The article highlights the limitations of current enterprise AI solutions, which often fail to deliver on their promise due to a lack of a solid data foundation. The problem lies not with the models themselves, but with the inconsistent logic, metric drift, and fragmented definitions in the underlying data model. A universal semantic layer is proposed as a solution to centralize data logic and make it available to every tool, including AI agents, ensuring consistency and trust in AI-generated insights. By defining clear definitions of KPIs, dimensions, and access policies, organizations can align their data stack around those definitions and create an environment where AI can deliver on its promise.