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All Journal EQUILIBIRIA Minda Baharu BENING Multidiciplinary Output Research for Actual and International Issue (Morfai Journal) International Journal of Social Science, Educational, Economics, Agriculture Research, and Technology (IJSET)
Dhenny Asmarazisa
Universitas Riau Kepulauan
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Social Sciences Other

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THE IMPACT OF GENERATIVE AL ON EMPLOYEE PRODUCTIVITY AND JOB REDESIGN IN MODERN ORGANIZATIONS Mawardi; Dhenny Asmarazisa; Catur Fatchu Ukhriyawati
International Journal of Social Science, Educational, Economics, Agriculture Research and Technology (IJSET) Vol. 5 No. 10 (2026): SEPTEMBER
Publisher : RADJA PUBLIKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.23134869

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.
Co-Authors Aditya Pratama Arnold AF Sihombing Asniati Audina Prastiwi Catur Fatchu Ukhriyawati Catur Fatchu Ukhriyawati Catur Fatchu Ukhriyawati Deby Andriany Dede Willis Silalahi Dian Molivia Diana Widyastika Edo Saputra Febiola Maria Tampubolon Firdaus Firdaus Hamta, Firdaus Firman Arifianto Gabriella Panjaitan Hans Cristian Ignasius Wahyudi Arianto Indria Sri Retnaningrum Iswahyudi Kamarul Azmi Kartika Simanjuntak Kurniawati Ningsih Larasati Iman Syalsabillah Linna Dhah Liawati Marhamah Mawardi Mirwan M Nunung N Nur Aye Nurhayati Nurhayati Nurlisa Rauda Agna Oktavianti Oktavianti Priyata Westraperdana Ramon Zamora Ridhian Wahyu Rizqy Samuel Eksaudi Tambunan Santia Rahmadani Sastra Tamami Siswandri Sriwati Sriwati Suwinto Syahrianto S Tibrani Tibrani Vera Alriani Wiwied Indartono Yudi Kurniadi Yulniarmah Zikri Helmi