January 1, 2025

Role of AI and Data Science in Data-Driven Decision Making for It Business Intelligence: A Systematic Literature Review

Puntos clave

A significant shift toward AI-enabled systems enhances data-driven decision making in business intelligence, improving operational efficiency.
Results from 156 studies illustrate advancements in predictive and prescriptive analytics, indicating profound impacts on strategic agility in IT enterprises.
The review utilizes the PRISMA framework to synthesize findings on the integration of AI in business frameworks, highlighting emerging trends.
While substantial progress is noted, challenges remain in ethical governance and algorithmic transparency, underscoring the need for unified frameworks.

Resumen

This systematic literature review examines the role of Artificial Intelligence (AI) and Data Science in enhancing data-driven decision-making within Business Intelligence (BI) systems for Information Technology (IT) enterprises, with the objective of identifying dominant research themes, methodological approaches, application domains, and existing gaps. Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, a total of 156 peer-reviewed studies published between 2010 and 2024 were systematically identified, screened, and analyzed from leading academic databases and reputable industry sources. The review synthesizes findings across key thematic areas, including predictive and prescriptive analytics, natural language processing for unstructured data, real-time and streaming analytics, governance and ethical considerations, cross-functional accessibility, and comprehensive AI-BI integration frameworks. Results indicate a clear shift from traditional descriptive BI toward proactive, AI-enabled systems capable of delivering timely, contextually relevant, and actionable insights, with significant advancements in automation, scalability, and inclusivity for both technical and non-technical stakeholders. The analysis also reveals that while technical progress has been substantial, challenges remain in areas such as ethical governance, algorithmic transparency, bias mitigation, and cross-border data compliance. This review contributes to the field by providing a consolidated view of current advancements, comparing them with earlier research trends, and identifying persistent gaps that hinder the full realization of AI and Data Science capabilities in BI environments. The findings underscore the strategic importance of adopting unified, ethically grounded, and scalable AI-BI frameworks to enhance operational efficiency, strategic agility, and competitive advantage in modern IT business contexts.

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