GenAI disrupts older workers' bridge employment, study finds
21 professionals reveal how GenAI forces constant adaptation in pre-retirement roles
A new study from CHIWORK'26, authored by Aditya Nayak, Aakash Gautam, and Rama Adithya Varanasi (arXiv:2606.07543), examines how generative AI (GenAI) impacts older workers (OWs) re-entering the workforce through bridge employment—temporary roles before final retirement. Based on in-depth semi-structured interviews with 21 professionals, the research reveals that GenAI causes both temporal disruptions (shifting skill timelines) and structural ones (redefining job roles) at every stage of bridge employment decision-making. Workers responded by reconfiguring tasks via boundary work—adjusting what tasks they do and how they interact with AI systems.
The authors conceptualize these adaptive strategies as 'AI resilience,' transforming bridge employment into an ongoing process of negotiation rather than a fixed decision. Their recommendations emphasize moving beyond individual-level resilience (e.g., upskilling) to include meso-level resilience collectives (peer support groups, mentorship) and macro-level adversarial and contestable AI-mediated organizational structures. This framework aims to reduce burnout among older workers navigating GenAI-driven workplace transformations, highlighting the need for systemic changes in how organizations deploy AI around vulnerable populations.
- 21 older professionals interviewed across bridge employment roles
- GenAI causes temporal and structural disruptions in all stages of re-entry decisions
- Workers employ 'boundary work' and 'AI resilience' as ongoing adaptation strategies
Why It Matters
As GenAI reshapes work, older workers face unique pressures—workplaces must build resilience systems, not just individual upskilling.