Individual performance at work is fundamental to the effective functioning of any organization. Due to its importance, it is one of the most relevant and researched constructs in people management, and organizational behavior studies. However, few studies are dedicated to analyzing the state of the art on the subject, especially in the post COVID-19 pandemic scenario, an event that has generated significant changes in the contemporary work context. Therefore, this paper presents a systematic review with bibliometric analysis of the literature on individual performance at work between 2016 and 2023, based on journals with a high impact factor. 145 articles published in journals with a JCR above 8 were reviewed. The findings confirm that individual performance at work is a criterion variable that is predominantly investigated using quantitative methods, and suggest opportunities to advance the theoretical definition of the construct, as well as the development of measurement instruments consistent with its multidimensionality.

This scoping review examines the integration of artificial intelligence (AI) within organization development (OD), a field dedicated to enhancing organizational effectiveness through planned, human-centered change interventions. As AI reshapes industries by optimizing decision making, processes, and workflows, its potential to transform OD practices remains underexplored. This paper synthesizes findings from 49 studies published between 2019 and 2024, identifying key themes in AI’s application to leadership development, organizational structure, performance management, human resources (HR), and organizational culture. Whereas AI offers promising avenues to enhance efficiency, flexibility, and data-driven insights, the review highlights critical concerns, including ethical implications, privacy issues, and the potential erosion of essential human qualities in organizational culture. The study underscores the need for comprehensive governance frameworks and human oversight to address these challenges. Additionally, it calls for empirical, longitudinal research to assess AI’s long-term impact on organizational outcomes and employee engagement. This review contributes to OD literature by mapping AI’s transformative potential and outlining a future research agenda aimed at bridging the gap between technology and the human-centered foundations of OD, offering valuable insights for practitioners and scholars alike.