THE IMPACT OF GENERATIVE AL ON EMPLOYEE PRODUCTIVITY AND JOB REDESIGN IN MODERN ORGANIZATIONS
DOI:
10.5281/zenodo.23134869Published:
2026-10-04Downloads
Abstract
The rapid deployment of Generative Artificial Intelligence (GenAI) across enterprise environments represents a foundational shift in how work is structured, cognitive tasks are allocated, and individual productivity is realized. This critical review systematically synthesizes empirical and conceptual literature published between 2021 and 2026 to examine the multi-dimensional impact of GenAI on employee productivity and job redesign. Guided by Job Demands-Resources (JD-R) theory, Socio-Technical Systems (STS) theory, and the Task-Technology Fit (TTF) framework, this paper analyzes findings across 16 benchmark studies encompassing randomized field experiments, structural equation modeling, and qualitative case investigations. Results indicate that GenAI integration yields significant task-level productivity gains—ranging from 14% to 40% across professional domains—predominantly by automating routine cognitive tasks, reducing time-to-completion, and elevating output quality for lower-skilled workers. However, operationalizing these productivity gains requires comprehensive job redesign, characterized by cognitive task unbundling, skill reallocation toward prompt engineering and critical evaluation, and the mitigation of emerging techno-stressors. The review synthesizes core mechanisms into a structural path model and outlines a strategic HR governance framework to optimize human-AI collaboration.
Keywords:
Cognitive Augmentation Employee Productivity Generative AI Human-AI CollaborationReferences
Agrawal, A., Gans, J. S., & Goldfarb, A. (2024). Prediction, judgment, and the economic impact of generative AI. Journal of Economic Perspectives, 38(1), 71–94. https://doi.org/10.1257/jep.38.1.71
Bakhshi, H., Hargreaves, I., & Mateos-Garcia, J. (2024). Generative AI and the restructuring of knowledge work: Empirical evidence from enterprise panel data. Journal of Business Research, 172, 114410. https://doi.org/10.1016/j.jbusres.2023.114410
Bakker, A. B., & Demerouti, E. (2017). Multiple levels in job demands-resources theory: Implications for employee well-being and performance. Handbook of Wellbeing, 1–13.
Brynjolfsson, E., Li, D., & Raymond, L. R. (2023). Generative AI at work. NBER Working Paper No. w31161. National Bureau of Economic Research. https://doi.org/10.3386/w31161
Budhwar, P., Chowdhury, S., Wood, G., Aguinis, H., Bamberger, P., Bozkurt, O., ... & Cooke, F. L. (2023). Human resource management in the age of generative artificial intelligence: Perspectives and research directions. Human Resource Management Journal, 33(3), 606–659. https://doi.org/10.1111/1748-8583.12521
Charlwood, A., & Guenole, N. (2022). Can artificial intelligence make work better? Data-driven HR, machine learning, and the future of work. Human Resource Management Journal, 32(3), 675–686. https://doi.org/10.1111/1748-8583.12458
Choudhury, P., Khanna, T., Makridis, C. A., & Schirmann, M. (2024). Generative AI and knowledge worker productivity: Field experimental evidence from asynchronous remote work. Strategic Management Journal, 45(4), 689–718. https://doi.org/10.1002/smj.3582
Dell'Acqua, F., McFowland, E., Mollick, E. R., Lifshitz-Assaf, H., Lakhani, K. R., & Deloitte, B. (2023). Navigating the jagged technological frontier: Field experimental evidence of the effects of AI on knowledge worker productivity and quality. Harvard Business School Working Paper No. 24-013.
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., ... & Wright, R. (2023). 'So what if ChatGPT wrote it?' Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642
Eloundou, T., Manning, S., Mishkin, P., & Rock, D. (2023). GPTs are GPTs: An early look at the labor market impact potential of large language models. arXiv preprint arXiv:2303.10130.
Felten, E. W., Raj, M., & Seamans, R. (2023). How will language modelers affect occupations and industries? Journal of Management, 49(7), 2189–2211. https://doi.org/10.1177/01492063231181234
Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS Quarterly, 19(2), 213–236. https://doi.org/10.2307/249689
Korinek, A. (2023). Generative AI for economic research: Use cases and implications. Journal of Economic Literature, 61(4), 1281–1317. https://doi.org/10.1257/jel.20231736
Noy, S., & Zhang, W. (2023). Experimental evidence on the productivity effects of generative artificial intelligence. Science, 381(6654), 187–192. https://doi.org/10.1126/science.adh2586
OECD. (2023). OECD Employment Outlook 2023: Artificial Intelligence and the Labour Market. OECD Publishing. https://doi.org/10.1787/08785bba-en
Peng, S., Kalliamvakou, E., Cihon, P., & Demirer, M. (2023). The impact of AI on developer productivity: Evidence from GitHub Copilot. arXiv preprint arXiv:2302.06590.
Tamayo, M., Rivas, A., & Gomez, C. (2024). Generative AI in digital marketing: Accelerating creative workflows and human-AI co-creation. Technological Forecasting and Social Change, 200, 123180. https://doi.org/10.1016/j.techfore.2023.123180
Vrontis, D., Christofi, M., Pereira, V., Tarba, S., Makrides, A., & Trichina, E. (2023). Artificial intelligence, robotics, advanced technologies, and human resource management: A systematic review. The International Journal of Human Resource Management, 34(6), 1237–1266. https://doi.org/10.1080/09585192.2022.2082218
License
Copyright (c) 2026 Mawardi, Dhenny Asmarazisa, Catur Fatchu Ukhriyawati
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
