Artificial intelligence (AI) is significantly impacting digital landscapes, with organizations at various stages of AI integration, from evaluation to optimization. The data warehouse plays a crucial role by serving as a central repository for organizational data, thus enabling AI to extract valuable insights and improve products through enhanced data analysis, real-time personalization, and automated task management. The rapid evolution of AI necessitates an agile data platform, with streaming engines providing the flexibility and responsiveness needed to handle real-time data processing. This approach allows for dynamic data management, where events are captured and processed continuously, facilitating immediate insights and actions. Open formats like Apache Iceberg offer freedom and flexibility, enabling seamless data integration across different platforms without compromising functionality. Best practices for maintaining robust and reliable systems include using schema registries for consistent data management, ensuring observability through Open Telemetry, and leveraging tiered storage to manage costs. As AI continues to democratize, real-time streaming data platforms will be pivotal for organizations aiming to harness AI's potential, promoting faster iteration cycles, enhanced engagement, and operational efficiency.