Oracle has weaved itself into the digital infrastructures of the world’s biggest companies for decades with its database technology.
The co-founder of Oracle, Larry Ellison, finding a second wind, raced back up the charts to briefly become the wealthiest man in the world in September 2025, after investors rewarded Oracle’s shift to renting computing power to AI developers.
Here we are a year later.
Ellison is now the 8th wealthiest man in the world, and Oracle still plays among the biggest boys in AI, much of its business driven by its growing AI cloud infrastructure offering for companies like OpenAI, Microsoft, Nvidia and Meta.
So you would think AI adoption for a big tech firm like Oracle, which helps other companies adopt AI, would be easy peasy. All Oracle needs to do is wave that magic AI wand, and voila – Oracle is an AI-First Organization!
But that isn’t the case.
Oracle appears to be having the same basic problems rolling out AI that many others are facing. Or as Oracle co-CEO Clay Magouyrk said recently, “When I think about where we were a year ago, I don’t think we figured out how do we make AI really that useful for ourselves.”
This contrasts with the leadership tone earlier in the year.
In March of 2026, the other co-CEO, Mike Sicilia, told investors that Oracle was adopting AI coding tools very rapidly. “The use of AI coding tools inside Oracle is enabling smaller engineering teams to deliver more complete solutions to our customers more quickly,” said Sicilia.
And yet, their products didn’t get to customers any faster than before. Even worse, a new AI security tool flagged more potential vulnerabilities in two weeks than Oracle had found in the previous year. Most turned out to be false alarms, but engineers had to add a step to check each one.
Why?
The bottleneck moved downstream, to testing, review and release processes that had not been redesigned to handle the deluge of code.
Still Scratching the Surface
Let’s say there are three levels of enterprise AI adoption: Surface AI Adoption, Workflow-level AI Integration, and True Agentic Transformation.
Most organizations , if they are making an attempt at all, are just scratching the surface.
Surface AI adoption: Adding copilots, assistants, or isolated AI tools to legacy workflows without changing process design, governance, roles, incentives, data architecture, or leadership routines. This is the most common form of “AI adoption.”
Those playing on the surface, need to dig deeper and get to the workflow level.
Workflow-level AI Integration: Embedding AI into recurring business processes so work changes materially, with real operational impact.
This is hard work. Leaders will say they have good, consistent workflows that work to achieve critical outcomes, but often they are working to outdated goals, or working on legacy workflows that have unnecessary steps.
True Agentic Transformation: Redesigning workflows, decision processes, capability models, governance, talent practices, and business model elements so agents can help with coordination and fundamental value creation.
Once your organization has identified the truly critical outcomes that will grow your business, and have built optimal workflows that leverage AI and humans in the right places, you are able to think about transforming how your organization is shaped.
The Common Mistake
Oracle’s experience matches what large surveys of companies find. In McKinsey’s 2026 State of AI survey of 1,719 respondents, nearly nine in ten said their organizations use AI regularly, and 80 percent reported gains in personal productivity. Only 37 percent reported any effect on earnings, a share unchanged from the year before.
Only 6 percent qualified as high performers. What set them apart was how they worked. Nearly three quarters of high performers said they ahd fundamentally redesigned workflows because of AI, compared with one in four among everyone else. An earlier McKinsey survey found that redesigning workflows had the largest effect of any practice it measured on whether a company saw earnings impact from generative AI.
BCGs 2025 survey of 1,250 executives found that 60 percent of companies reported minimal revenue or cost gains from AI, while 5 percent generated substantial value. BCG attributed the difference to companies reshaping how their businesses work, as opposed to automating tasks.
Oracle did part of the groundwork. It set security controls, standards and internal policies before the rollout. What the public record does not show is a defined business outcome, a map of the work that produces it, or a plan for the stages after coding.
Oracle measured adoption and code output, but delivery to customers did not change.
Oracle’s co-CEO Magouyrk admitted as much.
“When you make the actual act of writing the code quicker, it doesn’t mean that suddenly everything is 1,000 times faster.”
So much for the magic wand.
