This chapter examines the structural challenges underlying large-scale AI deployment. It argues that the primary constraint is not data volume but data stability, semantic coherence, and governance maturity. The chapter formalizes the reclassification of data from a project-level asset to an infrastructure layer and analyzes the limitations of approaches rooted solely in software engineering traditions. By integrating principles from systems and reliability engineering, it identifies core infrastructure requirements, including standardization, decoupling, measurability, scalability, and lifecycle segmentation. The chapter differentiates the proposed infrastructure-oriented methodology from prevailing paradigms such as Data Mesh and Lakehouse architectures, establishing the theoretical basis for the architectural framework developed in subsequent chapters.