A systematic review of AI driven enterprise data analytics frameworks for decision intelligence

Abstract

The rapid evolution of artificial intelligence (AI) has significantly transformed enterprise data analytics, enabling organizations to transition from descriptive reporting to predictive and prescriptive decision intelligence systems. This study presents a systematic review of AI-driven enterprise data analytics frameworks, focusing on their architectural design, functional components, and impact on organizational decision-making. The review synthesizes existing literature across domains such as machine learning, business intelligence, data governance, and real-time analytics to identify key trends, methodological approaches, and implementation challenges. Central to the analysis is the integration of AI techniques—including deep learning, natural language processing, and reinforcement learning—into enterprise analytics pipelines to enhance data processing, pattern recognition, and automated decision support. The paper critically examines how these frameworks incorporate data governance, scalability, interoperability, and explainability to ensure reliable and transparent decision outcomes. Furthermore, it evaluates the role of decision intelligence systems in bridging the gap between data insights and strategic actions through feedback-driven optimization and continuous learning mechanisms. The findings reveal that while AI-driven analytics frameworks significantly improve decision accuracy, operational efficiency, and risk management, they also introduce challenges related to data quality, model interpretability, and ethical considerations. The review highlights the importance of unified architectures that align data governance, analytics processes, and decision intelligence layers to achieve sustainable enterprise transformation. Additionally, the study identifies emerging research directions, including the integration of edge computing, federated learning, and explainable AI for enhanced performance and compliance. This systematic review contributes to the field by providing a comprehensive understanding of AI-enabled analytics frameworks and offering a structured foundation for future research and practical implementation in enterprise environments.