AI initiatives depend on timely access to trusted enterprise data, yet much of that data remains difficult to use across mainframe and cloud environments. This paper examines the architectural barriers that limit AI adoption, including batch delays, fragmented access, and inconsistent governance.
Explore a four-part framework for structured data delivery, file movement, live shared access, and elastic storage. Learn how PropelZ, FlowZ, Lozen, and Zaac support an incremental approach to making core enterprise data available for AI and analytics while preserving existing systems.
