The Role of Business Intelligence in Enhancing Organizational Decision-Making Processes

Abstract

This paper discusses how business intelligence (BI) systems, specifically those enriched through artificial intelligence (AI) and data analytics, play a role in organizational decision-making. With organizations going increasingly data-driven, the coupling of real- time dashboards, predictive modeling, and machine learning has transformed managerial practice. This thesis examines how BI aids decision-making processes, determines perceived benefits, and takes into account barriers to good implementation. An analytical meta- synthesis involving 34 peer-reviewed publications from 2018 through 2024 using thematic analysis centered on strategic benefits, operational effectiveness, and organizational facilitators or barriers to BI adoption was undertaken. Findings reveal BI facilitates better forecasting, real-time support for decisions, and automation of operations but is hampered by data silos, user resistance, and expense. The research discovered that BI implementation success is dependent on organizational culture, managerial mindsets, user understanding, and phased deployment approaches. Theoretical anchors comprise Rajagopal’s AI-Driven Framework and Galbraith’s Information Processing Theory, explicating how information processing and technology interface with decision quality and vice versa. Ultimately, the research highlights that technology is not enough; organizational capability to understand and act on intelligence is the actual source of BI value. The research presented here contributes actionable ideas for policymakers, strategists, and IT executive decision-makers who want to get the maximum out of BI.