Study of Managing Database Systems with Native AI

Abstract

The integration of artificial intelligence into database management systems has evolved from supplementary tools to native architectural components, fundamentally transforming how organizations store, retrieve, and optimize data operations. This study investigates the emerging paradigm of AI-native database systems, examining their capabilities in autonomous optimization, intelligent query processing, and adaptive resource management. Through comprehensive analysis of current implementations and architectural frameworks, we explore how native AI integration differs from traditional AI-enhanced databases. The research evaluates key performance metrics including query latency reduction, automated tuning efficiency, and real-time anomaly detection capabilities. Results indicate that AI-native databases achieve up to 67% reduction in query latency compared to manual optimization approaches while maintaining high accuracy in workload prediction. The study also addresses critical challenges including data governance, security considerations, and implementation complexity. Our findings demonstrate that native AI integration represents a fundamental shift in database architecture rather than incremental enhancement, offering significant advantages in enterprise environments dealing with complex, dynamic workloads. This research contributes to understanding the practical implications, benefits, and limitations of AI-native database systems for modern data-intensive applications.