What are AI layers? And 5 essential AI layers for data teams

AI layers, akin to interconnected systems in a car, are essential components that make analytics tools AI-native rather than merely AI-adjacent, providing a structured architecture that ensures AI functions effectively in production environments. These layers include infrastructure that supports scalability and connectivity, foundation models that utilize pre-trained capabilities for tasks like text-to-SQL translation, orchestration and tooling that manage workflows and ensure data currency, interfaces that facilitate user interaction through notebooks, chat, and APIs, and governance that maintains consistent definitions and access controls. When integrated from the start, these layers enable seamless operation and collaboration, allowing both technical and business users to interact with AI-generated insights from a unified source of truth, thereby mitigating fragmentation and ensuring reliable governance. Hex exemplifies the successful integration of these layers, providing a platform where data teams and business users collaborate within shared governance boundaries, enhancing the overall efficiency and reliability of AI-driven analytics.