At its latest stop on the Oracle AI World Tour in London, Oracle took a slightly different stance on AI.
Instead of leaning into the model race like so many others, the company is making a case for putting the database at the center of how agentic AI actually works in practice.
Oracle targets agentic AI with database-centric updates
Oracle rolled this out as part of its latest wave of AI updates tied to its database platform, with a focus on supporting what it’s calling agentic workloads. Instead of routing data through multiple layers of tools, Oracle is trying to bring AI closer to where that data already lives.
It’s definitely a shift away from how most teams have been putting AI together… stitching together models, vector databases, orchestration tools, and apps into a single setup. It can look solid in a demo, but things usually start to break down once it’s live in a real environment.
Oracle’s idea is to simplify some of that complexity by making the database itself a central point for AI processing. In theory, that means fewer handoffs, less data duplication, and tighter control over how information is accessed and used.
Why enterprise data is the real AI bottleneck
Many companies have already figured out how to experiment with AI. What’s been harder is getting those experiments to hold up once they’re connected to live business data.
That’s where things start to go haywire.
Data is usually scattered across different systems, in different formats, with different rules around who can access it.
Trying to move all of that around just to feed AI adds lag, drives up costs, and opens the door to more risk. It also creates more opportunities for things to break, especially once security and compliance get involved.
By keeping AI processing closer to the data layer, Oracle aims to reduce some of that friction.
Performance isn’t the only thing to worry about here; it’s about making AI systems more reliable and easier to manage once they move beyond proof-of-concept.
That matters more as companies start exploring agent-based systems that don’t just generate responses, but take action across multiple systems.
What Oracle’s AI strategy means for channel partners
This isn’t the kind of thing that immediately turns into a new SKU for partners to sell, but it does point to where things are heading.
As AI moves closer to core data systems, the work changes. This is now more about getting the data itself in order, how it’s structured, who can access it, and how it’s protected. That’s a bigger, more complex lift, and most organizations aren’t set up to tackle it on their own.
For partners, especially those working in cloud, data, and security, this opens up a different kind of opportunity.
We’ve been tracking how agentic AI is creating new gaps in areas like security and operations, especially as these systems plug directly into business data. It really is starting to be less about adding another AI tool and more about rethinking how these systems connect to the data underneath.