AI is transforming enterprise operations, but its success hinges on the quality of data. Over 80% of AI projects fail due to issues like fragmented or poorly governed data. The O’Reilly report "Structured for Intelligence" outlines essential practices for creating a robust data foundation that supports effective AI systems. It emphasizes the need for data that is governed, discoverable, and high-quality, providing a clear framework to address these challenges. Key points include the importance of strong semantics and metadata, which are vital for applications like conversational analytics and automated workflows. The report stresses that governance must be integrated from the outset, citing regulations like the EU AI Act and frameworks such as role-based access control (RBAC) and attribute-based access control (ABAC). Examples from companies like Walmart and Block illustrate how implementing a Model Control Program (MCP) and structured data context can help scale AI initiatives safely and efficiently. Readers will find a practical 10-step roadmap for developing a compliant, AI-ready data stack. This includes strategies for ensuring AI outputs are consistent and evidence-based, reducing metric drift, and fostering trust among teams. The guide aims to equip leaders in data and engineering roles with actionable insights to create dependable AI systems.