Workload in the Age of AI
Research Trends
DOI:
https://doi.org/10.37886/ip.2026.005Keywords:
Artificial Intelligence (AI), workload, job demands, occupational stress, work design, employee well-being, bibliometric analysisAbstract
Research Question (RQ): How has research on AI and workload developed over time, and what themes, methods, and gaps shape current understanding of workload and well-being in AI-enabled work?
Purpose: The purpose of this study is to map and describe the research landscape on AI and workload, identifying key topics, publication trends, and gaps relevant to work design, management, and well-being.
Method: The study applies bibliometric analysis to peer-reviewed publications indexed in Scopus over the last five years. A transparent search strategy covers AI, workload, and related concepts. The analysis examines publication growth, citation patterns, source and author networks, and keyword co-occurrence, supported by science-mapping techniques. Interpretation is guided by work-design and job demands perspectives.
Results: We found strong growth in publications across the AI and workload cross-section and high interdisciplinarity of the field. We reveal dominant research themes, the concentration of research across regions, and methodological patterns, as well as areas that remain understudied in relation to AI, workload, and well-being.
Organization: By synthesizing a fragmented body of research, the study helps managers and HR professionals understand where evidence on the use of AI is well-developed and where caution or further evaluation is needed when implementing AI in work processes.
Society: Clarifying how workload is addressed in AI-related research contributes to responsible AI use, worker health, and fair work practices in increasingly automated environments.
Originality: The study offers a focused bibliometric overview of AI-related workload research, bringing together interdisciplinary perspectives on work design, stress, and well-being that are often studied separately.
Limitations / further research: Bibliometric findings depend on coverage of a single database and on search choices and do not provide causal evidence. Future research should combine bibliometric mapping with empirical and intervention studies across sectors and regions.
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Copyright (c) 2026 asist. Tilen Medved, red. prof. dr. Zvone Balantič
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