Labor & Employment Law Daily Wrap Up, SENATE NEWS—Hearing examines AI’s effects on jobs, career pathways, and workforce data, (Jul 30, 2026)

By Patricia K. Ruiz, J.D.

The witnesses urged lawmakers to focus on worker mobility, AI literacy, local workforce systems, and improved labor-market measurement.

Artificial intelligence (AI) is changing how work is performed, how workers gain skills, and how employers evaluate and develop talent, according to witnesses who testified July 29 before the Senate Health, Education, Labor and Pensions (HELP) Subcommittee on Employment and Workplace Safety. While the witnesses differed in their emphasis, they generally described AI as a force reshaping tasks and career pathways more often than eliminating jobs outright, while warning that policymakers lack sufficient data to track many of the changes now underway.

Several witnesses also highlighted the AI Workforce PREPARE Act as a vehicle for improving labor-market intelligence, workforce planning, and workforce-development responses.

Job displacement.Justin Heck, who leads research and data production at Opportunity@Work, testified that debate over AI and the labor market has focused largely on automation and job displacement rather than on career mobility and workers’ ability to move through established job pathways. He said workers typically advance by moving into occupations with similar skill requirements and described gateway jobs as critical stepping-stones connecting lower-wage positions to higher-paying careers. According to Heck, those jobs are particularly important for workers who are Skilled Through Alternative Routes, or STARs, who acquire skills through work experience, military service, apprenticeships, or community college rather than four-year degrees.

Heck testified that AI could disrupt those pathways in two ways. First, he said AI may reduce the number of Gateway jobs in occupations such as customer service, bookkeeping, office management, and administrative support. Second, he said AI can weaken the learning opportunities that make such positions valuable by automating the more demanding tasks through which workers historically gained experience and transferable skills. According to Heck, workers whose roles remain intact may not immediately recognize the loss of skill-building opportunities until they later attempt to advance into higher-paying jobs.

He said these risks fall disproportionately on STARs, many of whom work in occupations with high AI exposure and limited capacity to absorb labor-market disruptions. Heck urged policymakers to strengthen skills-based hiring, support workforce innovation partnerships, improve worker-transition strategies, modernize workforce data systems, and better measure worker mobility and changing career pathways.

AI literacy as a foundational workforce skill.Jonathan Liebert, chief executive officer of the Better Business Bureau of Southern Colorado, testified that the key workforce challenge is no longer whether AI will be adopted but whether workers, employers, educators, and communities will be prepared to use it responsibly. Drawing on his experience training more than 5,500 people on AI since 2025, Liebert said many organizations are adopting AI tools without adequate guidance concerning privacy, verification, bias, intellectual property, and governance.

Liebert described his perspective as one of “cautious optimism,” stating that AI can improve productivity, strengthen customer service, reduce administrative burdens, and broaden access to expertise. At the same time, he warned about fabricated outputs, inaccurate information, biased results, and the disclosure of sensitive data. He repeatedly characterized AI as “an ideation engine” rather than “an oracle,” arguing that AI should generate options and draft material while humans remain responsible for judgment and decision-making.

He outlined the BBB AI Hub, which he said was created to provide practical AI education, workforce preparation, governance resources, and marketplace guidance. Liebert testified that workforce centers, community colleges, libraries, chambers of commerce, nonprofits, and other local institutions can serve as effective delivery systems for AI education and workforce training.

Liebert recommended funding AI upskilling through trusted local organizations, strengthening AI literacy for educators and students, providing governance tools to small organizations, improving workforce intelligence and task-level labor-market data, preserving transparency and human accountability in employment decisions, and ensuring broad access to AI-related opportunities for rural and underserved communities.

Better data infrastructure needed.Carol O. Rogers, director of the Indiana Business Research Center at Indiana University’s Kelley School of Business, testified that AI adoption has expanded rapidly among both businesses and individual workers but said the labor-market effects remain mixed, emerging, and difficult to measure. She cited data showing substantial state-by-state variation in AI adoption and research indicating that AI-exposed occupations in Indiana experienced steeper declines in job postings while also posting stronger wage growth since 2022.

Rogers said national evidence points more toward labor-market reallocation than broad-based employment losses, with some occupations experiencing declining demand and others seeing higher wages and increased need for skilled workers. She cautioned lawmakers against relying heavily on headline layoffs announced by major technology companies, noting that surveys show most small businesses using AI have not reduced employment.

Much of Rogers’ testimony focused on the AI Workforce PREPARE Act, which she described as fundamentally a data bill. She questioned whether existing federal and state data systems are currently capable of generating timely and reliable AI-specific labor-market information. She said state administrative wage records and longitudinal workforce systems are frequently underutilized, while occupational classification systems and employer-reported data remain too slow and inconsistent for measuring rapid technological change.

Rogers recommended greater use of task-level analysis, standardized methods for reporting AI-related layoffs, expanded researcher access to workforce data, stronger state-federal data linkages, independent validation of voluntary AI-adoption reports, and development of standardized measures of employer AI implementation and skills demand.

Need to respond before disruption becomes displacement.Ken Clark, president and chief executive officer of EmployIndy, testified that AI should be understood primarily as a workforce issue rather than a technology issue. Drawing on his experience in both technology leadership and workforce development, Clark said AI is likely to reshape far more jobs than it replaces, comparing its impact to the way the internet and email transformed work by changing tasks and improving productivity while creating new opportunities.

Clark said the greatest risk is not mass unemployment but disruption to traditional pathways through which workers gain experience and develop professional judgment. He testified that employers increasingly expect entry-level workers to bring practical experience, adaptability, and the ability to work alongside emerging technologies. He also said AI is changing workplace expectations across industries, including healthcare, where technology is being used to reduce administrative burdens and allow workers to focus on duties requiring human interaction and judgment.

According to Clark, workers will need both digital literacy and AI literacy, including the ability to evaluate AI-generated information, recognize bias, understand AI’s limitations, and determine when human judgment should prevail. He said many small and medium-sized employers remain in an experimental phase of AI adoption and need assistance identifying workforce implications and training needs.

Clark endorsed the AI Workforce PREPARE Act, arguing that local workforce development boards need stronger labor-market intelligence, forecasting tools, and research capabilities to identify changes in job tasks and career pathways before workers are displaced. He said workforce boards serve as intermediaries among employers, educators, training providers, and policymakers and are positioned to help communities adapt to AI-driven change.

No evidence yet of broad AI-driven job losses.Liya Palagashvili, senior research fellow and director of labor policy at the Mercatus Center at George Mason University, testified that early evidence does not support claims of widespread AI-driven employment losses. She said AI is affecting the labor market through multiple channels simultaneously, including task automation, worker augmentation, productivity improvements, new-task creation, and changes in how firms organize production.

Palagashvili testified that most studies show little effect on aggregate employment, unemployment, earnings, or work hours and that most AI-using businesses report no overall employment change. She identified younger workers entering highly AI-exposed occupations as the area of greatest potential concern but said current research does not establish AI as the primary cause of weaker entry-level hiring outcomes.

She devoted substantial attention to data limitations, arguing that federal labor-market statistics are not yet equipped to fully measure AI’s effects. According to Palagashvili, household surveys generally do not capture how AI changes tasks within jobs, while business surveys often are not connected to information on hiring, earnings, occupational composition, or workforce outcomes. She said the AI Workforce PREPARE Act would help address some of those gaps by expanding AI-related questions and improving federal data collection.

Palagashvili also presented research suggesting AI may be contributing to growth in independent work and self-employment. She testified that nonemployer-type business applications and solo self-employment have grown more rapidly in AI-exposed industries and occupations since early 2024, a pattern she said is consistent with AI lowering the cost of operating independently by assisting with research, drafting, coding, design, analysis, and communications tasks. She stressed that the findings were descriptive and not evidence of causation.

Among her recommendations, Palagashvili urged policymakers to link AI-adoption data with labor-market outcomes, improve occupation-level administrative data, develop better measures of nonemployer businesses, expand worker-focused AI surveys, modernize portable-benefit systems, and reduce disparities between the tax treatment of investments in worker training and investments in technology and equipment.

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