Abstract In today’s rapidly evolving software engineering landscape, building scalable AI-native software engineering intelligence analytics platforms requires both technical expertise and strategic vision. With AI-generated code becoming increasingly prevalent in enterprise environments, measuring its effectiveness has become critical for organizations seeking to optimize their development investments. Success depends on 3 fundamental foundations. First, prioritize engineering data fidelity and contextual intelligence over raw metrics volume. Our AI analytics are only as valuable as the engineering signals they interpret. Implement sophisticated data collection mechanisms that capture traditional metrics alongside specialized tracking for AI-generated code contributions, including origin identification, quality assessments, and downstream impact analysis. This foundation determines our platform’s capabilities and limitations. Second, embrace architectural flexibility for workflow integration and adaptive scaling—design an analytics platform that seamlessly integrates with existing development toolchains while handling unpredictable data patterns. Cloud-native microservices allow us to iterate on analytics components independently while maintaining system resilience. Include dedicated modules for measuring AI code effectiveness, tracking acceptance rates, modification frequencies, and security patterns. Finally, center development around engineering team productivity and developer experience. Implement tight feedback loops with development teams to understand how they consume intelligence insights. The most impactful AI-native platforms strike a balance between cutting-edge AI capabilities and pragmatic engineering principles. We focus on solving today’s delivery challenges and architecting tomorrow’s autonomous workflows. Co-sponsored by: Avimanyou Vatsa Speaker(s): Naveen Kumar, Room: 205, Bldg: Becton Hall, 960 River Road, Teaneck, New Jersey, United States, 07666, Virtual: https://events.vtools.ieee.org/m/503289