The country’s workforce faces numerous challenges in responding to the opportunities and threats posed by artificial intelligence (AI), ranging from minimizing displacement to cultivating a labor pool with the foundational skills, diversity of thought, and technical agility to drive and accelerate the pace of innovation. For policymakers, these challenges require a coordinated response that seeks to minimize harm to workers in jobs disrupted by the technology while maximizing benefits, and does so across multiple time horizons to address both immediate and future concerns. It also must take into account that there are vast unknowns associated with this new technology, including the pace of AI advancement, the limits of its capabilities, the emergence of new applications, the scalability of AI-driven systems, and the potential breakthroughs or constraints in computing power and energy efficiency—all of which will directly influence how jobs evolve, which skills will be in demand, and how workers can adapt in a rapidly shifting labor market.
Last week, President Trump issued an executive order entitled “Advancing Artificial Intelligence Education for American Youth,” reflecting a recognition that national coordination is needed to prepare students and workers for an AI-driven economy. While President Biden took a more proactive approach to regulating AI, the Trump administration has signalled its intent to advance the technology as a means of keeping pace with global competitors, particularly China. With fewer regulatory constraints, the rapid deployment of AI is likely to reshape the labor market faster than anticipated, a shift that underscores the urgency of preparing workers for a turbulent, more automated, and digitally driven economy.
Education and workforce leaders will need to accelerate their responses. Critically, the field will have to do so at a time when staff and resource cuts at key federal agencies under the Trump administration and the Department of Government Efficiency (DOGE) threaten the government’s ability to implement a cohesive AI strategy, and when the federal turn against diversity, equity, inclusion, and accessibility (DEIA) could complicate efforts to ensure that AI education and training resources will reach the communities already at risk of falling behind in the future labor market.
Disruption, Adaptation, and the Role of Human Expertise
The rapid integration of AI into the workforce presents significant challenges, particularly around job displacement, skills development, and labor market polarization. AI is revolutionizing industries across the economy, with companies integrating intelligent systems that enhance efficiency, precision, and decision-making. A growing trend in the labor market shows AI augmenting the productivity of high-performing workers (that is, those who consistently perform above expectations for their role) by boosting their efficiency, while threatening to displace lower-performing workers (at all levels) who struggle to upskill in response to technological change. This dynamic is increasingly polarizing roles within occupations, widening the gap between those whose work is augmented by AI and those whose tasks may be replaced by it.
This pattern is playing out across the workforce, reshaping how work is done and who is best positioned to succeed. In manufacturing, AI-driven advancements are going beyond traditional automation, enabling AI to manage intricate production tasks, improve quality control, and optimize operational processes. AI-powered systems can now detect defects better than humans, predict equipment failures through real-time performance analysis, and streamline supply chain management to minimize waste. That said, AI still requires human oversight—for example, overly accurate defect detection can slow production by flagging parts that are still functional, requiring humans to recalibrate thresholds to balance precision with practical efficiency.
AI still requires human oversight—for example, overly accurate defect detection can slow production by flagging parts that are still functional.
Another field AI is transforming is coding. Beginning in the early 2000s, coding emerged as the twenty-first century equivalent of a manufacturing line job, providing a pathway to high-quality employment for workers with sub-baccalaureate credentials. These roles were highly appealing to millennial workers seeking autonomy, flexibility, and upward mobility in an increasingly digital economy. However, just as automation reshaped traditional manufacturing, AI-driven platforms are rapidly transforming software development by automating routine coding tasks, debugging, and even generating complex scripts with minimal human input, aligning with the trend of AI amplifying the contributions of high-performing workers while potentially displacing lower-performing workers who are less effective in adapting to innovation cycles.
Unlike most other technology advancements to date, AI-driven automation not only is threatening jobs for workers who lack significant education or training, but also is encroaching on complex job functions that traditionally require years of advanced education and specialized training. For example, in radiology, AI-powered imaging analysis can now detect anomalies in X-rays, MRIs, and CT scans with a level of precision that rivals or even surpasses human radiologists (who require around thirteen years of post-high school training to become certified). Similarly, in accounting, AI and machine learning algorithms are revolutionizing financial analysis, detecting fraud, and even predicting future financial trends. These developments signal a fundamental shift in the labor market across industries: while AI is unlikely to render human labor entirely obsolete in any sector, it could erode traditional career pathways by automating entry-level and repetitive tasks, compressing job ladders, and reshaping the demand for human expertise in once-secure, high-status fields.
As AI continues to restructure the workforce, one skill that is likely to become increasingly indispensable is critical thinking—both because AI is unlikely to fully replicate it and because it is essential for ensuring that AI is used effectively and ethically. While AI excels at processing vast amounts of data and identifying patterns, it lacks the ability to apply human judgment, question assumptions, and navigate ethical complexities. As automation takes over routine analytical tasks, the capacity to evaluate AI-generated insights, interpret nuanced contexts, and make informed decisions will be crucial. Without analytical reasoning skills, organizations risk blindly implementing AI-driven recommendations, reinforcing biases, or misapplying automation in ways that could have unintended consequences. In an AI-driven economy, those who can think critically—assessing, refining, and guiding AI outputs—are likely to be best positioned to thrive. By deliberately investing in education and training programs that cultivate human-AI complementarity skills—such as critical thinking, ethical reasoning, and domain expertise—policymakers could not only empower individuals to adapt, but also unlock broader economic potential, ensuring that AI becomes a force for shared prosperity rather than deepened inequality.
Policy Recommendations to Prepare the Current and Future Workforce for AI
In light of this new reality, policymakers seeking to meet the challenge of preparing the workforce for AI should focus on three main objectives.
First, legislation should have the goal of preparing educators to teach in a world where AI is a valid tool for students in all subject areas; just as the education system once debated—and ultimately embraced—the use of calculators, the Internet, and autocorrect, it must now recognize AI as a legitimate and inevitable tool for learning. This will mean retooling instruction and evaluation frameworks to help students appropriately integrate and leverage this technology as part of their work, just as they will be expected to do in the labor market. A mechanism to achieve this would be an Artificial Intelligence Training Fund for educators from PreK through higher education. Specifically, Congress could create a fund within the U.S. Department of Education and/or the U.S. Department of Labor to support the creation and delivery of the training curriculum as well as educator time to attend trainings. Through this dedicated source of funding, states could develop and implement a strategy to upskill educators and prepare them to integrate AI into classrooms, ensuring that students know how to use basic AI-driven technologies before entering the workforce.
Second, legislation should focus on developing education pathways to prepare the range of talent necessary to develop and utilize AI technology. This could include a special concentration on the associates degree level, which can increase diversity in the AI workforce—an important consideration since, as a study by the Georgetown University Center on Education and the Workforce points out, “students [in certificate and associate’s degree programs] are more diverse by race and ethnicity, socioeconomic status, and age than those in bachelor’s degree programs.” Participation in relevant pathways should be incentivized via mechanisms such as College Promise programs, prioritizing fields that develop expertise with large language models (LLMs) and other AI systems, such as computer literacy, information science, and basic statistical analysis. Given ongoing problems with AI hallucination, it will be critical to educate students not only on how best to prompt systems, but also how to critically evaluate the outputs.
Third, policy should be developed with a plan for helping the workforce learn how to integrate AI into existing occupations and mitigating the impact of large-scale displacement. As noted above, AI has the potential to disrupt not just the routine, repetitive, and rules-based tasks that have traditionally been vulnerable to automation, but also occupations that have been largely immune to disruption from technology in the past. It is possible that the speed and scale of disruption may outpace workers’ ability to reskill through traditional means, underscoring the need for strategic support that empowers workers to use AI to enhance their productivity and adaptability. To achieve this, policymakers should focus on approaches that engage employers early in the technology adoption process. As part of this strategy, they could establish formal roles for unions and other worker groups, and consider mechanisms that incentivize companies to train existing workers for roles using new automated technologies rather than displacing them.
To help today’s workers adapt and thrive in this quickly changing landscape, Congress could create a new Artificial Intelligence Worker Training Fund via formula dollars under the Workforce Innovation and Opportunity Act (WIOA) to upskill or reskill workers who face displacement due to AI. Operating similarly to A Stronger Workforce For America’s Critical Industry Skills Fund, the fund could be used to both develop and deliver training. To encourage broad participation, courses could be offered to workers who are currently employed (especially if their company would provide full pay while the worker is attending training). To drive completion rates, all participants could receive a bonus upon passing a final exam.
The fund could also be used to provide stipends to unemployed workers in training programs, and states could modify their unemployment insurance (UI) rules to ensure that unemployed attendees could receive the stipend in conjunction with their UI benefits, which are usually too limited to cover living expenses. Providing stipends in addition to easily accessible wraparound services would reduce paperwork and other administrative hurdles that tend to impede access to critical resources in practice, and allow attendees to meet important needs not covered by wraparound services.
To help provide national oversight of this endeavor, the fund could also be used to establish a task force at the Department of Labor’s Employment and Training Administration (ETA). The task force could mirror the education and workforce coordination function provided by National Institute for Standards and Technology for the development of a semiconductor talent pool, and would be charged with monitoring and managing the impact of AI on the workforce, collecting and disseminating best practices from states, and helping training programs adapt as AI evolves.
Looking Ahead
AI appears to be a transformative and disruptive force poised to operate on a scale the world hasn’t seen since the Internet was released into the public domain. Given its potential to overhaul knowledge and skill requirements across the labor market, however, it is clear that the United States needs a national policy approach to mitigate threats and maximize the potential benefit AI presents.
Tags: manufacturing, workforce training, AI, labor
Labor Market Disruption and Policy Readiness in the AI Era
The country’s workforce faces numerous challenges in responding to the opportunities and threats posed by artificial intelligence (AI), ranging from minimizing displacement to cultivating a labor pool with the foundational skills, diversity of thought, and technical agility to drive and accelerate the pace of innovation. For policymakers, these challenges require a coordinated response that seeks to minimize harm to workers in jobs disrupted by the technology while maximizing benefits, and does so across multiple time horizons to address both immediate and future concerns. It also must take into account that there are vast unknowns associated with this new technology, including the pace of AI advancement, the limits of its capabilities, the emergence of new applications, the scalability of AI-driven systems, and the potential breakthroughs or constraints in computing power and energy efficiency—all of which will directly influence how jobs evolve, which skills will be in demand, and how workers can adapt in a rapidly shifting labor market.
Last week, President Trump issued an executive order entitled “Advancing Artificial Intelligence Education for American Youth,” reflecting a recognition that national coordination is needed to prepare students and workers for an AI-driven economy. While President Biden took a more proactive approach to regulating AI, the Trump administration has signalled its intent to advance the technology as a means of keeping pace with global competitors, particularly China. With fewer regulatory constraints, the rapid deployment of AI is likely to reshape the labor market faster than anticipated, a shift that underscores the urgency of preparing workers for a turbulent, more automated, and digitally driven economy.
Education and workforce leaders will need to accelerate their responses. Critically, the field will have to do so at a time when staff and resource cuts at key federal agencies under the Trump administration and the Department of Government Efficiency (DOGE) threaten the government’s ability to implement a cohesive AI strategy, and when the federal turn against diversity, equity, inclusion, and accessibility (DEIA) could complicate efforts to ensure that AI education and training resources will reach the communities already at risk of falling behind in the future labor market.
Disruption, Adaptation, and the Role of Human Expertise
The rapid integration of AI into the workforce presents significant challenges, particularly around job displacement, skills development, and labor market polarization. AI is revolutionizing industries across the economy, with companies integrating intelligent systems that enhance efficiency, precision, and decision-making. A growing trend in the labor market shows AI augmenting the productivity of high-performing workers (that is, those who consistently perform above expectations for their role) by boosting their efficiency, while threatening to displace lower-performing workers (at all levels) who struggle to upskill in response to technological change. This dynamic is increasingly polarizing roles within occupations, widening the gap between those whose work is augmented by AI and those whose tasks may be replaced by it.
This pattern is playing out across the workforce, reshaping how work is done and who is best positioned to succeed. In manufacturing, AI-driven advancements are going beyond traditional automation, enabling AI to manage intricate production tasks, improve quality control, and optimize operational processes. AI-powered systems can now detect defects better than humans, predict equipment failures through real-time performance analysis, and streamline supply chain management to minimize waste. That said, AI still requires human oversight—for example, overly accurate defect detection can slow production by flagging parts that are still functional, requiring humans to recalibrate thresholds to balance precision with practical efficiency.
Another field AI is transforming is coding. Beginning in the early 2000s, coding emerged as the twenty-first century equivalent of a manufacturing line job, providing a pathway to high-quality employment for workers with sub-baccalaureate credentials. These roles were highly appealing to millennial workers seeking autonomy, flexibility, and upward mobility in an increasingly digital economy. However, just as automation reshaped traditional manufacturing, AI-driven platforms are rapidly transforming software development by automating routine coding tasks, debugging, and even generating complex scripts with minimal human input, aligning with the trend of AI amplifying the contributions of high-performing workers while potentially displacing lower-performing workers who are less effective in adapting to innovation cycles.
Unlike most other technology advancements to date, AI-driven automation not only is threatening jobs for workers who lack significant education or training, but also is encroaching on complex job functions that traditionally require years of advanced education and specialized training. For example, in radiology, AI-powered imaging analysis can now detect anomalies in X-rays, MRIs, and CT scans with a level of precision that rivals or even surpasses human radiologists (who require around thirteen years of post-high school training to become certified). Similarly, in accounting, AI and machine learning algorithms are revolutionizing financial analysis, detecting fraud, and even predicting future financial trends. These developments signal a fundamental shift in the labor market across industries: while AI is unlikely to render human labor entirely obsolete in any sector, it could erode traditional career pathways by automating entry-level and repetitive tasks, compressing job ladders, and reshaping the demand for human expertise in once-secure, high-status fields.
As AI continues to restructure the workforce, one skill that is likely to become increasingly indispensable is critical thinking—both because AI is unlikely to fully replicate it and because it is essential for ensuring that AI is used effectively and ethically. While AI excels at processing vast amounts of data and identifying patterns, it lacks the ability to apply human judgment, question assumptions, and navigate ethical complexities. As automation takes over routine analytical tasks, the capacity to evaluate AI-generated insights, interpret nuanced contexts, and make informed decisions will be crucial. Without analytical reasoning skills, organizations risk blindly implementing AI-driven recommendations, reinforcing biases, or misapplying automation in ways that could have unintended consequences. In an AI-driven economy, those who can think critically—assessing, refining, and guiding AI outputs—are likely to be best positioned to thrive. By deliberately investing in education and training programs that cultivate human-AI complementarity skills—such as critical thinking, ethical reasoning, and domain expertise—policymakers could not only empower individuals to adapt, but also unlock broader economic potential, ensuring that AI becomes a force for shared prosperity rather than deepened inequality.
Policy Recommendations to Prepare the Current and Future Workforce for AI
In light of this new reality, policymakers seeking to meet the challenge of preparing the workforce for AI should focus on three main objectives.
First, legislation should have the goal of preparing educators to teach in a world where AI is a valid tool for students in all subject areas; just as the education system once debated—and ultimately embraced—the use of calculators, the Internet, and autocorrect, it must now recognize AI as a legitimate and inevitable tool for learning. This will mean retooling instruction and evaluation frameworks to help students appropriately integrate and leverage this technology as part of their work, just as they will be expected to do in the labor market. A mechanism to achieve this would be an Artificial Intelligence Training Fund for educators from PreK through higher education. Specifically, Congress could create a fund within the U.S. Department of Education and/or the U.S. Department of Labor to support the creation and delivery of the training curriculum as well as educator time to attend trainings. Through this dedicated source of funding, states could develop and implement a strategy to upskill educators and prepare them to integrate AI into classrooms, ensuring that students know how to use basic AI-driven technologies before entering the workforce.
Second, legislation should focus on developing education pathways to prepare the range of talent necessary to develop and utilize AI technology. This could include a special concentration on the associates degree level, which can increase diversity in the AI workforce—an important consideration since, as a study by the Georgetown University Center on Education and the Workforce points out, “students [in certificate and associate’s degree programs] are more diverse by race and ethnicity, socioeconomic status, and age than those in bachelor’s degree programs.” Participation in relevant pathways should be incentivized via mechanisms such as College Promise programs, prioritizing fields that develop expertise with large language models (LLMs) and other AI systems, such as computer literacy, information science, and basic statistical analysis. Given ongoing problems with AI hallucination, it will be critical to educate students not only on how best to prompt systems, but also how to critically evaluate the outputs.
Third, policy should be developed with a plan for helping the workforce learn how to integrate AI into existing occupations and mitigating the impact of large-scale displacement. As noted above, AI has the potential to disrupt not just the routine, repetitive, and rules-based tasks that have traditionally been vulnerable to automation, but also occupations that have been largely immune to disruption from technology in the past. It is possible that the speed and scale of disruption may outpace workers’ ability to reskill through traditional means, underscoring the need for strategic support that empowers workers to use AI to enhance their productivity and adaptability. To achieve this, policymakers should focus on approaches that engage employers early in the technology adoption process. As part of this strategy, they could establish formal roles for unions and other worker groups, and consider mechanisms that incentivize companies to train existing workers for roles using new automated technologies rather than displacing them.
To help today’s workers adapt and thrive in this quickly changing landscape, Congress could create a new Artificial Intelligence Worker Training Fund via formula dollars under the Workforce Innovation and Opportunity Act (WIOA) to upskill or reskill workers who face displacement due to AI. Operating similarly to A Stronger Workforce For America’s Critical Industry Skills Fund, the fund could be used to both develop and deliver training. To encourage broad participation, courses could be offered to workers who are currently employed (especially if their company would provide full pay while the worker is attending training). To drive completion rates, all participants could receive a bonus upon passing a final exam.
The fund could also be used to provide stipends to unemployed workers in training programs, and states could modify their unemployment insurance (UI) rules to ensure that unemployed attendees could receive the stipend in conjunction with their UI benefits, which are usually too limited to cover living expenses. Providing stipends in addition to easily accessible wraparound services would reduce paperwork and other administrative hurdles that tend to impede access to critical resources in practice, and allow attendees to meet important needs not covered by wraparound services.
To help provide national oversight of this endeavor, the fund could also be used to establish a task force at the Department of Labor’s Employment and Training Administration (ETA). The task force could mirror the education and workforce coordination function provided by National Institute for Standards and Technology for the development of a semiconductor talent pool, and would be charged with monitoring and managing the impact of AI on the workforce, collecting and disseminating best practices from states, and helping training programs adapt as AI evolves.
Looking Ahead
AI appears to be a transformative and disruptive force poised to operate on a scale the world hasn’t seen since the Internet was released into the public domain. Given its potential to overhaul knowledge and skill requirements across the labor market, however, it is clear that the United States needs a national policy approach to mitigate threats and maximize the potential benefit AI presents.
Tags: manufacturing, workforce training, AI, labor