quality: Insights from Frontline Workers” (2024). Partnership on AI / SSRN.