The foundational elements of AI architecture that IT leaders need to scale

This article describes four foundational elements for building reliable AI systems at scale: data quality, context engineering, governance, and human expertise. Poor data quality leads to AI hallucinations (when an AI generates false information) and bias, so organizations must connect data across systems and ensure it is organized and accessible. Context engineering (selecting and presenting the right information to an AI model) and RAG (retrieval-augmented generation, where an AI pulls in external documents to answer questions) help models produce accurate answers by feeding them minimum, current, and structured data rather than overwhelming them with too much information.