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.