Large Language Models (LLMs) are transforming how we search for information, generate content, and make decisions. Factual information remains crucial in the new wave of AI. Inaccuracies in financial queries, outdated data in manufacturing, life sciences, and consumer packaged goods can lead to costly mistakes. As AI adoption accelerates, organizations need to critically evaluate how LLMs interact with structured data, which is often the single source of truth. There are three categories of use cases: Structured-Only, where accuracy is non-negotiable; Structured + LLMs, where LLMs enhance structured data; and LLM-Only, when creativity or synthesis is important but factual accuracy is less critical. The future requires data observability to ensure trustworthy AI, and enterprises that successfully navigate this balance will lead the next wave of AI-driven transformation.