--- 格式版本: 2 标题: "From assistance to execution: How enterprises put AI to work | OpenAI" 原文链接: "https://openai.com/index/how-enterprises-put-ai-to-work" 发布日期: "2026-08-12" 发布时间校准状态: "found" 发布时间需复核: "否" 发布时间来源: "manual" 发布时间证据: "正文中标题附近明确标注'August 12, 2026'，符合发布时间优先级。" 发布时间校准原因: "正文中标题附近明确标注'August 12, 2026'，符合发布时间优先级。" 发布时间校准置信度: "manual-confirmed" 发布时间候选数量: 0 发布时间严格候选数量: 0 发布时间原页读取状态: "manual" 发布时间未找到原因: "" 发布时间校准时间: "2026-08-13T11:22:34+08:00" 发布时间仲裁状态: "manual" 发布时间仲裁尝试次数: 0 发布时间仲裁耗时毫秒: 0 发现时间: "2026-08-13T00:42:25+08:00" 入库时间: "2026-08-12T16:43:24.742Z" 来源平台: "固定入口" 搜索渠道: "fixed_url" 搜索词: "https://openai.com/news/" 匹配关键词: - "AI" 相关厂家: - "OpenAI" 相关专家: [] 内容类型: "网页" 抓取工具: "CDP Render" 清洗工具: "CDP Text + Defuddle/Readability 正文提取" 原始附件: [] AI优质: "否" AI打分: 20 AI分档: "非优质" AI质检状态: "不通过" AI打分理由: "讨论企业级AI应用（ChatGPT/Codex），无超节点、AI Rack、硬件架构、供电、散热、互连等硬件技术信号，与项目关注范围无关。" AI质检模型: "ali-deepseek-v4-flash" AI质检时间: "2026-08-13T00:43:33+08:00" AI主题相关性: 0 AI来源权威性: 10 AI新颖性: 5 AI技术细节: 0 AI商业部署信号: 0 AI完整性: 5 AI摘要: "OpenAI发布两份企业AI采用报告，显示企业正从“询问式”AI转向“执行式”智能体，前沿企业每月每活跃用户生成的输出token数已达典型企业的8.3倍。" AI摘要模型: "ali-deepseek-v4-flash" AI摘要时间: "2026-09-07T03:31:50.895Z" 采集批次: "2026年8月12日20点58分23秒" 采集批次ID: "20260812-205823-234" 去重键: "https://openai.com/index/how-enterprises-put-ai-to-work" --- Two new reports show how AI adoption is spreading across firms and workers—and what frontier organizations are doing differently. Organizations are expanding both where they use AI and what they ask it to do. Enterprise AI is moving from asking to doing, yet not all firms are making that transition at the same pace. Frontier firms—those in the top 10% of AI usage each month—now generate 8.3× as many output tokens per active user as typical firms. The measure is a proxy for depth of use, and the widening gap appears alongside greater adoption of capabilities that connect agents to company context, tools, and repeatable workflows. Today we are publishing two complementary studies that examine this shift. [Enterprise Signals](https://openai.com/signals/enterprise-data/) leads with a practical view of agentic AI across OpenAI’s enterprise customer base, including what frontier firms are doing differently and where agentic work is spreading. The companion working paper, [How Organizations Use AI: Evidence from ChatGPT⁠](https://cdn.openai.com/pdf/how-organizations-use-chatgpt.pdf), examines how adoption grows across companies, roles, and levels of seniority. Together, the studies point to a practical enterprise agenda: connect agents to the context and tools needed to complete valuable work; establish clear permissions, review, and governance; and help employees turn effective individual workflows into shared ways of working. These reports reveal five key insights on how organizations are putting AI to work: - **Enterprise use is becoming more agentic.** As of June, Codex generated 64% of combined Codex and ChatGPT output tokens among enterprise customers, suggesting that agents are enabling a shift toward more substantive, delegated work. - **The frontier gap is widening.** Frontier firms—those in the top 10% of AI usage each month—now generate 8.3× as many output tokens per active user as typical firms, up from 2.6× in January. - **Frontier firms use advanced capabilities more often.** Each week, 21% of active users at frontier firms use Plugins, compared with 9% at typical firms. At OpenAI, 95% of employees use Plugins weekly, highlighting the potential for deeper adoption. - **Agents are spreading across knowledge work.** Since February, weekly active enterprise Codex users grew 108× in legal, 41× in sales, 41× in recruiting, and 26× in marketing, compared with 5× in engineering. - **Early-career employees use AI more.** Usage is highest among early-career workers and falls among more senior employees, suggesting a potential comparative advantage in using AI. ## Enterprises are shifting from asking to doing Enterprise AI is moving from answering questions to carrying out work. Assistants help people think through work; agents help them complete it. Products like ChatGPT Work and Codex can use tools, create files, and produce work for review. Instead of asking AI how to prepare a presentation, for example, a worker can ask an agent to gather relevant information across sources and draft the presentation itself. This shift is visible in enterprise usage. As of June, Codex generated 64% of combined Codex and ChatGPT output tokens among enterprise customers. Agentic workflows typically generate more output because they carry out longer, multi-step tasks, so the figure reflects both how often Codex is used and how much output those tasks produce. ## The frontier gap widens as firms embrace agentic AI Each month, we rank enterprise customers by output tokens per active user. Frontier firms are those in the top 10% that month, while typical firms fall between the 45th and 55th percentiles. As of June, frontier firms generated 8.3× as many output tokens per active user as typical firms, a threefold increase compared to the 2.6× gap in January. The frontier gap appears across industries and company sizes, showing that intensive AI use is not limited to technology companies. [Enterprise Signals](https://openai.com/signals/enterprise-data/) examines how the gap varies across industries and functions. Among the U.S. public companies studied in [How Organizations Use AI: Evidence from ChatGPT⁠](https://cdn.openai.com/pdf/how-organizations-use-chatgpt.pdf), enterprise adopters had stronger financial measures compared to non-adopters. They held more assets, employed more workers, and had higher levels of R&D investment. Read together, the reports suggest that access alone may not be enough to scale AI. Complementary investments in continuous employee learning, shared workflows, data infrastructure, and governance can support broader and deeper adoption. AI agents need access to the right context and tools to be effective. [Plugins ⁠](https://chatgpt.com/features/plugins/?openaicom-did=73936287-a6d2-4adb-92af-087bea453b09&openaicom_referred=true) bundle capabilities that help agents complete specific workflows. They can combine *skills* that provide reusable instructions with *apps* that connect to company data, tools, and actions. For example, a sales Plugin can combine a team’s playbook with access to its CRM, allowing an agent to use current customer information and past proposals to prepare a tailored response for review. Frontier firms have a clear lead in advanced capabilities. Among weekly active users, 21% at frontier firms use Plugins and 19% use skills, compared with just 9% and 3% at typical firms. However, frontier firm adoption represents only a fraction of what is possible. OpenAI’s internal usage highlights the potential for deeper usage of these capabilities, with weekly Plugin usage at 95% of active users. Our research on [how agents are transforming work](https://openai.com/index/how-agents-are-transforming-work/) provides a closer look at how employees at OpenAI use advanced capabilities. ## Agents are spreading across knowledge work Software engineering was an early center of agentic adoption, but Codex use is now growing quickly across knowledge-work functions. Since February, weekly active enterprise Codex users grew 108× in legal, 41× in sales, 41× in recruiting, and 26× in marketing, compared with 5× in engineering. At [Virgin Atlantic](https://openai.com/index/virgin-atlantic/), that shift is visible across the business. Engineering teams use Codex to refactor legacy code in 30 minutes instead of two weeks. Their product teams use [ChatGPT Work](https://openai.com/index/chatgpt-for-your-most-ambitious-work/) to complete weeks of competitive research in hours, shaping the airline’s five-year digital strategy. [Enterprise Signals](https://openai.com/signals/enterprise-data/) explores how these capabilities are spreading across industries and functions, drawing on a sample of more than 10 million messages. ## Early-career employees use AI more Many surveys have reported higher levels of AI use among leaders and executives. However, administrative data from millions of conversations finds the opposite. Six months after adoption, early-career employees sent 13 more messages per week than executives. For leaders, the result points to a practical opportunity to identify employees with the strongest AI habits and make their workflows visible, helping effective practices spread across all levels. ## What leaders can do to narrow the frontier gap Companies may have access to the same models, but frontier firms are putting them to work faster and more deeply across their organizations. The opportunity for leaders is to close the frontier gap by extending agentic workflows beyond engineering and turning successful individual use cases into repeatable practices across the organization. By connecting agents to company context and tools, with clear permissions, governance and human review, firms can move from assistance to execution. Read [Enterprise Signals](https://openai.com/signals/enterprise-data/) for deeper insights into how frontier firms are putting AI to work across industries and functions. OpenAI Enterprise customers can also [request](https://openai.com/signals/enterprise/intake-form/) a customized benchmark to see how their organization compares against frontier firms. Read [How Organizations Use AI: Evidence from ChatGPT⁠](https://cdn.openai.com/pdf/how-organizations-use-chatgpt.pdf) to understand how adoption spreads across firms and workers.