AI-DRIVEN BUSINESS ANALYTICS FOR IMPROVING MANAGERIAL DECISION-MAKING AND ORGANIZATIONAL PERFORMANCE

Abstract

The rapid adoption of Artificial Intelligence (AI) and business analytics is transforming organizational decision-making by enabling managers to utilize large volumes of data for timely and informed business decisions. This study examines the role of AI-driven business analytics in improving managerial decision-making and organizational performance. The study proposes an integrated framework in which AI-driven business analytics capability influences organizational performance through enhanced managerial decision-making effectiveness. The framework considers the ability of AI-enabled analytics to provide predictive insights, identify business patterns, support risk assessment, and improve the quality and speed of managerial decisions. A quantitative research approach is proposed, using a structured questionnaire to collect data from managers and executives working in organizations that utilize AI and business analytics. The collected data will be analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to evaluate the proposed relationships and mediation effects. The study is expected to demonstrate that effective utilization of AI-driven analytics can strengthen managerial decision-making and contribute to improved organizational outcomes. The study contributes to the emerging literature on AI-enabled management by linking analytical capabilities, managerial decision-making, and organizational performance within a unified framework. The findings are expected to provide practical guidance for organizations seeking to develop data-driven and AI-enabled decision-making capabilities.