Data-Driven Decision Making And Artificial Intelligence In Strategic Management For Sustainable Business Growth

Authors

  • Dr. V. Srikanth Professor & Program Coordinator, Department Of MCA, Acharya Bangalore B School, Bengaluru, Karnataka
  • Dr Shweta Koparde Professor, Computer Science and Engineering, School of Engineering and Technology, DY Patil University, Ambi, Pune, Maharashtra
  • Dr. Kanika Mittal Assistant Professor, MMIM, Maharishi Markandeshwar (DEEMED TO BE UNIVERSITY) Mullana, Ambala (Haryana)
  • Dr. SDN Hayath Ali Associate Professor, Master of Computer Applications, Ballari Institute of Technology and Management, Ballari, Karnataka
  • Dr.J.Syed Nizamudeen Ahmed Assistant professor, Department Of CSE, St. Martin's Engineering College, Malkajgir, Secunderabad (Hyderabad), Telangana
  • Akansh Garg Director Array Research Pvt Ltd

DOI:

https://doi.org/10.29284/ew51d963

Keywords:

Data-Driven Decision Making; Artificial Intelligence; Strategic Management; Sustainable Business Growth; Predictive Analytics; Machine Learning; Business Intelligence.

Abstract

As technological development gains more momentum and competition in the global economy intensifies, organizations have resorted to data-driven decision-making (DDDM) and artificial intelligence (AI) as a way of strengthening the strategy management and ensuring the sustainability of a business growth. The strategic management based on the data improves the capacity of the organizations to mince actionable information out of the large heterogeneous and live time data sources so that these organizations can make the better forecast and effective operation and competitive advantage. At the same time, the artificial intelligence technologies, including machine learning, predictive analytics, natural language processing and intelligent automation, are changing the customary frameworks of decision-making and enhancing the abilities of all businesses to think strategically, distributing resources more efficiently, and modifying their business models. This paper will present an argument about the combination of DDDM and AI in strategic management and the possibility of the two issues to cooperate to ensure higher sustainability, value creation in the long-term, minimized risks, and organizational resilience. It deals with a more contemporary literature, and covers the state-of-the-art technique of analysis and relies as well as offers a conceptual side of the ways AI-based decision-systems may assist in strategic planning, performance measurements, stakeholder management and environmental, social and governance (ESG) activities. Besides it, the paper discusses real-life use in the industries as well as outlines the barriers to the adoption and also offers recommendations on the further development of the data-centric and AI-enabled strategic ecosystems. The findings spur the notion that these types of organizations that are open to data intelligence and AI-driven decision architecture are more likely to expand sustainably, at scale, and be future-craft in an extremely dynamic digital landscape.

References

1. Davenport, T. H., Analytics at Work: Smarter Decisions, Better Results. Harvard Business Press, 2010.

2. Provost, F. and Fawcett, T., Data Science for Business. O’Reilly Media, 2013.

3. Brynjolfsson, E. and McAfee, A., The Second Machine Age. W.W. Norton & Company, 2014.

4. Elkington, J., Cannibals with Forks: The Triple Bottom Line of 21st Century Business. Capstone, 1999.

5. Marr, B., Big Data in Practice. Wiley, 2016.

6. Shmueli, G. and Koppius, O., “Predictive Analytics in Information Systems Research,” MIS Quarterly, vol. 35, no. 3, pp. 553–572, 2011.

7. Russell, S. and Norvig, P., Artificial Intelligence: A Modern Approach, 4th ed., Pearson, 2021.

8. Wamba, S. F. et al., “Big Data Analytics and Firm Performance,” Information & Management, vol. 54, no. 3, pp. 329–344, 2017.

9. Chen, H., Chiang, R. H., and Storey, V., “Business Intelligence and Analytics,” MIS Quarterly, vol. 36, no. 4, pp. 1165–1188, 2012.

10. Goodfellow, I., Bengio, Y., and Courville, A., Deep Learning. MIT Press, 2016.

11. Bocken, N. et al., “A Literature Review on Sustainable Business Models,” Journal of Cleaner Production, vol. 65, pp. 42–56, 2014.

12. Porter, M. E. and Kramer, M. R., “Creating Shared Value,” Harvard Business Review, vol. 89, no. 1, pp. 62–77, 2011.

13. McKinsey Global Institute, Artificial Intelligence: The Next Digital Frontier?, McKinsey & Company, 2017.

14. Chui, M., Manyika, J., and Miremadi, M., “Where Machines Could Replace Humans—and Where They Can’t,” McKinsey Quarterly, 2016.

15. Raisch, S. and Krakowski, S., “Artificial Intelligence and Management: The Automation–Augmentation Paradox,” Academy of Management Review, vol. 46, no. 1, pp. 192–210, 2021.

16. Nocedal, J. and Wright, S., Numerical Optimization. Springer, 2006.

17. Hair, J. F. et al., Multivariate Data Analysis, 8th ed., Cengage Learning, 2019.

18. Eisenhardt, K. M., “Building Theories from Case Study Research,” Academy of Management Review, vol. 14, no. 4, pp. 532–550, 1989.

19. Yin, R. K., Case Study Research and Applications, 6th ed., Sage Publications, 2017.

20. [20] Teece, D. J., “Dynamic Capabilities and Strategic Management,” Strategic Management Journal, vol. 18, no. 7, pp. 509–533, 1997.

21. Kagermann, H., Wahlster, W., and Helbig, J., Recommendations for Implementing Industry 4.0, National Academy of Science and Engineering, 2013.

22. Kaplan, R. and Norton, D., The Balanced Scorecard. Harvard Business School Press, 1996.

23. IBM Institute for Business Value, The Enterprise Guide to AI Adoption, IBM, 2020.

24. OECD, AI in Business and Finance: Opportunities and Policy Considerations, OECD Publishing, 2021.

25. United Nations Global Compact, Accelerating Sustainability with AI and Data, UNGC, 2022.

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Published

2026-03-09

Issue

Section

Articles

How to Cite

[1]
D. V. Srikanth, D. S. Koparde, D. K. Mittal, D. S. H. Ali, D. N. Ahmed, and A. Garg, “Data-Driven Decision Making And Artificial Intelligence In Strategic Management For Sustainable Business Growth”, IJASIS, pp. 1296–1304, Mar. 2026, doi: 10.29284/ew51d963.

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