Matthias Oschinski, Ali Crawford and Maggie Wu
Introduction
Artificial intelligence (AI) has the potential to substantially increase productivity for the U.S. economy, in turn bolstering economic growth and overall living standards. At the same time, it may have the capacity to transform the nature of work across various industries, thereby significantly reshaping employment patterns and job roles. Research suggests that knowledge workers, typically shielded from technological disruption, may be significantly impacted by AI.
As a consequence, the rapid rise and integration of AI has sparked renewed discussions on workforce development, primarily driven by concerns over worker displacement. Moreover, it underscores the pressing need to cultivate a larger pool of skilled but economically resilient talent. In the context of these technological shifts, the United States also faces the challenges of low and declining labor force participation, a decentralized training system, and reduced federal support for training programs. Against this backdrop, a new era of workforce development is emerging with renewed focus on skills-based learning programs. Both workers and employers are starting to shift away from traditional workforce on-ramps and embracing avenues for re skilling and upskilling. Government agencies, employers, and educational institutions need to evaluate whether current workforce training and work-based learning programs are designed to maximize the country’s ability to reap the economic benefits of AI-driven productivity growth and ensure that these benefits are widely shared across the workforce. Achieving this requires a deep understanding of existing challenges, as well as identifying and scaling the key factors that drive successful workforce training.
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