Concerns and Strategic Responses of Older Workers Navigating Generative AI in Bridge Employment

A research study examines how older workers navigating bridge employment experience disruptions from generative AI adoption and develop resilience strategies to adapt. The findings reveal that older workers face temporal and structural challenges throughout their re-entry into the workforce, responding through task reconfiguration and boundary work while requiring organizational and collective support to prevent burnout.

This academic research addresses a demographic vulnerability intersecting workforce dynamics and AI disruption. Older workers pursuing bridge employment—temporary roles between career transitions and retirement—represent a growing segment facing accelerated technological change without necessarily possessing native digital fluency. The study's identification of both temporal and structural disruptions suggests GenAI doesn't simply automate tasks but fundamentally alters decision-making processes across job search, role evaluation, and task execution phases.

The labor market context amplifies these findings' significance. As organizations rapidly integrate GenAI capabilities, workers lacking early exposure or training struggle with adoption curves. Bridge employment typically attracts workers seeking income stability and social engagement; GenAI integration transforms these roles from relatively stable arrangements into continuous adaptation exercises. This creates psychological burden beyond technical skill gaps.

The research's framework of 'AI resilience' spanning individual, collective, and organizational levels provides actionable insight for employers managing workforce retention. Organizations implementing GenAI without supporting vulnerable worker populations risk increased turnover, productivity loss, and potential legal exposure under age discrimination considerations. The recommendation for 'adversarial and contestable AI-mediated structures' suggests workers need agency in technology implementation rather than passive adoption.

The implications extend beyond HR policy. As GenAI deployment accelerates across sectors, understanding how vulnerable populations navigate technology-driven disruption becomes critical for maintaining workforce stability and social cohesion. Future research should examine whether similar patterns emerge in other vulnerable worker categories and whether prescribed interventions measurably reduce burnout and improve retention.

• →Older workers in bridge employment experience dual disruptions from GenAI affecting both job search processes and daily task execution.
• →Workers develop boundary work and task reconfiguration strategies to restore stability, demonstrating adaptive capacity rather than passive obsolescence.
• →Individual resilience strategies alone prove insufficient; organizations must implement collective support systems and contestable AI governance structures.
• →GenAI integration in hiring and workplace processes may inadvertently amplify age-related employment barriers without intentional mitigation.
• →Supporting vulnerable worker populations through GenAI transitions requires coordinated efforts across individual skill-building, peer networks, and organizational design.