As digital pathology enters a new era, the promise of AI-assisted diagnostics is often held back not by model performance, but by limited access to structured, interoperable data. The absence of standardized formats reduces the usability of whole slide images for machine learning, restricts multi-institutional collaborations, and complicates integration with clinical workflows. We explore how the lack of standardization in data formats remains a key obstacle to translating research models into clinical tools. We highlight the practical benefits of adopting the DICOM standard for digital pathology and demonstrate how standardization can improve data integration, enhance AI workflows, and support large-scale collaborations, ultimately accelerating the clinical impact of computational pathology.