AI Strategic Integration and Sustainable Organizational Performance

Authors

  • Erina Sovania Bina Nusantara University, Jakarta, Indonesia
  • Agus Salim IKPIA Perbanas, Jakarta, Indonesia
  • Fatchur Rohman Universitas Brawijaya, Malang, Indonesia

DOI:

https://doi.org/10.38035/dijemss.v7i6.7312

Keywords:

Artificial Intelligence, AI Strategic Integration, Augmented Management Capability, Intelligent Governance Systems, Sustainable Organizational Performance

Abstract

Artificial intelligence (AI) has evolved from an operational technology into a strategic organizational capability supporting managerial decision-making and governance. However, limited empirical research explains how strategic AI integration contributes to sustainable organizational performance through managerial and governance capabilities. This study examines the relationships among AI Strategic Integration, Augmented Management Capability, Intelligent Governance Systems, and Sustainable Organizational Performance using the Dynamic Capabilities perspective. A quantitative survey was conducted with 200 managerial-level respondents from organizations across multiple industries. Data were analyzed using Partial Least Squares Structural Equation Modeling with 5,000 bootstrap resamples. The findings show that AI Strategic Integration positively influences Augmented Management Capability, which strengthens Intelligent Governance Systems and subsequently contributes to Sustainable Organizational Performance. AI Strategic Integration also has a significant direct effect on Sustainable Organizational Performance and a significant sequential indirect effect through both mediators. The study demonstrates that AI creates organizational value through interconnected managerial and governance capabilities.

References

Agrawal, A., Gans, J., & Goldfarb, A. (2018). Prediction machines: The simple economics of artificial intelligence. Harvard Business Review Press.

Benbya, H., Davenport, T. H., & Pachidi, S. (2020). Artificial intelligence in organizations: Current state and future opportunities. MIS Quarterly Executive, 19(4), 299–313.

Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. W. W. Norton & Company.

Brynjolfsson, E., Rock, D., & Syverson, C. (2018). Artificial intelligence and the modern productivity paradox: A clash of expectations and statistics. In A. Agrawal, J. Gans, & A.

Goldfarb (Eds.), The economics of artificial intelligence: An agenda (pp. 23–57). University of Chicago Press.

Carter, W., & Wynne, K. T. (2024). Integrating artificial intelligence into team decision-making: Toward a theory of AI–human team effectiveness. European Management Review. Advance online publication. https://doi.org/10.1111/emre.12685

Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108–116.

Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., Schafer, B., Valcke, P., & Vayena, E. (2018). AI4People—An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28(4), 689–707. https://doi.org/10.1007/s11023-018-9482-5

Floridi, L., Cowls, J., King, T. C., & Taddeo, M. (2020). How to design AI for social good: Seven essential factors. Science and Engineering Ethics, 26(3), 1771–1796. https://doi.org/10.1007/s11948-020-00213-5

Fügener, A., Walzner, D. D., & Gupta, A. (2025). Roles of artificial intelligence in collaboration with humans: Automation, augmentation, and the future of work. Management Science, 72(1), 538–557. https://doi.org/10.1287/mnsc.2024.05684

Giachino, C., Cepel, M., Truant, E., & Bargoni, A. (2024). Artificial intelligence-driven decision making and firm performance: A quantitative approach. Management Decision, 63(10), 3454–3476. https://doi.org/10.1108/MD-10-2023-1966

Janssen, M., van der Voort, H., & Wahyudi, A. (2017). Factors influencing big data decision-making quality. Journal of Business Research, 70, 338–345. https://doi.org/10.1016/j.jbusres.2016.08.007

Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human–AI symbiosis in organizational decision making. Business Horizons, 61(4), 577–586. https://doi.org/10.1016/j.bushor.2018.03.007

Mikalef, P., Krogstie, J., Pappas, I. O., & Pavlou, P. A. (2020). Exploring the relationship between big data analytics capability and competitive performance: The mediating roles of dynamic and operational capabilities. Information & Management, 57(2), 103169. https://doi.org/10.1016/j.im.2019.05.004

Mökander, J., Morley, J., Taddeo, M., & Floridi, L. (2021). Ethics-based auditing of automated decision-making systems: Nature, scope, and limitations. Science and Engineering Ethics, 27(4), 44. https://doi.org/10.1007/s11948-021-00319-4

Neiroukh, S., Emeagwali, O. L., & Aljuhmani, H. Y. (2024). Artificial intelligence capability and organizational performance: Unraveling the mediating mechanisms of decision-making processes. Management Decision, 63(10), 3501–3532. https://doi.org/10.1108/MD-10-2023-1946

Papagiannidis, E., Mikalef, P., & Conboy, K. (2025). Responsible artificial intelligence governance: A review and research framework. The Journal of Strategic Information Systems, 34(2), 101885. https://doi.org/10.1016/j.jsis.2024.101885

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

Ransbotham, S., Kiron, D., LaFountain, B., Khodabandeh, S., & Chu, M. (2020). Expanding AI’s impact with organizational learning. MIT Sloan Management Review, 61(4), 1–9.

Teece, D. J. (2007). Explicating dynamic capabilities: The nature and microfoundations of (sustainable) enterprise performance. Strategic Management Journal, 28(13), 1319–1350. https://doi.org/10.1002/smj.640

Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533.

Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J.-F., Dubey, R., & Childe, S. J. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356–365. https://doi.org/10.1016/j.jbusres.2016.08.001

Downloads

Published

2026-09-01

How to Cite

Sovania, E., Salim, A., & Rohman, F. (2026). AI Strategic Integration and Sustainable Organizational Performance. Dinasti International Journal of Education Management and Social Science, 7(6), 5395–5409. https://doi.org/10.38035/dijemss.v7i6.7312

Issue

Vol. 7 No. 6 (2026): Dinasti International Journal of Education Management and Social Science (August - September 2026)

Section

Articles

License

Copyright (c) 2026 Erina Sovania, Agus Salim, Fatchur Rohman

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors who publish their manuscripts in this journal agree to the following conditions:

  1. The copyright on each article belongs to the author(s).
  2. The author acknowledges that the Dinasti International Journal of Education Management and Social Science (DIJEMSS) has the right to be the first to publish with a Creative Commons Attribution 4.0 International license (Attribution 4.0 International (CC BY 4.0).
  3. Authors can submit articles separately, arrange for the non-exclusive distribution of manuscripts that have been published in this journal into other versions (e.g., sent to the author's institutional repository, publication into books, etc.), by acknowledging that the manuscript has been published for the first time in the Dinasti International Journal of Education Management and Social Science (DIJEMSS).