With the rise of generative AI, companies are moving quickly to restructure their workforces. Employees, though, often feel left in the dark by leadership—not fully understanding what those changes could mean for them. New research suggests generative AI could create a strategic risk for organizations as the gap between employer adaptation and employee anxiety steadily grows amidst reshaping workforces.
As the first major technological threat to white-collar work emerges, the evidence of employer adaptation is striking. My co-authors and I analyzed job-posting data from more than 60,000 employers, tracking hiring patterns at S&P 1500 firms across 13 years. This included data from before and after ChatGPT’s release in 2022. At firms with the highest concentrations of white-collar workers, weekly job postings fell by as much as 18.9% after the release, a contraction comparable to hiring declines during the early months of the COVID-19 pandemic.
But the decline was selective. Firms with stronger financial positions, greater digital readiness and more technically skilled workforces adapted most aggressively, cutting lower-level white-collar roles, such as administrative positions, junior analysts and entry-level office jobs, while accelerating demand for higher-level technical, managerial and AI-related talent.
See also: Why HR needs a ‘recovery layer’ for real AI transformation
Companies and workers are dealing with gen AI’s disruption differently
Organizations are responding decisively and strategically to technological disruption. But inside those same firms, employees are reacting very differently. Glassdoor data from the same period reveals significant declines in CEO approval ratings and business outlook scores. The sharpest drops came from junior employees whose roles were most directly in the path of the hiring cuts. However, senior-level employees reported feelings of uneasiness as well, suggesting the distrust runs deep.
When we examined the language employees used in their reviews, two concerns surfaced repeatedly: job insecurity and a lack of transparency from management. In the absence of clear direction, many workers acted on their own. Among more than 15.8 million workers who changed jobs after ChatGPT’s release, those in vulnerable roles were more likely to pivot toward AI-related work or shift into less exposed occupations. Employees weren’t resisting change; they were navigating it alone.
The issue is a fundamental corporate mismatch: Employers are executing a deliberate workforce strategy, while employees experience it as growing anxiety, uncertainty and declining trust in leadership.
Whether the gap reflects intentional withholding or poor communication, the consequences are the same. AI transformation requires workers to learn new tools, take on new responsibilities, and often redefine their roles. That kind of buy-in necessitates trust in leadership and confidence in where the organization is headed.
Adaptation is uneven: leaders’ response will determine if gap grows
The data also captured uneven adaptation. Women were significantly less likely to pivot to AI-related work, and R&D firms, which might be expected to lead adaptation, cut hiring across levels even as they increased demand for AI skills.
The research, detailed in a new brief from Columbia Business School’s Reuben Mark Initiative for Organizational Character and Leadership, offers a clear diagnosis. How leaders respond will determine whether this gap widens or closes. Transparent communication about which skills will be valued, how responsibilities will evolve and what the organization is working toward may help close the gap revealed in the data. So, too, might framing generative AI as a tool that expands what employees can do, rather than one that determines how many of them are needed.
Generative AI will reshape the white-collar workforce regardless of how any one company responds. What remains within leaders’ control is whether employees experience that transformation as an opportunity or as something being done to them. The difference will be determined not by technology, but by humans themselves.
This article was co-authored by Philip G. Berger of the University of Chicago, Lin Qiu of Purdue University and Cindy Xinyi Shen of Northwestern University.
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