AI-Powered Enterprise Database Modernization Framework for Hybrid and Cloud Environments

Abstract

Enterprise database estates spanning on-premises, hybrid, and cloud infrastructure face a persistent modernization gap. Manual migration tooling scales poorly against the volume and heterogeneity of mission-critical databases, while pure automation frameworks frequently lack the operational safeguards that large organizations require before cutting over production workloads. This paper proposes a layered framework that combines artificial intelligence-driven schema conversion and observability with established high availability and disaster recovery engineering practice, positioning automation as an accelerant to migration discipline rather than a replacement for it. Three integrated layers structure the framework: an AI-assisted schema and query translation layer, an automated validation and rollback layer, and an operational continuity layer grounded in high availability and disaster recovery patterns already proven in production database environments. Recent advances in large language model-based SQL translation and schema inference, considered alongside operational patterns for SQL Server and Oracle high availability, address a gap in the literature where AI-driven migration tooling and enterprise operational rigor are rarely treated as a single design problem. A representative hybrid-cloud migration scenario illustrates how each layer interacts with the others, and the discussion identifies where current automation still depends on human validation. The framework gives enterprise database teams a structured basis for evaluating AI-assisted modernization tools without discarding the operational safeguards that large-scale migrations demand.