Generative AI in the Workplace: A Systematic Review of Productivity Effects, Employment Perceptions, and Job Insecurity

Abstract

The growing adoption of generative artificial intelligence (AI) in workplace settings has generated significant interest in its implications for productivity, employee perceptions, and job security. This systematic literature review synthesises findings from 40 empirical and conceptual studies published between 2020 and 2025 across organisational and professional contexts to evaluate the multifaceted impact of generative AI on organisational and workforce outcomes. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, a structured search was conducted across Google Scholar and Dimensions.ai, yielding 3,252 database records, with 8 additional hand-searched studies, of which 40 met the inclusion criteria. The review identifies consistent evidence of productivity improvements driven by task automation, decision support, and knowledge augmentation. However, these gains are accompanied by mixed employee perceptions, with increased efficiency and job satisfaction coexisting alongside concerns about skill obsolescence and role displacement. Job insecurity emerges as a critical mediating factor influencing employee attitudes and behavioral responses, including upskilling intentions and resistance to technological change. Importantly, the review reveals a significant research gap in the comparative understanding of generative AI's impact across developed and developing economies, where differences in technological infrastructure, labor market dynamics, and skill distributions may lead to uneven outcomes. The findings highlight that the effects of generative AI are heterogeneous and context-dependent, shaped by job roles, skill levels, and institutional environments. By integrating fragmented literature into a cohesive framework, this study contributes to the emerging discourse on AI-driven workplace transformation and offers implications for managers and policymakers to ensure more balanced, inclusive, and context-sensitive AI adoption strategies.