超过十万名Oracle员工正在使用ChatGPT Work和Codex，从人才招聘到Oracle应用实验室再到IT组织。在每种情况下，过去依赖专家且需要数天完成的工作，现在任何人都能完成，并且几分钟内就能搞定。招聘人员省去了数天的市场调研，业务用户描述结果而非寻找报告，技术负责人构建过去需要整个团队花费数月才能完成的工具。
“我们使用ChatGPT Work构建了人才市场情报工具，它真正彻底改变了我们招聘流程的前端工作方式。”
—Jan Ackerman，Oracle高级副总裁兼全球人才招聘主管
让招聘研究更高效
人才招聘团队使用ChatGPT Work构建了一个人才市场情报工具，该工具可以接收职位描述，研究可比职位，对标薪酬，并评估相关地点的人才库。该工具为招聘人员提供了与招聘经理交谈前所需的宝贵信息，而这些信息此前需要2至4天才能整理完成。
“我们从零做到了百分之百，”Oracle高级副总裁兼全球人才招聘主管Jan Ackerman说。“现在我们可以坐下来，使用我们借助[ChatGPT] Work构建的工具，大约15到20分钟就能完成准备。”
这个过程不仅更快，而且更加一致。招聘人员过去每次招聘的 intake 流程都不尽相同，但有了人才市场情报工具，流程变得一致，因此无论招聘经理与哪位招聘人员合作，他们都能获得同等质量的数据和洞察。
业务用户更快获得所需答案
Oracle应用实验室团队负责帮助运行Oracle的许多核心业务流程，他们构建了公司对象、关系和规则的本体，因此可以用Codex将通俗语言的业务问题转化为可靠的SQL查询。业务用户描述他们想要的结果，Codex决定调用哪些内部系统，收集信息，并返回分析、报告或应用程序。
这与过去的工作方式相比发生了变化。Lam说，有一位用户来找他，此前她提出了一个通常需要几个小时才能回答的问题。她告诉Lam：“有了新工具，我提交请求后几乎立即就得到了回复。”当她将结果与旧的手动流程进行核对时，数字完全吻合。
在生产工程中，站点可靠性工程师使用Codex收集事件的相关上下文，并自动调出正确的操作手册。SRE花更多时间指导决策，花更少时间寻找信息。“过去解决一个典型的简单事件需要一个小时，现在几分钟就能处理完，”Lam说。
尽管Codex承担了更多工作，Lam仍谨慎指出，这一切并非自动运行。仍然需要有人确保底层系统构建正确。
“业务用户现在描述他们想要的结果，而不是去寻找报告。这是一个重大转变。我甚至称之为业务转型。”
—Richard Lam，Oracle应用实验室集团副总裁
领导力经验
虽然AI构建工具，但人仍然对输出负责。有几点经验尤为突出：
设置正确的护栏。 “你在系统设计、架构、安全性以及你希望Codex如何为你构建代码方面，仍然必须非常负责，”Lam说。
提供原型而非规格说明。 “通常，我会把[我的想法]写在纸上，但现在我会把它做成原型，”Barry Shilmover，副总裁兼CIO技术顾问说。
掌控代码。 “如果你不与Codex并肩工作，你就会陷入拥有大量不可维护代码的境地，”Lam说。
下一步是什么
Oracle团队正在彻底转变公司借助AI运营的方式。在招聘、分析和工程领域，每条业务线都描述结果，让Codex和ChatGPT处理工作如何完成。已有数千名ChatGPT和Codex用户，Oracle员工在无数工作任务中使用AI。正如Shilmover所说：“我不知道答案是什么，因为我不知道我下一个要解决的问题是什么。我只知道我要做的第一件事之一就是利用Codex来完成它。”
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.
“We’ve built our talent market intelligence tool using ChatGPT Work, and it’s really revolutionized how we do the front end of our recruitment process.”
—Jan Ackerman, Senior Vice President and Global Head of Talent Acquisition, Oracle
Making recruiting research more efficient
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.
Business users get the answers they need faster
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.
“Business users now describe the outcome they want instead of hunting for a report. That is a major shift. I would even call it a business transformation.”
—Richard Lam, Group Vice President, Oracle Applications Lab
Leadership lessons
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.
What’s next
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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