A significant challenge facing enterprise analytics teams is the repetition of redundant questions, which not only wastes time but also exposes underlying structural inefficiencies. This issue arises from inconsistent definitions, a lack of shared logic, and weak institutional memory, leading to misalignment in data interpretation. The use of AI tools in this context can exacerbate the problem, as machine-generated responses may not be grounded in a shared understanding of the business. To break the cycle of redundancy and confusion, enterprises need a consistent, semantic foundation that defines how metrics are calculated and makes those definitions available to every data consumer, including AI agents. By centralizing and sharing this logic, analysts can stop reinventing the wheel, business users get consistent answers across tools, and AI agents produce outputs that align with BI dashboards and spreadsheets, ultimately leading to a single source of truth and eroding trust in the data function.