When AI Acceleration Meets Human Limits: Understanding and Managing Workload Creep in the Age of Generative AI

Abstract: Recent empirical research reveals a paradox at the heart of workplace AI adoption: rather than reducing workload, generative AI tools frequently intensify work demands through a phenomenon researchers term "workload creep." Drawing on longitudinal qualitative research from UC Berkeley and emerging evidence from workplace studies, this article examines how voluntary AI adoption can create self-reinforcing cycles of task expansion, attention fragmentation, and boundary erosion between work and non-work time. Despite productivity gains on discrete tasks, organizations adopting AI without governance structures often experience diminished employee wellbeing and limited organizational performance improvements. This article synthesizes evidence on the organizational and individual consequences of unmanaged AI adoption, provides intervention strategies grounded in job design theory and change management research, and outlines a framework for building sustainable AI integration capabilities that protect both productivity and human flourishing.

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