Generative AI has significant potential to add value to the global economy, but most organizations are hindered by their data management. Despite investments in data lakes, warehouses, and analytics tools, complexity persists due to siloed systems, duplicated data, and outdated batch processes. The "data liberation problem" arises from this complexity, making it difficult for AI projects to access the right data when needed. Data streaming offers a solution by treating data as a continuously moving asset, providing real-time data flow across on-prem, cloud, and hybrid environments. This approach eliminates batch delays, improves data quality, and enables scalable AI interactions, ultimately giving AI systems the high-quality, up-to-date data they require to perform effectively.