Enterprise AI is entering a new execution era. Organizations are moving beyond experimental copilots toward autonomous, agentic workloads that must reason, act, and execute across complex business environments. But many AI initiatives are running into the same obstacle: fragmented data architectures that introduce latency, integration complexity, security gaps, and stale context.

To operationalize agentic AI at scale, enterprises need a data foundation that brings reasoning logic, persistent memory, transactional integrity, and governance closer to where the data resides. Converged architectures that unify relational, document, graph, vector, and other data capabilities can help reduce integration overhead while supporting the performance, security, and reliability required for mission-critical autonomous workloads.

In our latest report, Data Gravity in the Age of AI: Engineering the Mission-Critical Engine for Autonomous Workloads, completed in partnership with Oracle, Futurum Research explores why fragmented data strategies are limiting enterprise AI progress and how Oracle AI Database 26ai provides a unified foundation for scalable, secure, and operationally consistent agentic AI.

In this report, you will learn:

• Why fragmented data architectures create barriers for agentic AI
• How converged database architectures can reduce integration complexity
• The role of in-kernel vector processing, JSON-relational duality, and persistent agent memory
• How Oracle supports interoperability across open standards, multicloud environments, and distributed data estates
• Why availability, security, compliance, and deployment parity are essential for mission-critical autonomous workloads

If you are interested in learning more, be sure to download your copy of Data Gravity in the Age of AI: Engineering the Mission-Critical Engine for Autonomous Workloads today.