Predictive Analytics Model for AI-Enhanced Decision Support in Corporate Management

Hazirah Bee Yusof Ali; Zhang Jian Gang

doi:10.57159/jcmm.4.6.25232

Authors

Hazirah Bee Yusof Ali Faculty of Information Technology, City University, Petaling Jaya, Selangor, Malaysia 46100
Zhang Jian Gang City Graduate School, City University, Petaling Jaya, Selangor, Malaysia 46100

DOI:

https://doi.org/10.57159/jcmm.4.6.25232

Keywords:

Predictive Analytics, Decision Support Systems, Artificial Intelligence, Corporate Management

Abstract

AI and predictive analytics have revolutionized corporate management by replacing guesswork with facts. A comprehensive literature review reveals that AI-enhanced decision support systems are increasingly incorporating machine learning predictive analytics models. This paper summarizes research from academic studies and case studies performed by businesses to demonstrate how predictive analytics becomes an integrated part of planning corporate strategy, allocating resources among departments, and ensuring administrative efficiency. This study focuses on classifiers, which are fundamental machine learning techniques for predicting and simplifying complex decision-making processes. Such techniques include neural networks, regression analysis, decision trees, and others. The authors explain the various aspects to consider when implementing AI-driven solutions successfully, including data quality, model interpretability, and ethics. The findings show that organizations adopting predictive analytics report measurable improvements, including up to 15% reduction in employee turnover, 20 -30% improvement in risk mitigation, 25% sales growth, and 40% reduction in operational inefficiencies when integrated with ERP systems. The study also examines how predictive analytics is affecting various disciplines, such as risk management, market trend forecasting, and employee performance appraisal, by analyzing specific real-life examples. Findings suggest that the implementation of real-time analytics in ERP systems has the potential to enhance strategic decision-making significantly. The review also reveals gaps in the literature and contributes to future research by highlighting the need to scale solutions to problems and applications across industries.

References

A. Aldoseri, K. N. Al-Khalifa, and A. M. Hamouda, “AI-powered innovation in digital transformation: Key pillars and industry impact,” Sustainability, vol. 16, no. 5, p. 1790, 2024.

L. Hallo and T. Nguyen, “Holistic view of intuition and analysis in leadership decision-making and problem-solving,” Administrative Sciences, vol. 12, no. 1, p. 4, 2021.

C. Newman, D. Edwards, I. Martek, J. Lai, W. D. Thwala, and I. Rillie, “Industry 4.0 deployment in the construction industry: A bibliometric literature review and UK-based case study,” Smart and Sustainable Built Environment, vol. 10, no. 4, pp. 557–580, 2021.

N. K. Rajagopal, N. I. Qureshi, S. Durga, E. H. Ramirez Asis, R. M. Huerta Soto, S. K. Gupta, and S. Deepak, “Future of business culture: An artificial intelligence-driven digital framework for organization decision-making process,” Complexity, vol. 2022, Art. no. 7796507, 2022.

T. Heilig and I. Scheer, Decision Intelligence: Transform Your Team and Organization with AI-Driven Decision-Making. Hoboken, NJ, USA: John Wiley & Sons, 2023.

M. M. Rahaman, S. Rani, M. R. Islam, and M. M. R. Bhuiyan, “Machine learning in business analytics: Advancing statistical methods for data-driven innovation,” Journal of Computer Science and Technology Studies, vol. 5, no. 3, pp. 104–111, 2023.

N. L. Rane, S. K. Mallick, Ö. Kaya, and J. Rane, Applied Machine Learning and Deep Learning: Architectures and Techniques. Deep Science Publishing, 2024.

T. R. Akash, J. Reza, and M. A. Alam, “Evaluating financial risk management in corporation financial security systems,” World Journal of Advanced Research and Reviews, vol. 23, no. 1, pp. 2203–2213, 2024.

M. R. Khatri, “Integration of natural language processing, self-service platforms, predictive maintenance, and prescriptive analytics for cost reduction, personalization, and real-time insights in customer service and operational efficiency,” International Journal of Information and Cybersecurity, vol. 7, no. 9, pp. 1–30, 2023.

J. P. Bharadiya, “Machine learning and AI in business intelligence: Trends and opportunities,” International Journal of Computer, vol. 48, no. 1, pp. 123–134, 2023.

A. A. Adesina, T. V. Iyelolu, and P. O. Paul, “Leveraging predictive analytics for strategic decision-making: Enhancing business performance through data-driven insights,” World Journal of Advanced Research and Reviews, vol. 22, no. 3, pp. 1927–1934, 2024.

Y. Niu, L. Ying, J. Yang, M. Bao, and C. Sivaparthipan, “Organizational business intelligence and decision making using big data analytics,” Information Processing and Management, vol. 58, no. 6, Art. no. 102725, 2021.

C. Aliferis and G. Simon, “Overfitting, underfitting, and general model overconfidence and under-performance pitfalls and best practices in machine learning and AI,” in Artificial Intelligence and Machine Learning in Health Care and Medical Sciences: Best Practices and Pitfalls. Cham, Switzerland: Springer, 2024, pp. 477–524.

R. T. Potla, “Explainable AI (XAI) and its role in ethical decision-making,” Journal of Science and Technology, vol. 2, no. 4, pp. 151–174, 2021.

J. E. Korteling, G. L. Paradies, and J. P. Sassen-van Meer, “Cognitive bias and how to improve sustainable decision making,” Frontiers in Psychology, vol. 14, Art. no. 1129835, 2023.