What Happened at Oracle
If you work in enterprise technology and you haven't been watching what's happening at Oracle, you should start now. Not because of the stock price, though that's a story in itself, but because Oracle is doing something that every business leader needs to understand: they're rebuilding their entire company around AI, in real time, with real consequences.
And the lessons from their transformation apply to every company, regardless of size.
In the last six months, Oracle's stock has dropped over 50% from its September highs. As of this week, shares are down roughly 25% year-to-date, trading near $147. Wall Street has punished the company for an aggressive AI infrastructure buildout, including plans to raise $50 billion in debt and equity to construct data centers at a pace that would make most CFOs faint.
Then on March 31st, Oracle laid off an estimated 20,000 to 30,000 employees; nearly 18% of its global workforce. Employees received emails at 6 a.m. with no prior warning. No conversation with a manager. No heads-up from HR. Just a message from "Oracle Leadership" saying their role had been eliminated, effective immediately.
The numbers behind the move tell a clear story. Oracle's remaining performance obligations hit $553 billion in Q3, up 325% year over year. Cloud revenue grew 44%. Their multi-cloud database business grew 531%. Revenue hit $17.2 billion in a single quarter, up 22%. Net income jumped 95% to $6.13 billion.
This is not a company in trouble. This is a company making a massive bet on where the world is going.
The Real Question Isn't About Oracle
What caught my attention isn't the stock price or the layoffs. It's the pattern.
Oracle, Microsoft, Meta, Amazon: every major technology company is going through the same calculation right now. How do we restructure our workforce to operate in an AI-native world? Oracle just did it louder and faster than most. But make no mistake, this recalibration is happening everywhere. And it's coming to mid-market and small businesses next.
The question for the rest of us isn't whether AI will change how we work. It already has. The question is: how do we make ourselves more valuable because of it?
We Will Get Better, If We Learn to Be the Human in the Loop
I've spent 30 years in sales and technology consulting. I've watched every major platform shift: from client-server to cloud, from on-premise to SaaS, from manual reporting to real-time analytics. Each time, the professionals who thrived were the ones who learned the new tools first and applied them with judgment.
AI is no different. Except the stakes are higher and the timeline is shorter.
Here's what I believe: AI makes us better professionals. Not by replacing our judgment, but by amplifying it. The tools available today, from intelligent automation to agentic applications that can execute complex workflows, are extraordinary. But they're tools. They infer. They pattern-match. They optimize for what they've been trained on.
What they cannot do is understand context the way a human does. They cannot read the room in a client meeting. Yet. They cannot sense when a data model is technically correct but strategically wrong. They cannot weigh the ethical implications of a recommendation against the lived experience of the people affected by it.
AI can make mistakes. It can infer something unintended. I've seen it firsthand: models that surface the right data but draw the wrong conclusion, automations that optimize for efficiency at the expense of relationships, AI-generated content that sounds authoritative but misses the nuance that makes it trustworthy.
This is exactly why the human in the loop isn't optional. It's essential.
Data Quality Is the Foundation
Here's the part that doesn't get enough attention: AI is only as good as the data it operates on. Oracle knows this. Their entire cloud infrastructure play is built on the premise that enterprises need reliable, well-governed data pipelines to power AI workloads.
At Comerit, we've spent 25 years helping Fortune 500 companies get their data right. SAP transformations. Cloud migrations. Analytics strategies that turn raw data into actionable intelligence. What we've learned is that the companies who invest in data quality, governance, and integration before they invest in AI are the ones who see real returns.
Well-built data combined with human oversight absolutely creates more profitability. Not in theory. In practice. We've seen it with clients who reduced their total cost of ownership by 35% through HANA migrations. We've seen it with organizations that went from 90-day reporting cycles to real-time decision-making. The pattern is consistent: clean data, good tools, and a human who knows what to do with the output.
Where This Is Going
I'm optimistic about the future. Not because AI will solve everything, but because the professionals who embrace these tools will become dramatically more effective.
Oracle's 22 new Fusion Agentic Applications, AI agents built directly into HR, supply chain, sales, and finance workflows, represent the direction of the entire industry. Every enterprise software platform is moving this way. The companies that adopt early and train their teams to work alongside AI will have a significant competitive advantage.
But "alongside" is the key word. The goal isn't to remove humans from the equation. The goal is to elevate what humans can do. A recruiter with AI can source and screen candidates ten times faster, but the recruiter still decides who's the right fit. A financial analyst with AI can model scenarios in minutes instead of weeks, but the analyst still interprets what the numbers mean for the business. A sales professional with AI can personalize outreach at scale, but the relationship still depends on trust, empathy, and follow-through.
The human need doesn't go away. It becomes more valuable.
What I'd Tell Every Business Leader Right Now
Don't wait for the perfect AI strategy. Start with your data. Get it clean, get it connected, and get it governed. Then bring in tools that let your team work smarter; not tools that replace your team.
Invest in your people. Train them on the platforms that matter. Help them understand how to be the human in the loop: the person who checks the AI's work, applies business judgment, and makes the final call.
And when you look at what's happening at Oracle, at Microsoft, at every major technology company, don't see it as a threat. See it as a signal. The tools are getting better. The platforms are getting smarter. The companies that figure out the right balance between AI capability and human judgment will win their markets.
We're not being replaced. We're being upgraded. But only if we choose to learn.
David Caspillo is Managing Partner at Comerit, an AI-powered data transformation and analytics consulting firm with 25 years of enterprise experience. Comerit helps Fortune 500 companies modernize their SAP environments, migrate to Google Cloud, and build intelligent data ecosystems. Learn more at comerit.com.
References
- Oracle Q3 FY2026 Earnings: $17.2B revenue (+22% YoY), $553B RPO backlog (+325% YoY), cloud revenue $8.9B (+44% YoY)
- Oracle stock performance: Down approximately 25% YTD 2026 (Motley Fool, April 6, 2026)
- Oracle layoffs: Estimated 20,000-30,000 employees, approximately 18% of workforce (CNBC, March 31, 2026; TD Cowen estimates)
- Oracle AI infrastructure spending: $50B planned in debt and equity for data center buildout (CNBC)
- Oracle Fusion Agentic Applications: 22 new AI agents across HR, supply chain, sales, and finance (PYMNTS, March 24, 2026)
- Oracle multi-cloud database growth: 531% YoY (Oracle Q3 FY2026 earnings report)
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