The Impact of Artificial Intelligence on Job Performance: A Systematic Review

The rapid diffusion of artificial intelligence (AI) technologies across organizational functions has intensified scholarly interest in their implications for employee job performance. While existing studies highlight both productivity gains and emerging challenges associated with AI adoption, the intellectual structure, thematic evolution, and research frontiers of this growing body of literature remain fragmented. The goal of this study is to organize the scholarly contribution to the impact of artificial intelligence on job performance. The study was carried out by examining 1,747 publications that were indexed in the Scopus database. The data were analyzed to give an overview of the domain using the PRISMA sampling approach and VOSviewer software. We then reported tables, graphs, and maps to highlight the key performance metrics for the creation of articles and their citations. The findings demonstrate the frequent usage of phrases like “job performance,” “artificial intelligence,” “organization performance,” “human resources management,” “performance management,” and “machine learning,” among others. Furthermore, some of the more recent study subjects are revealed by the density map, including “digital education technology,” “innovation performance,” “emotional intelligence,” “big data analysis,” “deep learning algorithms,” “data augmentation,” “smart technologies,” and “knowledge specificity." The study offers valuable insights for managers and policymakers who aim to utilize AI to improve job performance while minimizing negative impacts on employees.