Oracle reports that more than 100,000 employees now use ChatGPT Work and Codex across functions including talent acquisition, the Oracle Applications Lab, and IT. The common pattern is replacing specialist-dependent, multi-day workflows with self-service tasks completed 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 previously required a full team months to produce.

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. Jan Ackerman, Senior Vice President and Global Head of Talent Acquisition at Oracle, says the tool gives recruiters the information they need before a conversation with the hiring manager, replacing what previously took 2–4 days to compile. Prep time has dropped to about 15–20 minutes using the tool. Ackerman describes the change as going “from zero to a hundred.” The process is also more consistent: recruiters used to run the intake process differently from search to search, but the tool now gives every hiring manager the same quality of data and insights regardless of which recruiter they worked with.

The Oracle Applications Lab team 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. Richard Lam, Group Vice President of Oracle Applications Lab, described one user who asked a question that would normally have taken a couple of hours; she got a response almost immediately, and the numbers matched the old manual process exactly. In production engineering, site reliability engineers use Codex to gather context about an incident and automatically pull up the relevant playbook. Lam says a typical simple incident that used to take an hour to resolve can now be handled in minutes.

Lam is careful to note that none of this runs on autopilot. Someone still has to make sure the underlying system is built correctly. The leadership lessons in the article emphasize responsibility: give the correct guardrails around system design, architecture, security, and how Codex structures code; provide prototypes instead of specs, as Barry Shilmover, Vice President and Technical Advisor to the CIO, puts it: “Normally, I put [my idea] down on paper, but now I put it down in a prototype”; and own the code, since working alongside Codex is necessary to keep the code maintainable.

Oracle describes the shift as a broader change in how the company operates with AI. Across recruiting, analytics, and engineering, each line of business stated the outcome they wanted and let Codex and ChatGPT handle the implementation. Shilmover sums up the forward-looking stance: “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.”