Generative AI is rapidly becoming a key part of knowledge work, offering opportunities to increase productivity, creativity, job satisfaction, and improve learning outcomes, while also raising concerns about potential skill erosion as tasks are increasingly delegated to GenAI systems. Despite growing research on productivity, ethics, and governance, limited attention has been given to how knowledge workers manage their interaction with GenAI to sustain professional skills, which are critical for personal development, organizational efficacy, and societal resilience.

This thesis aims to enhance understanding of how GenAI impacts professional skills and whether knowledge workers self-regulate their use to counteract skill changes. Using a qualitative research approach, 38 semi-structured interviews were conducted with knowledge workers across ten sectors. Thematic analysis revealed that GenAI use can result in four distinct skill outcomes: development, maintenance, erosion, and revaluation. Crucially, these outcomes depend on the way workers engage with GenAI rather than the technology alone, with skill revaluation showing that erosion is not always perceived as harmful.

Knowledge workers employ self-regulatory strategies before, during, and after GenAI use, including setting boundaries, tailoring usage to specific situations, critically reviewing outputs, and rewriting or documenting results. Engagement in self-regulation is shaped by drivers across interactional, organizational, and societal contexts, yet organizational support for mitigating risks of skill erosion remains limited, leaving responsibility with individual workers.

By integrating insights on skill outcomes, strategies, and drivers, this study highlights how self-regulation unfolds within a complex sociotechnical system. The findings provide implications for knowledge workers, organizations, and policymakers to design interventions that foster responsible adoption of GenAI, protect professional competencies, and strengthen societal resilience. Future research should further explore organizational-level interventions, governance structures, and profession-specific experiences to ensure skill preservation in the age of GenAI.