The Transformation of Knowledge Work in The Age of Generative AI: Evidence from Organizations in Banten, Indonesia

Authors

  • Sri Lestari Universitas Insan Pembangunan Indonesia, Indonesia
  • Agus Santhuso Universitas Islam Assyafiiyah, Indonesia
  • Etty Susilowati Universitas Pertanian Bogor, Indonesia

DOI:

https://doi.org/10.5555/ijosmas.v7i3.620

Keywords:

Adaptive learning, generative AI, human-AI collaboration, knowledge work, organizational transformation

Abstract

Background: The rapid adoption of generative artificial intelligence (GenAI) technologies including large language models (LLMs) and AI-enabled knowledge systems presents a fundamental transformation of knowledge work in contemporary organizations. Little research has explored how these technologies reshape the nature of knowledge creation, organizational learning processes, and professional capabilities in emerging economy contexts. Research Objective: To develop an integrated understanding of how Generative AI transforms knowledge work within organizations operating in Banten Province, Indonesia. Research Method: This qualitative descriptive study employed multi-case inquiry involving 20 participants across 5 organizations, utilizing semi-structured interviews, organizational document analysis, and observation protocols. Thematic analysis integrated knowledge-based view, dynamic capability theory, and organizational learning frameworks. Participants: Senior managers, HR professionals, knowledge workers, digital transformation leaders, and AI implementation specialists from diverse sectors. Main Findings: GenAI transforms knowledge work through four primary mechanisms: (1) augmentation of cognitive labor, shifting employees toward higher-order thinking and judgment; (2) reorganization of knowledge creation processes enabling rapid synthesis and pattern recognition; (3) evolution of organizational learning from episodic to continuous adaptive learning; (4) emergence of new workforce competencies centered on human-AI collaboration, critical evaluation, and adaptive expertise. Novelty: This research adopts a knowledge work transformation lens rather than focusing on technological adoption or productivity metrics, grounded in human-AI collaboration theory, and situated within an emerging economy context. Practical Implications: Organizations must strategically redesign work processes, invest in adaptive workforce development, and create governance frameworks balancing efficiency gains with meaningful human contribution.

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References

Bankins, S., Ocampo, A. C., Marrone, M., Restubog, S. L. D., & Woo, S. E. (2023). A multilevel review of artificial intelligence in organizations: Implications for organizational behavior research and practice. Journal of Organizational Behavior, 44(8), 1455-1470. https://doi.org/10.1002/job.2735

Brown, O., Davison, R. M., Decker, S., Ellis, D. A., Faulconbridge, J., Gore, J., ... & Zilber, T. B. (2024). Theory‐driven perspectives on generative artificial intelligence in business and management. British Journal of Management, 35(2), 415-431. https://doi.org/10.1111/1467-8551.12788

Budhwar, P., Chowdhury, S., Wood, G., Aguinis, H., Bamber, G. J., Beltran, J. R., ... & Varma, A. (2023). Human resource management in the age of generative artificial intelligence: Perspectives and research directions on ChatGPT. Human Resource Management Journal, 33(4), 606-659. https://doi.org/10.1111/1748-8583.12524

Cao, G., Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2021). Understanding managers' attitudes and behavioral intentions towards using artificial intelligence for organizational decision-making. Technovation, 106, 102312. https://doi.org/10.1016/j.technovation.2021.102312

Chatterjee, S., Chaudhuri, R., Vrontis, D., & Giovando, G. (2023). Digital workplace and organization performance: Moderating role of digital leadership capability. Journal of Innovation & Knowledge, 8(2), 100334. https://doi.org/10.1016/j.jik.2023.100334

Dave, D. M., & Mandvikar, S. (2023). Augmented intelligence: Human-AI collaboration in the era of digital transformation. International Journal of Engineering and Advanced Studies, 8(6), 58-67. https://doi.org/10.33564/ijeast.2023.v08i06.003

Dąbrowska, J., Almpanopoulou, A., Brem, A., Chesbrough, H., Cucino, V., Di Minin, A., ... & Nylund, P. A. (2022). Digital transformation, for better or worse: A critical multi-level research agenda. R&D Management, 52(2), 207-227. https://doi.org/10.1111/radm.12531

Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E., Jeyaraj, A., Kar, A. K., ... & Wright, R. (2023). Opinion paper: "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

Feuerriegel, S., Hartmann, J., Janiesch, C., & Zschech, P. (2023). Generative AI. Business & Information Systems Engineering, 65(5), 577-580. https://doi.org/10.1007/s12599-023-00834-7

Grabowska, S., Saniuk, S., & Gajdzik, B. (2022). Industry 5.0: Improving humanization and sustainability of Industry 4.0. Scientometrics, 131(5), 3201-3223. https://doi.org/10.1007/s11192-022-04370-1

Gupta, M., Akiri, C., Aryal, K., Parker, E., & Praharaj, L. (2023). From ChatGPT to ThreatGPT: Impact of generative AI in cybersecurity and privacy. IEEE Access, 11, 80218-80245. https://doi.org/10.1109/access.2023.3300381

Hanelt, A., Bohnsack, R., Marz, D., & Marante, C. (2020). A systematic review of the literature on digital transformation: Insights and implications for strategy and organizational change. Journal of Management Studies, 58(5), 1159-1197. https://doi.org/10.1111/joms.12639

Jarrahi, M. H., Newlands, G., Lee, M. K., Wolf, C. T., Kinder, E., & Sutherland, W. (2021). Algorithmic management in a work context. New Technology, Work and Employment, 36(3), 231-250. https://doi.org/10.1177/20539517211020332

Jöhnk, J., Weißert, M., & Wyrtki, K. (2020). Ready or not, AI comes—An interview study of organizational AI readiness factors. Business & Information Systems Engineering, 63(1), 5-20. https://doi.org/10.1007/s12599-020-00676-7

Kaczorowska-Spychalska, D., Kotula, N., Mazurek, G., & Sułkowski, Ł. (2024). Generative AI as source of change of knowledge management paradigm. Entrepreneurship and Sustainability Issues, 20(1), 111-128. https://doi.org/10.14254/1795-6889.2024.20-1.7

Khan Raiaan, M. A., Mukta, M. S. H., Fatema, K., Fahad, N. M., Sakib, S., Jannat Mim, M. M., ... & Ali, M. E. (2024). A review on large language models: Architectures, applications, taxonomies, open issues and challenges. IEEE Access, 12, 26839-26874. https://doi.org/10.1109/access.2024.3365742

Kolbjørnsrud, V. (2023). Designing the intelligent organization: Six principles for human-AI collaboration. Organizational Dynamics, 52(4), 100999. https://doi.org/10.1177/00081256231211020

Korzyński, P., Mazurek, G., Altmann, A., Ejdys, J., Kazlauskaitė, R., Paliszkiewicz, J., Wach, K., & Ziemba, E. (2023). Generative artificial intelligence as a new context for management theories: Analysis of ChatGPT. Central European Management Journal, 31(2), 280-294. https://doi.org/10.1108/cemj-02-2023-0091

Korteling, J. E., van de Boer-Visschedijk, G. C., Blankendaal, R., Boonekamp, R., & Eikelboom, A. R. (2021). Human- versus artificial intelligence. Frontiers in Artificial Intelligence, 4, 622364. https://doi.org/10.3389/frai.2021.622364

Lee, H., Sarkar, A., Tankelevitch, L., Drosos, I., Rintel, S., Banks, R., & Wilson, N. (2025). The impact of generative AI on critical thinking: Self-reported reductions in cognitive effort and confidence effects from a survey of knowledge workers. Proceedings of the CHI Conference on Human Factors in Computing Systems, 1-19. https://doi.org/10.1145/3706598.3713778

Longo, F., Padovano, A., & Umbrello, S. (2020). Value-oriented and ethical technology engineering in Industry 5.0: A human-centric perspective for the design of the factory of the future. Applied Sciences, 10(12), 4182. https://doi.org/10.3390/app10124182

Mariani, M. M., Machado, I., Magrelli, V., & Dwivedi, Y. K. (2022). Artificial intelligence in innovation research: A systematic review, conceptual framework, and future research directions. Technovation, 117, 102623. https://doi.org/10.1016/j.technovation.2022.102623

Markauskaitė, L., Marrone, R., Poquet, O., Knight, S., Martínez-Maldonado, R., Howard, S., ... & Siemens, G. (2022). Rethinking the entwinement between artificial intelligence and human learning: What capabilities do learners need for a world with AI? Computers and Education: Artificial Intelligence, 3, 100056. https://doi.org/10.1016/j.caeai.2022.100056

Mirbabaie, M., Brünker, F., Frick, N., & Stieglitz, S. (2021). The rise of artificial intelligence – understanding the AI identity threat at the workplace. Electronic Markets, 32(1), 129-147. https://doi.org/10.1007/s12525-021-00496-x

Morandini, S., Fraboni, F., De Angelis, M., Puzzo, G., Giusino, D., & Pietrantoni, L. (2023). The impact of artificial intelligence on workers' skills: Upskilling and reskilling in organisations. Education Sciences, 13(1), 15. https://doi.org/10.28945/5078

Peres, R. S., Jia, X., Lee, J., Sun, K., Colombo, A. W., & Barata, J. (2020). Industrial artificial intelligence in Industry 4.0 - Systematic review, challenges and outlook. IEEE Access, 8, 220121-220139. https://doi.org/10.1109/access.2020.3042874

Poláková, M., Suleimanová, J. H., Madzík, P., Copuš, L., Molnárová, I., & Polednová, J. (2023). Soft skills and their importance in the labour market under the conditions of Industry 5.0. Heliyon, 9(7), e18670. https://doi.org/10.1016/j.heliyon.2023.e18670

Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation–augmentation paradox. Academy of Management Review, 46(1), 192-210. https://doi.org/10.5465/amr.2018.0072

Richey, R. G., Chowdhury, S., Davis-Sramek, B., Giannakis, M., & Dwivedi, Y. K. (2023). Artificial intelligence in logistics and supply chain management: A primer and roadmap for research. Journal of Business Logistics, 44(4), 512-527. https://doi.org/10.1111/jbl.12364

Sima, V., Gheorghe, I. G., Subić, J., & Nancu, D. (2020). Influences of the Industry 4.0 revolution on the human capital development and consumer behavior: A systematic review. Sustainability, 12(10), 4035. https://doi.org/10.3390/su12104035

Spitzer, P., Holstein, J., Hemmer, P., Vossing, M., Kuhl, N., Martin, D., & Satzger, G. (2024). Human delegation behavior in human-AI collaboration: The effect of contextual information. ACM Transactions on Computer-Human Interaction, 31(2), 1-30. https://doi.org/10.1145/3710999

Sun, J., Yang, J., Zhou, G., Jin, Y., & Gong, J. (2024). Understanding human-AI collaboration in music therapy through co-design with therapists. Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems, 1-15. https://doi.org/10.1145/3613904.3642764

Torre, D., Colapinto, C., Durosini, I., & Triberti, S. (2021). Team formation for human-artificial intelligence collaboration in the workplace: A goal programming model to foster organizational change. IEEE Transactions on Engineering Management, 69(3), 1033-1045. https://doi.org/10.1109/TEM.2021.3077195

Trenerry, B., Chng, S., Wang, Y., Suhaila, Z. S., Lim, S. S., Lu, H., & Oh, P. H. (2021). Preparing workplaces for digital transformation: An integrative review and framework of multi-level factors. Frontiers in Psychology, 12, 620766. https://doi.org/10.3389/fpsyg.2021.620766

Wach, K., Duong, C. D., Ejdys, J., Kazlauskaitė, R., Korzyński, P., Mazurek, G., Paliszkiewicz, J., & Ziemba, E. (2023). The dark side of generative artificial intelligence: A critical analysis of controversies and risks of ChatGPT. Entrepreneurship and Business Economics Review, 11(1), 21-41. https://doi.org/10.15678/eber.2023.110201

Wang, L., Ma, C., Feng, X., Zhang, Z., Yang, H., Zhang, J., ... & Wen, J. R. (2024). A survey on large language model based autonomous agents. Frontiers of Computer Science, 18(3), 186358. https://doi.org/10.1007/s11704-024-40231-1

Yenduri, G., Ramalingam, M., Chemmalar Selvi, G., Supriya, Y., Srivastava, G., Maddikunta, P. K. R., ... & Gadekallu, T. R. (2024). GPT (generative pre-trained transformer)—A comprehensive review on enabling technologies, potential applications, emerging challenges, and future directions. IEEE Access, 12, 26839-26873. https://doi.org/10.1109/access.2024.3389497

Yun, B., Feng, D., Chen, A. S., Nikzad, A., & Salehi, N. (2025). Generative AI in knowledge work: Design implications for data navigation and decision-making. Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, 1-20. https://doi.org/10.1145/3706598.3713337

Zirar, A., Ali, S. I., & Islam, N. (2023). Worker and workplace artificial intelligence (AI) coexistence: Emerging themes and research agenda. Technovation, 123, 102747. https://doi.org/10.1016/j.technovation.2023.102747

Published

2026-06-05

How to Cite

Lestari, S., Santhuso, A. ., & Susilowati, E. . (2026). The Transformation of Knowledge Work in The Age of Generative AI: Evidence from Organizations in Banten, Indonesia. International Journal of Social and Management Studies, 7(3), 23–37. https://doi.org/10.5555/ijosmas.v7i3.620