From Technostressors to AI-Stressors: A Systematic Literature Review of Stressors Associated with AI Systems

Abstract

This systematic review explores how the factors associated with artificial intelligence (AI) systems induce stress in workplace contexts. Following a pre-specified protocol and the PRISMA 2020 guidelines, we searched Scopus on 7 March 2025 for peer-reviewed journal articles written in English (no date limits) that empirically or conceptually link AI use to negative stress at work. Studies in which AI was investigated solely as a stress-relieving tool were excluded. Screening 1,333 records yielded 66 eligible articles (40 quantitative, 9 qualitative, 6 mixed-methods, and 11 conceptual) spanning healthcare, hospitality, manufacturing, transport, and other sectors. Although no formal risk-of-bias tools were applied, evidence strength was noted descriptively. Data were charted on context, AI type, methods, and stress findings and then narratively synthesized. Across the reviewed studies, AI use amplifies the six established technostressors: techno-overload, techno-invasion, techno-complexity, techno-insecurity, technouncertainty, and techno-unreliability. In addition, it introduces five emerging AI-stressors (i.e., stressors unique to AI, distinct from the established technostressors): techno-unpredictability, loss of autonomy, ethical and moral conflict, social erosion, and career disruption. These findings indicate that the established technostressors are inadequate to capture the distinctive characteristics of contemporary AI systems. Although most evidence is drawn from cross-sectional designs and focuses on negative outcomes, the review highlights an urgent need for more nuanced and responsible approaches to AI utilization in organizational contexts.