Predictive Analytics Model for AI-Enhanced Decision Support in 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.