Over a hundred thousand employees across Oracle are using ChatGPT Work and Codex, from talent acquisition to Oracle Applications Lab to the IT organization. In each case, work that used to rely on specialists and take days can now be done by anyone and is complete in minutes. Recruiters skip days of market research, business users describe an outcome instead of hunting for a report, and technical leads build tools that used to take a full team months.

The talent acquisition team used ChatGPT Work to build a talent market intelligence tool that takes a job description, researches comparable roles, benchmarks compensation, and assesses the talent pool across relevant locations. The tool arms the recruiter with valuable information that they need going into a conversation with the hiring manager, information that previously took 2–4 days to compile.

“We’ve gone from zero to a hundred,” says Jan Ackerman, Senior Vice President and Global Head of Talent Acquisition at Oracle. “Now we’re able to sit down and prep for about 15 to 20 minutes using the tool that we’ve built using [ChatGPT] Work.”

Not only is the process faster, but it’s also more consistent. Recruiters used to run the intake process differently from one search to the next, but with the talent market intelligence tool, the process is consistent, so every hiring manager has the same quality of data and insights, no matter which recruiter they worked with.

The Oracle Applications Lab team, which helps run many of Oracle’s core business processes, built an ontology of the company’s objects, relationships, and rules, so a plain-language business question can be turned into a reliable SQL query with Codex. A business user describes the outcome they want, and Codex decides which internal systems to call, gathers the information, and returns an analysis, a report, or an application.

That’s a change from how work used to get done. Lam said one user came to him after asking a question that would normally have taken her a couple of hours to answer. She told Lam, “With the new tool, I put in the request and got a response almost immediately.” When she checked the result against the old manual process, the numbers matched exactly.

In production engineering, site reliability engineers use Codex to gather relevant context about an incident and automatically pull up the right playbook. SREs spend more time guiding decisions and less time hunting for information. “A typical simple incident that used to take an hour to resolve can now be handled in minutes,” says Lam.

Even as Codex takes on more of the work, Lam is careful to note that none of this runs on autopilot. Someone still has to make sure the underlying system is built right.

While AI builds the tools, the people are still responsible for the output. A few lessons stand out:

• Give the correct guardrails. “You have to still be very responsible about your system design, your architecture, your security, and how you would like Codex to structure the code for you,” says Lam.

• Provide prototypes instead of specs. “Normally, I put [my idea] down on paper, but now I put it down in a prototype,” says Barry Shilmover, Vice President and Technical Advisor to the CIO.

• Own the code. “If you don’t work alongside Codex, you’re going to get into a situation of having lots of code that is not going to be maintainable,” says Lam.

The Oracle team is completely shifting how the company operates with AI. Across recruiting, analytics, and engineering, each line of business described an outcome and let Codex and ChatGPT handle how the work gets done. With thousands of ChatGPT and Codex users already, Oracle employees use AI across countless work tasks. As Shilmover puts it, “I don’t know what the answer is because I don’t know what my next problem to solve will be. I just know that one of the first things I’ll do is I’ll leverage Codex to do that.”

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