Semantic View Autopilot for AI Governance

This article discusses the challenges of manual semantic governance in data management, where schema evolution and business changes cause documentation to drift. It proposes 'semantic view autopilot,' using AI to draft table descriptions, column labels, and metric definitions, but emphasizes that AI should only assist, not replace, human review. The article outlines where automation helps, potential pitfalls like subtle inaccuracies, and the need for governance to ensure trusted outputs. It covers topics like lineage for metric definitions, programmatic review, and measuring autopilot effectiveness, with a focus on Dremio-friendly approaches. This is relevant to IT/technology professionals dealing with data engineering, AI governance, and semantic layers.