In such a context, the enterprises of the modern world go towards cloud-native analytics. This is to match ever-increasing needs towards real-time insights, scalability of data processing, and AI-driven decision support. The aim of this paper is to introduce an integrated cloud-native Business Intelligence framework with scalable mechanisms driven by artificial intelligence using Snowflake and Google Big Query. The proposed framework covers the following challenges a business intelligence environment faces: fluctuating analytical workloads, high operational costs, fragmentation in governance, and variability in performance across different platforms. The architecture here proposed incorporates most of the desirable features discussed above: predictive auto-scaling, AI-driven anomaly detection, dynamic resource optimization for performance efficiency, which cuts down on unnecessary compute consumption. Elasticity is provided by Mult cluster warehouses from Snowflake and serverless execution by Big Query. AI models predict the intensity of workloads to enable the system to decide autonomously how to scale up or down. The system includes automated governance, security monitoring, and compliance controls to support data integrity and privacy in multi-cloud environments. Query performance, cost-to-performance, and system responsiveness during periods of peak demand are demonstrated by experimental investigation. AI's limited capacity for generalization and dependence on a single source are additional issues; the process is made even more difficult by the addition of more clouds. This is where the development of a durable, scalable, and intelligent enterprise business intelligence ecosystem starts. In order to construct analytics for the future, this paper explores the transformative effects of combining cloud-native data warehousing with AI-driven orchestration.