Hybrid analytics architecture: integrating traditional BI with AI-powered insights

Shah, Kushal (2025) Hybrid analytics architecture: integrating traditional BI with AI-powered insights. World Journal of Advanced Engineering Technology and Sciences, 15 (1). pp. 1283-1291. ISSN 2582-8266

The rapid evolution of data analytics has led to the convergence of traditional Business Intelligence (BI) systems with Artificial Intelligence (AI)-driven insights, resulting in a hybrid analytics architecture. This paper explores the integration of AI capabilities within conventional BI frameworks to enhance decision-making, predictive analytics, and operational efficiency. We propose a structured approach that leverages machine learning models alongside traditional BI reporting to bridge the gap between historical analysis and real-time, data-driven insights. The study evaluates the effectiveness of this hybrid model through comparative analysis and case studies, highlighting its advantages over standalone BI and AI approaches. Findings suggest that organizations adopting hybrid analytics architectures can achieve enhanced scalability, agility, and accuracy in their decision-making processes.