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Oracle Goes Deep on AI and Multicloud: Enterprise AI Database 26ai Revolution

Oracle's latest announcement of Database 26ai represents a significant leap forward in enterprise AI capabilities, bringing artificial intelligence directly into the database layer where data and controls already reside. This revolutionary approach addresses one of the most critical challenges facing organizations today: how to operationalize AI in mission-critical environments while maintaining security, governance, and operational excellence.

Our interpretation of these findings is that most organizations struggle with data silos and lack a coherent, unified data model that harmonizes information across departments and processes. This data challenge causes great difficulty when implementing cross-functional automation or Agentic AI projects. We believe recent Oracle announcements demonstrate a clear understanding of this problem, and the company is putting forth the beginnings of a roadmap to address the challenge.

In Search of Enterprise AI ROI

A new, AI native organization can start with a clean sheet of paper and develop processes that unify data from the start. The vast majority of established enterprises, however, suffer from data fragmentation and stale data, two of the most significant barriers to enterprise AI adoption.

What 26ai Changes – and Why It Matters

We believe Oracle is executing on a concept we've been advocating – i.e. bring AI to where the data and controls already live. The company has infused AI across the stack (transactions, DR, security, query processing, and application tooling) while keeping the database's core architecture intact.

Enterprise-Class AI Capabilities

Vectors as a native type. 26ai embeds vectors turning myriad data formats into numbers, strings, and dates, and integrates with Oracle's heritage value elements around transactions, disaster recovery, security and governance. Unified search enables vector similarity with simple filters, so recommendations, fraud detection, and document/image retrieval can include who the user is, what they're entitled to see, and an audit of the workflow.

Natural-language access with safety. The "SQL translator" lets users ask questions in English (or other languages), with the database enforcing row-level privacy, roles, and policies. Moving trust guarantees down into the database – rather than relying on app-level code – is a very Oracle-like move to prevent leaks and privacy violations.

Comprehensive Multicloud Strategy

Oracle's multicloud is now a reality. It claims to have ~50 data center locations across Azure, Google, and most recently AWS; plus Cloud@Customer (on-prem) and even on-prem cloud regions when required. The same Exadata, RAC, Data Guard stack runs everywhere, which, when combined with 26ai, enables region- and cloud-aware fault-domain strategies without killing the operational model.

Our Take

In our opinion, Oracle's AI Database 26ai is one of the more grounded attempts to operationalize AI in mission-critical settings. The seamless upgrade, converged data model, open Lakehouse stance, and database-level trust addresses key real blockers to AI scale – i.e poor data quality, data silos and complexity. Oracle's bet is that the path to AI is shortest by placing intelligence inside the database. We think that's a defensible thesis that can stand the test of time given Oracle's track record and vast technology asset base.