Skill Development, Maintenance, Erosion, and Revaluation: How Knowledge Workers Experience Generative AI

Source: TU Delft (Oviedo-Trespalacios, Laksanadjaja, Torkamaan), AHFE Open Access · 2026

Generative AI (GenAI) is rapidly embedding itself in knowledge work, supporting tasks such as writing, analysis, coding, and information synthesis. Although widely promoted as enhancing productivity and learning, concerns persist regarding overreliance, deskilling, and erosion of professional expertise. Current debates typically frame GenAI’s impact on skills in binary terms—upskilling versus deskilling—yet empirical evidence on how workers themselves experience these changes in everyday practice remains limited. This study examines how knowledge workers perceive the impact of GenAI on their professional skills. Semi-structured interviews were conducted with 38 professionals in the Netherlands, including academics (e.g., lecturers and professors) and non-academic professionals (e.g., consultants, analysts, engineers, legal professionals, and public sector employees) with varying levels of experience. Data were analyzed using inductive thematic analysis to identify recurring patterns in participants’ accounts of skill-related change. Four perceived skill outcomes emerged: skill development, skill maintenance, skill erosion, and skill revaluation. Skill development involved acquiring or strengthening competencies through learning from GenAI outputs, expanded information access, and offloading routine tasks to focus on higher-level work. Skill maintenance described situations where participants perceived little or no change, often linked to selective and critical use. Skill erosion referred to diminished ability to perform tasks independently without GenAI support. Skill revaluation captured shifts in perceived skill importance as certain tasks became delegable while others gained prominence. Overall, findings indicate that GenAI’s impact on professional skills is heterogeneous and practice-dependent. The proposed four-outcome framework offers a nuanced account of how workers interpret skill change in everyday GenAI use.