Context files play a crucial role in AI data integration by providing machine-readable metadata that encodes organizational intent, governance, and structure, which enables AI systems to automate data pipelines effectively and safely. Enterprises often invest heavily in data pipelines to make data "AI-ready," but these efforts can fall short without the essential layer of context that includes metadata, lineage, and governance rules. Unlike traditional configuration files that specify tasks, context files explain the purpose and constraints of those tasks, enabling AI systems to understand and act on them intelligently. This approach transforms static data pipelines into dynamic, context-aware systems that can autonomously manage and optimize data processes. Platforms like Matillion's Maia leverage context files to autonomously generate low-code pipelines that reflect organizational practices, enhancing productivity, compliance, and scalability in multi-cloud environments. By embedding context into data infrastructure, organizations can achieve trustworthy AI model deployment, reduce technical debt, and strengthen governance, thus addressing the productivity crisis faced by data teams.