Oracle如何将工作天数与ChatGPT和Codex转换成分钟
Oracle在招聘、工程和业务方面，将专门知识与ChatGPT Work和Codex一起转化为快速、可重复的工作流程。
98%
减少人才获取研究所需的时间
130K
活动 ChatGPT 用户在 Oracle
95K+
Oracle 活跃的代码用户
Oracle各地超过10万名员工正在使用ChatGPT Work和Codex，从人才获取到Oracle应用实验室到IT组织。 在每一种情况下，过去依靠专家并需要几天时间的工作现在都可以由任何人完成，几分钟内完成。 招募者跳过数日的市场研究，商业用户描述一个结果而不是寻找报告，技术线索构建工具过去要花整整一个团队月的时间。
“我们用ChatGPT工作建立了我们的人才市场情报工具
人才征集团队利用ChatGPT Work建设人才市场智能工具，采用职务说明，研究可比角色，基准测试补偿，评估相关地点的人才库。 该工具向招募者提供他们需要与雇用经理谈话的宝贵信息，而以前收集这些信息需要2-4天。
称 Jan Ackerman是Oracle的资深副总裁兼全球人才采购主管。 “现在，我们可以坐下来准备大约15至20分钟，使用我们使用的工具[ChatGPT]。 工作。”
不仅过程更快，招生人员曾以不同的搜索方式运行录取程序，但借助人才市场智能工具，这一过程是一致的，所以每个招聘经理都有同样质量的数据和见解，无论他们与哪个招聘人员合作。
Oracle应用实验室团队帮助运行了Oracle的许多核心业务流程，构建了公司对象，关系，规则的本体论，因此一个普通语的商业问题可以与Codex一起变成一个可靠的SQL查询。 一个商业用户描述他们想要的结果，而Codex则决定哪些内部系统可以调用，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不同。 Lam 称一名用户问了一个问题，通常要花几个小时才能回答。 她对Lam说，“有了新的工具，我几乎立即提出了要求并得到了答复。” 当她对照旧手动程序检查结果时，数字完全吻合。
在生产工程中，站点可靠性工程师使用Codex来收集事件的相关背景，并自动拉出正确的游戏本。 SREs花更多的时间指导决策，较少时间寻找信息。 称 Lam说：“一个典型的简单事件，过去需要一小时才能解决，现在可以在几分钟内处理。
即便Codex承担了更多的工作，Lam也谨慎地指出，这些都不是运行在自动驾驶上。 还需要有人确保基本系统是正确的。
“商业用户现在描述他们想要的结果，而不是寻找报告。 这是一个重大转变。 我甚至会称之为企业转型。”
在人工智能构建工具的同时，民众仍负责产出。 几个教训突出：
- 给正确的护栏。 称 Lam。 “你仍必须非常负责你的系统设计、建筑、安全
- 提供原型而不是规格。 “诺玛利，我把[我的想法]写在纸上，但现在我把它写成原型，”副总统兼首席情报员技术顾问称 Barry Shilmover。
- 拥有密码称 Lam表示：「如果你不和Codex一起工作
Oracle团队完全转变了公司与AI的运营方式。 在招聘、分析和工程方面，每一行业务都描述了一个结果，并让Codex和ChatGPT处理工作如何完成。 ChatGPT和Codex用户已达数千人，Oracle员工跨越无数的工作任务使用AI. Shilmover指出：「我不知道答案是什么，我只知道，我首先要做的是利用Codex来做到这一点。
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How Oracle turns days of work into minutes with ChatGPT and Codex
Across recruiting, engineering, and operations, Oracle turns specialist knowledge into fast, repeatable workflows with ChatGPT Work and Codex.
98%
Decrease in the time it takes for talent acquisition research
130K
Active ChatGPT users at Oracle
95K+
Active Codex users at Oracle
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.”
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.
“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.”
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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