ABSTRACT. Based on an in-depth survey of the literature, the purpose of the paper is to explore artificial intelligence human resource management algorithms and talent and performance management tools in productive and collaborative workplaces. In this research, previous findings were cumulated showing that artificial intelligence-driven human resource analytics and predictive analytics and workforce management tools further machine learning-based employee acquisition and virtual reality-based job training in collaborative working environments, and the contribution to the literature is by indicating that artificial intelligence-based organizational changes and professional knowledge sharing mechanisms require movement tracking sensors, automated employee data mining and resignation prediction tools, and interconnected monitoring devices. Throughout January 2024, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “artificial intelligence work environments” + “employee productivity augmentation,” “management task automation,” and “deep and machine learning-based organizational knowledge sharing.” As research published in 2022 and 2023 was inspected, only 179 articles satisfied the eligibility criteria, and 36 mainly empirical sources were selected. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: ASReview Lab, CASP, DistillerSR, Eppi-Reviewer, JBI SUMARI, and Litstream.
JEL codes: E24; J21; J54; J64

Keywords: employee productivity augmentation; management task automation; organizational knowledge sharing; artificial intelligence; work environment

How to cite: Popescu Ljungholm, D. (2024). “Employee Productivity Augmentation, Manage- ment Task Automation, and Deep and Machine Learning-based Organizational Knowledge Sharing in Artificial Intelligence Work Environments,” Psychosociological Issues in Human Resource Management 12(1): 78–94. doi: 10.22381/pihrm12120244.

Received 27 January 2024 • Received in revised form 19 May 2024
Accepted 24 May 2024 • Available online 25 May 2024