Integrating Artificial Intelligence into Modern Management: Opportunities, Challenges, and Impacts in the Era of Digital Transformation
DOI:
https://doi.org/10.65310/rr425c59Abstract
This study examines the integration of intelligent technologies within modern management practices and highlights how digital transformation reshapes organizational structures and decision-making processes. Through a descriptive–qualitative approach supported by thematic synthesis of reputable scientific publications, international reports, and empirical findings from 2020–2025, this research identifies key opportunities, challenges, and managerial implications emerging from the adoption of advanced data-driven systems. The findings reveal that intelligent automation enhances managerial accuracy, operational efficiency, and strategic responsiveness by enabling real-time processing of organizational information. However, successful implementation depends heavily on employee readiness, structured governance, and the ability of leaders to develop strong analytical competencies. The study also notes that reliance on predictive systems requires continuous evaluation to minimize bias and ensure inclusive decision outcomes. Empirical evidence from global datasets further demonstrates the growing adoption of automated decision-making tools across various managerial functions. Overall, the research concludes that organizations capable of aligning technological capabilities with adaptive management strategies will strengthen their competitive advantage and improve long-term performance in the digital transformation era
Downloads
References
Ali, I., Warraich, N. F., & Butt, K. (2025). Acceptance and use of artificial intelligence and AI-based applications in education: A meta-analysis and future direction. Information Development, 41(3), 859–874. https://doi.org/10.1177/02666669241257206
Alsheibani, S., Messom, C., & Cheung, Y. (2020). Re-thinking the competitive landscape of artificial intelligence. Dipresentasikan pada Hawaii International Conference on System Sciences. https://doi.org/10.24251/HICSS.2020.718
Chang, C., Jen, H., & Su, W. (2022). Trends in artificial intelligence in nursing: Impacts on nursing management. Journal of Nursing Management, 30(8), 3644–3653. https://doi.org/10.1111/jonm.13770
Dabbous, A., Aoun Barakat, K., & Merhej Sayegh, M. (2022). Enabling organizational use of artificial intelligence: An employee perspective. Journal of Asia Business Studies, 16(2), 245–266. https://doi.org/10.1108/JABS-09-2020-0372
Damioli, G., Van Roy, V., & Vertesy, D. (2021). The impact of artificial intelligence on labor productivity. Eurasian Business Review, 11(1), 1–25. https://doi.org/10.1007/s40821-020-00172-8
Dubey, R., Gunasekaran, A., Childe, S. J., Bryde, D. J., Giannakis, M., Foropon, C., … Hazen, B. T. (2020). Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organisations. International Journal of Production Economics, 226, 107599. https://doi.org/10.1016/j.ijpe.2019.107599
El Hajj, M., & Hammoud, J. (2023). Unveiling the influence of artificial intelligence and machine learning on financial markets: A comprehensive analysis of AI applications in trading, risk management, and financial operations. Journal of Risk and Financial Management, 16(10), 434. https://doi.org/10.3390/jrfm16100434
Fanelli, S., Pratici, L., Salvatore, F. P., Donelli, C. C., & Zangrandi, A. (2023). Big data analysis for decision-making processes: Challenges and opportunities for the management of health-care organizations. Management Research Review, 46(3), 369–389. https://doi.org/10.1108/MRR-09-2021-0648
Flak, O. (2024). Online managerial tools as research tools to apply artificial management: Results of research. International Conference on AI Research, 4(1), 115–125. https://doi.org/10.34190/icair.4.1.3190
Foroughi, B., Naghmeh-Abbaspour, B., Wen, J., Ghobakhloo, M., Al-Emran, M., & Al-Sharafi, M. A. (2025). Determinants of generative AI in promoting green purchasing behavior: A hybrid partial least squares–artificial neural network approach. Business Strategy and the Environment, 34(4), 4072–4094. https://doi.org/10.1002/bse.4186
Gong, Q., Fan, D., & Bartram, T. (2025). Integrating artificial intelligence and human resource management: A review and future research agenda. The International Journal of Human Resource Management, 36(1), 103–141. https://doi.org/10.1080/09585192.2024.2440065
Grandinetti, R. (2020). How artificial intelligence can change the core of marketing theory. Innovative Marketing, 16(2), 91–103. https://doi.org/10.21511/im.16(2).2020.08
Harfizar, Wicaksono, M. W., Hakim, M. B., Wijaya, F. H., Saleh, T., & Sana, E. (2024). Analyzing the influence of artificial intelligence on digital innovation: A SmartPLS approach. IAIC Transactions on Sustainable Digital Innovation, 5(2), 108–116. https://doi.org/10.34306/itsdi.v5i2.659
Hassan, D. O., & Hassan, B. A. (2025). A comprehensive systematic review of machine learning in the retail industry: Classifications, limitations, opportunities, and challenges. Neural Computing and Applications, 37(4), 2035–2070. https://doi.org/10.1007/s00521-024-10869-w
Impact of artificial intelligence (AI) enabled management information system (MIS) in managerial decision making: An empirical study of leading business organisation. (2024). Journal of Informatics Education and Research. https://doi.org/10.52783/jier.v4i2.919
Jin, B. E., & Shin, D. C. (2021). The power of 4th industrial revolution in the fashion industry: What, why, and how has the industry changed? Fashion and Textiles, 8(1), 31. https://doi.org/10.1186/s40691-021-00259-4
Koenecke, A., Nam, A., Lake, E., Nudell, J., Quartey, M., Mengesha, Z., … Goel, S. (2020). Racial disparities in automated speech recognition. Proceedings of the National Academy of Sciences, 117(14), 7684–7689. https://doi.org/10.1073/pnas.1915768117
Li, Z. (2024). The impact of artificial intelligence technology innovation on economic development—From the perspective of generative AI products. Journal of Education, Humanities and Social Sciences, 27, 565–574. https://doi.org/10.54097/8eb1ks76
Lim, W. M. (2025). What is qualitative research? An overview and guidelines. Australasian Marketing Journal, 33(2), 199–229. https://doi.org/10.1177/14413582241264619
Mendy, J., Jain, A., & Thomas, A. (2024). Artificial intelligence in the workplace: Challenges, opportunities and HRM framework. Journal of Managerial Psychology. https://doi.org/10.1108/JMP-05-2024-0388
Na, K. (2024). The role of artificial intelligence in China’s manufacturing industry: Reality and prospects. Highlights in Business, Economics and Management, 39, 252–258. https://doi.org/10.54097/p62r3m68
Osadcha, K. P., & Shumeiko, N. V. (2024). Artificial intelligence, the labor market, and education for sustainable development: The points of intersection. IOP Conference Series: Earth and Environmental Science, 1415(1), 012015. https://doi.org/10.1088/1755-1315/1415/1/012015
Prasetya, F., Fortuna, A., Samala, A. D., Latifa, D. K., Andriani, W., Gusti, U. A., … Cabanillas García, J. L. (2025). Harnessing artificial intelligence to revolutionize vocational education: Emerging trends, challenges, and contributions to SDGs 2030. Social Sciences & Humanities Open, 11, 101401. https://doi.org/10.1016/j.ssaho.2025.101401
Rane, N. L., Desai, P., & Choudhary, S. (2024). Challenges of implementing artificial intelligence for smart and sustainable industry: Technological, economic, and regulatory barriers. Dalam N. L. Rane (Ed.), Artificial intelligence and industry in Society 5.0. Deep Science Publishing. https://doi.org/10.70593/978-81-981271-1-2_5
Secundo, G., Spilotro, C., Gast, J., & Corvello, V. (2025). The transformative power of artificial intelligence within innovation ecosystems: A review and a conceptual framework. Review of Managerial Science, 19(9), 2697–2728. https://doi.org/10.1007/s11846-024-00828-z
Tenakwah, E. S., & Watson, C. (2025). Embracing the AI/automation age: Preparing your workforce for humans and machines working together. Strategy & Leadership, 53(1), 32–48. https://doi.org/10.1108/SL-05-2024-0040
Thakur, J., & Kushwaha, B. P. (2024). Artificial intelligence in marketing research and future research directions: Science mapping and research clustering using bibliometric analysis. Global Business and Organizational Excellence, 43(3), 139–155. https://doi.org/10.1002/joe.22233
Theoharidis, A. F., Guillén, D. A., & Lopes, H. (2023). Deep learning models for inflation forecasting. Applied Stochastic Models in Business and Industry, 39(3), 447–470. https://doi.org/10.1002/asmb.2757
Tu, T., Schaekermann, M., Palepu, A., Saab, K., Freyberg, J., Tanno, R., … Natarajan, V. (2025). Towards conversational diagnostic artificial intelligence. Nature, 642(8067), 442–450. https://doi.org/10.1038/s41586-025-08866-7
Uddin, M. K. S., & Hossan, K. M. R. (2024). A review of implementing AI-powered data warehouse solutions to optimize big data management and utilization. Academic Journal on Business Administration, Innovation & Sustainability, 4(3), 66–78. https://doi.org/10.69593/ajbais.v4i3.92
Vuković, D. B., Dekpo-Adza, S., & Matović, S. (2025). AI integration in financial services: A systematic review of trends and regulatory challenges. Humanities and Social Sciences Communications, 12(1), 562. https://doi.org/10.1057/s41599-025-04850-8
Wang, X. (2023). Research on the realization path of inclusive employment structure under the application of artificial intelligence. Socio Economy and Policy Studies, 3(2), 124–127. https://doi.org/10.26480/seps.02.2023.124.127
Yang, Y., Zhang, Y., Sun, D., He, W., & Wei, Y. (2025). Navigating the landscape of AI literacy education: Insights from a decade of research.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Mohammad Abdul Aziz Alwahedi, Durrin Ni’am, Mohammad Firmansyah (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
