Featuring scholarly and practitioner insights from HR and people leaders, industry experts, and researchers.

Abstract: Early evidence on generative artificial intelligence at work suggests that its benefits are real but unevenly shared. Year-two results from the American Job Quality Study show that most employees who use AI report working faster and more creatively, yet only 28% of U.S. employees use AI at least weekly and 54% have never used it in their role. Regular use is concentrated among college graduates, managers, higher earners, and knowledge-intensive industries, the same groups that already hold quality jobs, while about half of all employees report less influence over technology adoption than they would like. This article defines the "AI dividend gap," reviews research on who adopts and who gains from generative AI, and examines organizational and individual consequences. It then synthesizes five evidence-based responses, illustrated with documented examples from entertainment, technology, healthcare, hospitality, retail, and financial services: designing for broad access, closing the use gap, building worker voice into technology decisions, bargaining over how gains and risks are shared, and reinvesting gains in better jobs.

The AI dividend gap describes the uneven distribution of generative artificial intelligence benefits in the workplace, where advantages flow primarily to college graduates, managers, and knowledge workers rather than frontline staff. Although research demonstrates that novices and lower-skilled employees often achieve the greatest productivity gains from artificial intelligence, regular tool usage remains concentrated among individuals who already hold high-quality jobs. To address this disparity, organizations must implement broad access, targeted training, and meaningful employee participation in technological decisions. Furthermore, leaders should establish formal agreements regarding gain-sharing and transparency while actively reinvesting productivity dividends into enhanced job roles. Ultimately, closing this divide transforms artificial intelligence from a source of workplace anxiety into an opportunity for shared organizational prosperity.

The AI dividend gap describes the uneven distribution of generative artificial intelligence benefits in the workplace, where advantages flow primarily to college graduates, managers, and knowledge workers rather than frontline staff. Although research demonstrates that novices and lower-skilled employees often achieve the greatest productivity gains from artificial intelligence, regular tool usage remains concentrated among individuals who already hold high-quality jobs. To address this disparity, organizations must implement broad access, targeted training, and meaningful employee participation in technological decisions. Furthermore, leaders should establish formal agreements regarding gain-sharing and transparency while actively reinvesting productivity dividends into enhanced job roles. Ultimately, closing this divide transforms artificial intelligence from a source of workplace anxiety into an opportunity for shared organizational prosperity.

The AI dividend gap describes the uneven distribution of generative artificial intelligence benefits in the workplace, where advantages flow primarily to college graduates, managers, and knowledge workers rather than frontline staff. Although research demonstrates that novices and lower-skilled employees often achieve the greatest productivity gains from artificial intelligence, regular tool usage remains concentrated among individuals who already hold high-quality jobs. To address this disparity, organizations must implement **broad access**, **targeted training**, and **meaningful employee participation** in technological decisions. Furthermore, leaders should establish formal agreements regarding **gain-sharing and transparency** while actively **reinvesting productivity dividends** into enhanced job roles. Ultimately, closing this divide transforms artificial intelligence from a source of workplace anxiety into an opportunity for shared organizational prosperity.

The **AI dividend gap** describes the uneven distribution of generative artificial intelligence benefits in the workplace, where advantages flow primarily to college graduates, managers, and knowledge workers rather than frontline staff. Although research demonstrates that novices and lower-skilled employees often achieve the greatest productivity gains from artificial intelligence, regular tool usage remains concentrated among individuals who already hold high-quality jobs. To address this disparity, organizations must implement **broad access**, **targeted training**, and **meaningful employee participation** in technological decisions. Furthermore, leaders should establish formal agreements regarding **gain-sharing and transparency** while actively **reinvesting productivity dividends** into enhanced job roles. Ultimately, closing this divide transforms artificial intelligence from a source of workplace anxiety into an opportunity for shared organizational prosperity.

This research examines the glass cliff phenomenon, which describes how women and racial or ethnic minorities are disproportionately appointed to precarious leadership roles during organizational crises. It reviews two decades of mixed academic research, experimental findings, and recent corporate controversies—such as a high-profile debate involving a McKinsey partner—to evaluate the prevalence and drivers of these high-risk assignments. The research highlights the negative organizational and individual consequences of such appointments, including heightened scrutiny, shorter tenures, and unfair performance attribution. To mitigate these issues, the author outlines five evidence-based board responses, ranging from transparent due diligence and early succession pipelines to proper resourcing and objective evaluations. Ultimately, the research argues that boards must establish long-term governance disciplines and foster evidence-first cultures to set all turnaround leaders up for sustainable success.

This research examines the glass cliff phenomenon, which describes how women and racial or ethnic minorities are disproportionately appointed to precarious leadership roles during organizational crises. It reviews two decades of mixed academic research, experimental findings, and recent corporate controversies—such as a high-profile debate involving a McKinsey partner—to evaluate the prevalence and drivers of these high-risk assignments. The research highlights the negative organizational and individual consequences of such appointments, including heightened scrutiny, shorter tenures, and unfair performance attribution. To mitigate these issues, the author outlines five evidence-based board responses, ranging from transparent due diligence and early succession pipelines to proper resourcing and objective evaluations. Ultimately, the research argues that boards must establish long-term governance disciplines and foster evidence-first cultures to set all turnaround leaders up for sustainable success.

This research examines the glass cliff phenomenon, which describes how women and racial or ethnic minorities are disproportionately appointed to precarious leadership roles during organizational crises. It reviews two decades of mixed academic research, experimental findings, and recent corporate controversies—such as a high-profile debate involving a McKinsey partner—to evaluate the prevalence and drivers of these high-risk assignments. The research highlights the negative organizational and individual consequences of such appointments, including heightened scrutiny, shorter tenures, and unfair performance attribution. To mitigate these issues, the author outlines five evidence-based board responses, ranging from transparent due diligence and early succession pipelines to proper resourcing and objective evaluations. Ultimately, the research argues that boards must establish long-term governance disciplines and foster evidence-first cultures to set all turnaround leaders up for sustainable success.