aiqbench analysis
This study identifies "AI resilience" as a critical framework for older workers navigating generative AI disruptions in bridge employment, emphasizing the need to balance individual adaptation strategies with organizational structural reforms. By analyzing boundary work and task reconfiguration, the research highlights that mitigating burnout requires addressing both temporal and structural instabilities caused by rapid AI integration. The findings offer actionable insights for policymakers and HR leaders aiming to sustain workforce participation among vulnerable demographics amidst accelerating technological change.
Concerns and Strategic Responses of Older Workers Navigating Generative AI in Bridge Employment
arXiv:2606.07543v1 Announce Type: cross Abstract: Generative AI (GenAI) is transforming workplaces at a rapid pace. This disproportionately affects vulnerable communities, including older workers (OWs) who re-enter the workforce through bridge employment prior to final retirement. Through in-depth semi-structured interviews with 21 professionals, we examine how OWs navigate GenAI-driven disruptions while pursuing bridge roles, focusing on their concerns about GenAI integration and their responses to these changes. Our findings show that OWs experienced both temporal and structural disruptions across all stages of the bridge employment decision-making process due to GenAI. In response, they reconfigured their tasks through different forms of boundary work aimed at restoring stability and continuity. We conceptualize these responses as AI resilience, which reshaped OWs' bridge employment decision-making into an ongoing process of negotiation and adaptation. We conclude by offering recommendations to reduce burnout among OWs by balancing individual-level AI resilience strategies with meso-level AI resilience collectives and macro-level adversarial and contestable AI-mediated organizational structures.
Abstract:Generative AI (GenAI) is transforming workplaces at a rapid pace. This disproportionately affects vulnerable communities, including older workers (OWs) who re-enter the workforce through bridge employment prior to final retirement. Through in-depth semi-structured interviews with 21 professionals, we examine how OWs navigate GenAI-driven disruptions while pursuing bridge roles, focusing on their concerns about GenAI integration and their responses to these changes. Our findings show that OWs experienced both temporal and structural disruptions across all stages of the bridge employment decision-making process due to GenAI. In response, they reconfigured their tasks through different forms of boundary work aimed at restoring stability and continuity. We conceptualize these responses as AI resilience, which reshaped OWs' bridge employment decision-making into an ongoing process of negotiation and adaptation. We conclude by offering recommendations to reduce burnout among OWs by balancing individual-level AI resilience strategies with meso-level AI resilience collectives and macro-level adversarial and contestable AI-mediated organizational structures.
Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
arXiv:2606.07543 [cs.CY]
(or arXiv:2606.07543v1 [cs.CY] for this version)
https://doi.org/10.48550/arXiv.2606.07543 Focus to learn more arXiv-issued DOI via DataCite
https://doi.org/10.1145/3808045.3808070 Focus to learn more DOI(s) linking to related resources
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Full text under the publisher's open licence · 261 words
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