Architecting Applied AI–Driven Enterprise Analytics Platforms on Modern Data Warehouses

The rapid adoption of applied artificial intelligence (AI) in enterprise environments has intensified the need for scalable, governed, and high-performance analytics platforms. This study examines the architectural principles and design considerations for building applied AI–driven enterprise analytics platforms on modern data warehouses. Using a design-oriented and empirical analytical approach, the research evaluates how data warehouse–centric architectures support diverse AI workloads across performance efficiency, scalability, governance, and model lifecycle management dimensions. The results demonstrate that architectures leveraging elastic compute, integrated metadata services, and automated model orchestration achieve superior analytical performance, stronger compliance readiness, and improved operational stability compared to fragmented or legacy-extended platforms. Visual synthesis through multi-dimensional and cluster-based analysis further reveals that AI-native warehouse architectures align more effectively with enterprise trust and latency requirements. The study concludes that modern data warehouses serve not only as data repositories but as strategic analytical substrates for operationalizing applied AI at enterprise scale.