Summary
Enterprise AI struggles to scale not because of model limitations, but because it relies on incomplete or outdated data. This paper explains the gap between the data AI typically uses and the real-time, authoritative data in systems of record—and why that gap leads to failure in high-stakes use cases. Solving it requires rethinking how AI accesses, governs, and acts on enterprise data.
What You’ll Learn
- What it takes to make enterprise data truly “AI-ready,” including governance and audit requirements
- Which type of data can safely drive critical business decisions
- Why most AI systems fail in production, even after successful pilots
- How data timelines shape AI architecture and outcomes
- Why real-time access to systems of record is essential for agentic AI
- How stale or replicated data leads to costly errors in automated decisions
How data integration helps you thrive in a hybrid world
Data integration enables seamless data movement and synchronization between on-premises systems and the cloud, supporting a hybrid IT environment for reliable, real-time insights.
Sharing mainframe data with the cloud?
Integrate mainframe and cloud data by addressing connectivity, latency, transformation, and simplicity to enable seamless hybrid data movement and real-time synchronization.
Data access
Learn more about data access, the importance of using data access tools, and how data access applies to businesses through several use cases in this Software AG article.