---
格式版本: 2
标题: "How NVIDIA scales expertise with ChatGPT Work"
原文链接: "https://openai.com/index/nvidia/chatgpt-work"
发布日期: "2026-09-07"
发布时间校准状态: "found"
发布时间需复核: "否"
发布时间来源: "llm:scrape:provider_published_at"
发布时间证据: "provider publishedAt: 2026-09-07"
发布时间校准原因: "提供商元数据中明确标注的发布时间，无其他冲突候选，可确认为文章发布时间。"
发布时间校准置信度: "1"
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发布时间原页读取状态: "source template page reused from URL open"
发布时间未找到原因: ""
发布时间校准时间: "2026-09-07T22:58:19+08:00"
发布时间仲裁状态: "confirmed"
发布时间仲裁尝试次数: 1
发布时间仲裁耗时毫秒: 2536
发现时间: "2026-09-07T22:06:43+08:00"
入库时间: "2026-09-07T14:58:26.995Z"
来源平台: "固定入口"
搜索渠道: "fixed_url"
搜索词: "https://openai.com/news/security/"
匹配关键词:
  - "AI"
相关厂家:
  - "OpenAI"
  - "NVIDIA"
相关专家:
  []
内容类型: "网页"
抓取工具: "Free Fetch + Defuddle"
清洗工具: "Defuddle Markdown + Defuddle/Readability 正文提取"
原始附件:
  []
AI优质: "否"
AI打分: 27
AI分档: "非优质"
AI质检状态: "不通过"
AI打分理由: "正文主线是OpenAI官方客户案例，介绍NVIDIA员工使用ChatGPT Work自动化会务、信息筛选和原型开发，新增可核验事实仅包括每周节省16小时、将25—40条更新提炼为5—8条信号及原型周期缩短至3—5天。来源可追溯但带产品营销属性；历史证据中的NVIDIA机架系统、互连和数据中心架构与本文无直接新增关系。正文没有AI机架、关键部件、网络、供电、液冷、RAS或基础设施部署信息，命中应用与工作流效率强否决项，不具备超节点情报准入价值。"
AI质检模型: "gpt-5.6-sol"
AI质检时间: "2026-09-08T04:35:20+08:00"
AI主题相关性: 1
AI来源权威性: 11
AI新颖性: 5
AI技术细节: 1
AI商业部署信号: 0
AI完整性: 9
AI评分提示词版本: "v17-精简生产版"
AI评分提示词SHA256: "48fb9777f386026761b4873eaff30807694fb11e9b352d7c69bf2dfde750cc7d"
AI评分知识库版本: "knowledge_base_v1-20260819+runtime.87"
AI评分知识库SHA256: "93ca8fb8a4c1ba6cd0f932089fad55eb0ad588dfb335dc4bd8fb1ef642a34d31"
AI评分知识库检索词: "[\"OpenAI\",\"NVIDIA\",\"https://openai.com/news/security/\",\"Intel\",\"GTC\",\"GTM\",\"DC\",\"AI-enabled\"]"
AI评分知识库命中: "[{\"id\":\"historical-jan-apr-01\",\"title\":\"一、NVIDIA GTC 2026 相关基础设施发布与展示\",\"sourceType\":\"curated_item\",\"time\":\"2026-01_to_2026-04\",\"matchedTerms\":[\"NVIDIA\",\"GTC\",\"DC\"],\"rank\":-14.33291376688257},{\"id\":\"historical-may-024\",\"title\":\"OpenAI、Microsoft等围绕MRC协议构建更大规模AI以太网训练网络\",\"sourceType\":\"curated_item\",\"time\":\"2026-05\",\"matchedTerms\":[\"OpenAI\",\"NVIDIA\",\"Intel\",\"DC\"],\"rank\":-11.081101024933444},{\"id\":\"july-correct-0034\",\"title\":\"AMD Fires Back at Nvidia with Helios AI System, Epyc CPUs\",\"sourceType\":\"labeled_article\",\"time\":\"2026-07\",\"matchedTerms\":[\"NVIDIA\",\"Intel\",\"GTC\",\"DC\"],\"rank\":-9.134424070524545},{\"id\":\"runtime-ebfe0103300840358caa3312\",\"title\":\"Nvidia’s Vera CPU Will Anchor Next Phase of AI Infrastructure\",\"sourceType\":\"ai_excellent_article\",\"time\":\"2026-08-17\",\"matchedTerms\":[\"NVIDIA\",\"Intel\",\"GTC\",\"DC\"],\"rank\":-8.087582017439914},{\"id\":\"runtime-94cb0be38e787f74feef8ece\",\"title\":\"With Taalas, AMD Can Bake AI Inference Directly Into Its Chippery\",\"sourceType\":\"ai_excellent_article\",\"time\":\"2026-08-07\",\"matchedTerms\":[\"NVIDIA\",\"Intel\",\"GTC\"],\"rank\":-7.766581142641076}]"
AI摘要: "NVIDIA团队使用ChatGPT Work将GTC大会筹备等重复性工作自动化，在12周规划周期内每周节省约16小时。"
AI摘要模型: "ali-deepseek-v4-flash"
AI摘要时间: "2026-09-07T21:43:33.762Z"
采集批次: "2026年9月7日22点05分18秒"
采集批次ID: "20260907-220517-361"
去重键: "https://openai.com/index/nvidia/chatgpt-work"
---

NVIDIA teams use ChatGPT Work to reduce manual tasks, connect fast-moving signals, and scale successful workflows globally.

Company size: Enterprise

Region: North America

Industry: Technology

Products: ChatGPT

16

Hours saved per week using ChatGPT Work during the GTC planning cycle

3–5

Days to create a working prototype with ChatGPT Work, compared to 2–3 weeks previously

5–8

Actionable signals surfaced per week by ChatGPT Work from 25–40 external AI updates

At NVIDIA, ChatGPT Work is helping knowledge workers spend less time assembling information and more time acting on it.

For teams like GTM and solutions architecture, ChatGPT has become part of how work gets organized, automated, and scaled. For GTM, it transforms recurring operational processes, while solutions architects are using it to connect fast-moving external developments with NVIDIA’s internal priorities.

## Freeing teams to focus on customers

Will Daney helps NVIDIA’s global sales, business development, and product leaders execute and measure their strategies. One of his recurring responsibilities is supporting the field organization around GTC, NVIDIA’s global AI conference.

Previously, preparing for GTC required extensive work in spreadsheets: assembling account lists, tracking registrations, and helping teams identify the actions needed to create a productive experience for customers and partners. During the lead-up to the event, Will estimates that manual analysis consumed about 40% of his time. Today, he has turned much of that work into an automated ChatGPT Work process that runs twice a week. Across the 12-week GTC planning cycle, the workflow saves about 16 hours per week.

“I’m able to give time back, work with the actual field team, get to know them better, and help them figure out how to help our customers be more successful,” Will says.

And because he owns the workflow, he can adapt it as the event changes without waiting for a new tool to be purchased, implemented, and maintained. He can also share the underlying process with teams in other regions. Colleagues supporting events in San Jose, Taipei, Europe, and Washington, DC have received his ChatGPT workflows and customized them for their local needs.

> “With ChatGPT, I think the real key is that I’m able to take a workflow I’ve already developed and I’m able to automate it event over event with little to no overhead.”

—Will Daney, Go-To-Market Strategist at NVIDIA

## Finding the signal in a fast-moving industry

Rachita Jain works on the AI operations team within NVIDIA’s marketing organization, where she builds AI workflows and helps teams adopt new tools. Her challenge is keeping pace with an industry where new models, benchmarks, and research appear every day.

The information is readily available. The harder task is determining which developments matter to NVIDIA and connecting them with internal projects, conversations, and priorities. Rachita built a workflow with ChatGPT Work that reviews trusted external sources alongside internal context, identifies meaningful areas of overlap, and surfaces insights that can inform action. Each week, it distills roughly 25–40 external AI updates into 5–8 actionable signals.

“ChatGPT helped me change passive reading into active intelligence,” she says.

The same environment supports the broader building process. Rachita can begin with an idea, explore possible approaches, work through a codebase, debug problems, and refine the result without continually moving between disconnected tools. Initiatives that might once have remained side projects can develop into working products within days. In one case, she moved from idea to working prototype in about 3–5 days, compared with an estimated 2–3 weeks if she had built the components manually across separate tools.

> “I think the biggest problem I’m trying to solve is information overload, because everything is moving so fast. It’s getting harder by the day to keep track of all the changes. And with ChatGPT, it becomes much simpler.”

—Rachita Jain, Solutions Architect at NVIDIA

## What’s next

The next opportunity is to scale what’s already working. By turning specialized knowledge into reusable workflows, teams across NVIDIA can adapt proven processes across functions, events, and regions—while keeping the people closest to the work in control of how those processes evolve.

And as the AI landscape continues to change, these shared workflows can help NVIDIA connect external developments with internal priorities more quickly and extend AI-enabled ways of working to more employees. The goal is to give teams more time to interpret findings, collaborate, and focus on work that supports customers.

That potential is already visible in Will’s experience. “ChatGPT has really been a force multiplier for me personally,” he says. “It feels like I have a team working for me. It’s helped me get out of the weeds and focus more on the work that matters.”

## Join the new era of work

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