Oracle Staff Use ChatGPT Work and Codex to Cut Days of Tasks to Minutes

Recruiting research drops from days to minutes

Oracle's talent acquisition team used ChatGPT Work to build a talent market intelligence tool. It takes a job description, researches comparable roles, benchmarks compensation and assesses the talent pool across relevant locations. Recruiters use the output to prepare for conversations with hiring managers. According to OpenAI, that information 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."

The tool also makes the process more consistent. Recruiters used to run the intake process differently from one search to the next. With the tool, every hiring manager gets the same quality of data and insights, regardless of which recruiter they work with.

Business users describe outcomes, Codex does the lookup

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. This lets a plain-language business question 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.

Richard Lam, Group Vice President at Oracle Applications Lab, described one user who asked a question that would normally have taken her a couple of hours to answer. She told him, "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 context about an incident and automatically pull up the right playbook. They 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.

Lam also stresses that none of this runs on autopilot. Someone still has to make sure the underlying system is built right.

Leadership lessons from Oracle

OpenAI's account lists several lessons from Oracle's leaders, built on the point that people remain responsible for the output even when AI builds the tools.

• Give the correct guardrails. Lam says, "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."
• Provide prototypes instead of specs. Barry Shilmover, Vice President and Technical Advisor to the CIO, says, "Normally, I put [my idea] down on paper, but now I put it down in a prototype."
• Own the code. Lam warns, "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."

What it means

According to OpenAI, Oracle teams across recruiting, analytics and engineering now describe an outcome and let Codex and ChatGPT handle how the work gets done. The company says thousands of Oracle employees already use ChatGPT and Codex across a wide range of tasks.

For other organizations, the examples show where AI tools are being applied inside a large enterprise: research preparation, data queries and incident response. They also show the caveats Oracle's own leaders raise, namely sound system design, security and maintainable code. Shilmover summed up the open-ended nature of the approach: "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."