Two recent reports, considered together, describe a workforce development challenge that goes beyond how artificial intelligence will change any single job. A recent report from ETS found that U.S. workers understand the need to continually adapt. In fact, 78% said job security now requires continuous adaptation, and 85% said upskilling or reskilling is a necessity. Yet only 71% reported proactively developing new skills, a lower share than in other high-growth countries such as India and China. Workers pointed to practical barriers including finding time to learn while managing a current workload, the cost of training, and a lack of employer support.
The issue might not be awareness. Workers understand their jobs are changing. The bigger question is whether organizations are providing the time, resources, and direction employees need to keep pace.
Research from Stanford's Digital Economy Lab adds a second dimension, particularly for early-career workers. Using ADP payroll data, researchers found that employment among 22- to 25-year-olds in occupations highly exposed to AI is now about 19% below where it would be expected, based on trends in less-exposed occupations. The researchers are careful to note that these findings are descriptive, not causal.
The Stanford team offers a likely explanation. They make a distinction between explicit knowledge, which can be documented and taught, and tacit knowledge, which comes from experience, practice, mentorship and knowing how to apply what you’ve learned. Employment has declined most among young workers in roles that lean on codified knowledge, the kind AI increasingly reproduces. Employment among experienced workers in roles built on tacit knowledge has held steady or grown.
This distinction matters because tacit knowledge has traditionally been built on the job, often in the same entry-level roles now most exposed to AI. Early-career employees have long learned by observing experienced colleagues, receiving feedback, and gradually taking on responsibility that isn't written into any job description. If AI is narrowing access to those roles, one of the main paths through which tacit knowledge gets built and passed on may be narrowing with it, at the same moment ETS finds that current employees already lack the time, resources and support to develop new skills.
Together, the two reports describe pressure from both directions. Entry-level workers may have fewer traditional openings to build experience-based knowledge. Existing employees know they need new capabilities but are not being given what they need to build them. Neither report set out to make this connection, but read side by side, they raise a real question about how organizations will develop the workforce they need.
This suggests workforce development needs to become more intentional, not something employees are simply told to handle on their own time. Organizations need to understand which capabilities they will need, where those capabilities exist today, and how they will be built whether it be through formal training, restructured entry-level roles, or deliberate mentorship.
That means creating structured ways for early-career employees to build tacit knowledge and giving existing employees real time and resources to develop new skills, not just the expectation that they will.
Sources: prnewswire.com; digitaleconomy.stanford.edu