IG1424 AI-native Data Architecture for Autonomous Operations Use Cases and Requirements v1.1.0

Traditional data architectures, designed for static, predefined, and human-centric decision-making cannot adequately support AI-driven autonomous operations and closed-loop management. These conventional systems excel at bulk data processing and structured workflows but fail to deliver the real-time analytics, continuous learning, contextual awareness, optimization capabilities, and zero-touch management essential for truly autonomous environments. Unlike traditional operational environments with predictable data flows, predetermined needs, and rigid data models, autonomous operations function in dynamic settings where data requirements evolve in real-time and decision-making occurs automatically with continuous adaptation.

Enabling these autonomous operations demands an AI-native data architecture capable of handling unpredictable data flows and allowing data models to evolve dynamically without any rigidity or limitations.  Beyond traditional data and insights, autonomous systems require contextual understanding and knowledge to make decisions that consider situational factors, historical information, and future predictions.

This IG explores the fundamental concepts of AI-native data architecture from an outside-in perspective, capturing requirements of autonomous operational domains, especially from the context of AI-CLA project. While aligned with the Modern Data Architecture project’s goals, this IG offers a unique viewpoint on how autonomous operations use data layer capabilities to enable intelligent, closed-loop behaviors. This guide is structured into multiple parts, as outlined below.