Traditional enterprise AI relies heavily on context engineering, where each application builds its own knowledge representations. This approach creates inconsistencies and redundancies as multiple teams process the same information independently. The current model breaks down with increased AI deployment due to inconsistent knowledge, difficult change propagation, and duplicated efforts. This highlights a shift from context engineering to enterprise knowledge management. An enterprise knowledge platform is proposed as a solution, analogous to enterprise data platforms for structured data. This platform manages enterprise knowledge as a shared asset, publishing reusable representations for all AI applications. It operates in four distinct layers: Raw, Refined, Integrated, and Serving. The Raw layer preserves original data sources for reprocessing. The Refined layer normalizes diverse sources into consistent, managed knowledge objects. The Integrated layer connects these objects into a unified knowledge model, capturing business relationships. Finally, the Serving layer publishes optimized representations for AI workloads. This layered approach enables robust knowledge lifecycle management, strong governance, and the creation of reusable knowledge services, forming the essential data foundation for enterprise AI.