The Impact of AI-Based Business Analytics on Corporate Strategic Decision Making

Penulis

  • Daquan Gosheven Financial Technology and Analytics Department, Naveen Jindal School of Management, Dallas, Texas, USA
  • Langundo Philly Financial Technology and Analytics Department, Naveen Jindal School of Management, Dallas, Texas, USA
  • Taila Taila Financial Technology and Analytics Department, Naveen Jindal School of Management, Dallas, Texas, USA

Kata Kunci:

Artificial Intelligence, Business Analytics, Strategic Decision Making, Predictive Analytics, Data-Driven Decision Making

Abstrak

The rapid growth of digital transformation has encouraged organizations to increasingly adopt AI-Based Business Analytics to enhance strategic decision making in highly dynamic and competitive business environments. Artificial Intelligence (AI) technologies, including machine learning, predictive analytics, and real-time data processing, enable organizations to generate faster, more accurate, and evidence-based insights that support executive decision making and improve organizational performance. This study aims to examine the impact of AI-Based Business Analytics on corporate strategic decision making. A quantitative research approach was employed using an explanatory cross-sectional survey design. Data were collected from 300 corporate executives, strategic planning managers, business analysts, senior managers, and AI implementation specialists working in organizations that have adopted AI-enabled business analytics. The data were analyzed using Partial Least Squares Structural Equation Modeling (SEM-PLS) to evaluate the measurement and structural models and test the proposed hypotheses. The findings indicate that AI-Based Business Analytics has a positive and statistically significant influence on corporate strategic decision making. The results also demonstrate that AI-generated insights enable organizations to make more informed and adaptive strategic decisions in increasingly uncertain business environments. The study concludes that adopting AI-Based Business Analytics strengthens organizational strategic decision-making capabilities, enhances sustainable competitive advantage, and supports long-term organizational performance. These findings contribute to the literature on Strategic Management, Artificial Intelligence, Business Analytics, and Decision Science while providing practical guidance for organizations seeking to maximize the strategic value of AI-driven analytics in corporate decision-making processes.

Referensi

Adadi, A., & Berrada, M. (2018). Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE Access, 6, 52138–52160.

Amershi, S., Begel, A., Bird, C., DeLine, R., Gall, H., Kamar, E., Nagappan, N., Nushi, B., & Zimmermann, T. (2019). Software engineering for machine learning: A case study. 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP), 291–300.

Brock, J. K.-U., & Von Wangenheim, F. (2019). Demystifying AI: What digital transformation leaders can teach you about realistic artificial intelligence. California Management Review, 61(4), 110–134.

Brown, T. A., & Moore, M. T. (2012). Confirmatory factor analysis. Handbook of Structural Equation Modeling, 361(2012), 379.

Chen, H. (2019). Success factors impacting artificial intelligence adoption: Perspective from the Telecom Industry in China. Old Dominion University.

Davenport, T., & Harris, J. (2017). Competing on analytics: Updated, with a new introduction: The new science of winning. Harvard Business Press.

Dearden, L., McIntosh, S., Myck, M., & Vignoles, A. (2002). The returns to academic and vocational qualifications in Britain. Bulletin of Economic Research, 54(3), 249–274.

Forth, J., Bewley, H., & Bryson, A. (2006). Small and medium-sized enterprises. Department of Trade and industry.

Kamis, A., Saibon, R. A., Yunus, F., Rahim, M. B., Herrera, L. M., & Montenegro, P. (2020). The SmartPLS analyzes approach in validity and reliability of graduate marketability instrument. Social Psychology of Education, 57(8), 987–1001.

Keding, C. (2021). Understanding the interplay of artificial intelligence and strategic management: four decades of research in review. Management Review Quarterly, 71(1), 91–134.

Lin, W.-K., Lin, S.-J., & Yang, T.-N. (2017). Integrated business prestige and artificial intelligence for corporate decision making in dynamic environments. Cybernetics and Systems, 48(4), 303–324.

Rai, N., & Thapa, B. (2015). A study on purposive sampling method in research. Kathmandu: Kathmandu School of Law, 5(1), 8–15.

Rea, L. M., & Parker, R. A. (2014). Designing and conducting survey research: A comprehensive guide. John Wiley & Sons.

Regmi, P. R., Waithaka, E., Paudyal, A., Simkhada, P., & Van Teijlingen, E. (2016). Guide to the design and application of online questionnaire surveys. Nepal Journal of Epidemiology, 6(4), 640.

Sarstedt, M., Hopkins, L., & Kuppelwieser, V. G. (2014). Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. European Business Review, 26(2), 106–121.

Selvarajan, G. (2021). Leveraging AI-enhanced analytics for industry-specific optimization: A strategic approach to transforming data-driven decision-making. International Journal of Enhanced Research In Science Technology & Engineering, 10(1), 78–84.

Spetzler, C., Winter, H., & Meyer, J. (2016). Decision quality. Wiley Online Library.

Srinivas, T. (2004). Tradition in transition: globalisation, priests, and ritual innovation in neighbourhood temples in Bangalore. Journal of Social and Economic Development.

Tsai, Y. (2011). Relationship between organizational culture, leadership behavior and job satisfaction. BMC Health Services Research, 11(1), 98.

Uzzaman, A., Kudapa, S. P., & Nijhum, A. M. (2021). Predictive Analytics For Improving Financial Forecasting And Risk Management In US Capital Markets. American Journal of Interdisciplinary Studies, 2(04), 69–100.

Wong, K. K.-K. (2019). Mastering partial least squares structural equation modeling (PLS-Sem) with Smartpls in 38 Hours. IUniverse.

Yahaya, M., Murtala, A. A., & Onukwube, H. N. (2019). Partial least square structural equation modeling (PLS-SEM): a note for beginners. International Journal of Environmental Studies and Safety Research, 4(4), 1–30.

Zhou, Q., & Wang, S. (2021). Study on the relations of supply chain digitization, flexibility and sustainable development—A moderated multiple mediation model. Sustainability, 13(18), 10043.

Diterbitkan

2025-09-30