How AI-native companies turn workflows into operating capability

OpenAI’s latest Enterprise Signals shows enterprise AI moving from assistance to execution at sharply different speeds. Frontier firms (those with the top 10% of AI usage) now generate 8.3× as many output tokens per active user as typical firms, up from 2.6× in January. The widening gap points to a deeper operating shift: leading firms connect agents to company context and tools, delegate more substantive work, and make successful workflows easier to repeat. OpenAI 最新的《企业信号》（Enterprise Signals）报告显示，企业级 AI 正以截然不同的速度从“辅助”向“执行”转型。前沿企业（AI 使用率排名前 10% 的公司）目前每位活跃用户的输出 token 数量是普通企业的 8.3 倍，而这一数字在 1 月份仅为 2.6 倍。这种不断扩大的差距预示着更深层次的运营变革：领先企业已将智能体与公司业务背景及工具相连接，能够委派更实质性的工作，并使成功的业务流程更易于复用。

For leaders, the challenge is to turn that depth into work people can trust, measure, and improve. Leaders should also leave room for experimentation, including use cases whose value is not obvious on the first try.对于领导者而言，挑战在于如何将这种深度转化为人们可以信任、衡量并改进的工作成果。领导者还应为实验留出空间，包括那些初次尝试时价值并不明显的用例。

Startups Basis⁠(opens in a new window), Clay⁠(opens in a new window), and Exa Labs⁠(opens in a new window) have built agents into employee onboarding, account management, and developer ecosystem growth. Their workflows differ, but the progression is instructive: teach an agent a stable process, give it persistent context as work changes, then let it carry opportunities into tested action.初创公司 Basis (在新窗口中打开)、Clay (在新窗口中打开) 和 Exa Labs (在新窗口中打开) 已将智能体构建到员工入职、账户管理和开发者生态系统增长等环节中。尽管它们的工作流程各不相同，但其演进路径具有启发意义：教会智能体一个稳定的流程，在工作变动时为其提供持续的背景信息，然后让它将机会转化为经过测试的行动。

Together, these examples show how teams can build agents into familiar work and improve the process over time.这些案例共同展示了团队如何将智能体融入日常工作，并随着时间的推移不断优化流程。

Onboarding has long been cumbersome for employers and employees. At Basis, which builds AI agents for accounting firms, first-day onboarding now takes 30 minutes instead of two hours, giving HR more time for culture and support.长期以来，员工入职对雇主和员工来说都是一件繁琐的事。Basis 是一家为会计师事务所构建 AI 智能体的公司，现在其首日入职流程已从两小时缩短至 30 分钟，让 HR 有更多时间专注于企业文化建设和员工支持。

On day one, employees receive immediate access to Codex and a company-specific onboarding skill (a reusable set of instructions and resources for a specific workflow). Codex welcomes them, introduces key company concepts, and uses their computer to complete integration setup in the background. When recurring questions or exceptions appear, HR can update the skill before the next cohort.入职第一天，员工即可获得 Codex 的访问权限以及公司特定的“入职技能”（一套针对特定工作流程的可复用指令和资源）。Codex 会向他们表示欢迎，介绍公司关键理念，并在后台利用他们的电脑完成集成设置。当出现重复性问题或异常情况时，HR 可以在下一批员工入职前更新该技能。

Basis demonstrated the onboarding process once, then turned it into a reusable skill with a clear trigger, known steps, access to the right tools, and a clear definition of “done.” The process no longer depends on one person’s availability, yet the team can step in for exceptions or complex questions. Onboarding is now more consistent, repeatable, and easier to improve, and new employees gain an immediate model for working with AI.Basis 先演示了一遍入职流程，然后将其转化为一种可复用的技能，包含明确的触发条件、已知的步骤、对必要工具的访问权限以及清晰的“完成”定义。该流程不再依赖于特定人员的空闲时间，同时团队仍可在遇到异常或复杂问题时介入。入职流程现在变得更加一致、可重复且易于改进，新员工也由此获得了与 AI 协作的即时模型。

Clay, a company building a self-learning revenue engine for go-to-market teams, faces a familiar sales challenge: critical deal context is scattered across CRM records, email, Slack, calls, presentations, text messages, and conversations with internal teams and customer champions.Clay 是一家为市场推广团队构建自学习营收引擎的公司，他们面临着一个常见的销售挑战：关键的交易背景信息分散在 CRM 记录、电子邮件、Slack、通话、演示文稿、短信以及与内部团队和客户负责人的沟通中。

To keep that context current, one of Clay’s GTM engineers experimented with a better approach—a persistent workspace and dedicated subagent for every account. Each subagent reviews primary sources and updates its deal folder overnight. Every morning, a coordinating agent turns those updates across all her accounts into a short list of priority moves: answer a lingering customer question, fill a gap in the buying committee, or give a prospect a reason to re-engage.为了保持这些背景信息的实时性，Clay 的一名市场推广工程师尝试了一种更好的方法——为每个账户建立一个持久化的工作空间和专属子智能体。每个子智能体会在夜间审查主要来源并更新其交易文件夹。每天早上，一个协调智能体会将所有账户的更新信息整理成一份简短的优先事项清单：回答客户悬而未决的问题、填补购买委员会中的信息缺口，或为潜在客户提供重新互动的理由。

The workflow saves her roughly an hour of inbox triage each night, according to Clay. The daily priorities help her follow through on the small actions that can add up over a long enterprise sales cycle. The supporting evidence stays close to each recommendation, so sellers can inspect the primary sources before acting. Enterprises could extend that shared context to account executives, BDRs, solutions engineers, and sales leaders, subject to existing account permissions.据 Clay 称，该工作流程每晚为她节省了大约一小时的收件箱分类时间。这些每日优先事项有助于她跟进那些在漫长的企业销售周期中积少成多的细小行动。支持性证据紧邻每项建议，因此销售人员在采取行动前可以先核实原始来源。在现有账户权限范围内，企业可以将这种共享背景信息扩展至客户经理、业务开发代表 (BDR)、解决方案工程师和销售主管。

Clay shows what evolving work needs in order to scale: a consistent structure, a useful refresh cadence, shared evidence, and human judgment at the point of action.Clay 展示了演进中的工作模式实现规模化所需具备的要素：一致的结构、有效的刷新频率、共享的证据，以及在行动点上的关键人工判断。

Exa Labs, which builds web search infrastructure for AI agents, wants to make its search API available wherever developers could use it. The team calls this goal “Exa everywhere.” Pursuing it once required developer relations and account teams to monitor repositories and the wider ecosystem, identify promising integrations, gather context, and coordinate work across systems. Although the opportunities varied, the path from discovery to implementation followed a consistent sequence. Exa turned that sequence into a defined workflow for Codex, with clear priorities, access to the necessary sources, and human review before anything ships.Exa Labs 为 AI 智能体构建网络搜索基础设施，其目标是让开发者在任何需要的地方都能使用其搜索 API。团队将这一目标称为“Exa 无处不在”。过去，追求这一目标需要开发者关系团队和客户团队监控代码库及更广泛的生态系统，识别有潜力的集成项目，收集背景信息，并跨系统协调工作。尽管机会各异，但从发现到实施的路径遵循着一致的顺序。Exa 将这一顺序转化为 Codex 的明确工作流程，设定了清晰的优先级，确保其能访问必要资源，并在任何内容发布前进行人工审核。

Codex now monitors for high-priority integration opportunities, gathers the relevant context, creates pull requests, runs tests, and prepares weekly updates using sources such as Slack and Notion. When appropriate, it can also draft the next step, including an initial announcement, for the team to review. The workflow carries an opportunity from signal to tested artifact while reducing handoffs across research, engineering, and communication.Codex 现在负责监控高优先级的集成机会，收集相关背景信息，创建拉取请求 (pull requests)，运行测试，并利用 Slack 和 Notion 等来源准备每周更新。在适当的情况下，它还可以起草下一步计划（包括初步公告）供团队审核。该工作流程将机会从信号转化为经过测试的交付成果，同时减少了研究、工程和沟通部门之间的交接工作。

People still decide which opportunities matter, which commitments Exa should make, and how external relationships should be managed. Tests and review points make the agent’s work visible before it ships. Test results and human review can show the team where to adjust the workflow before the next run. As the work becomes more consequential, permissions, evidence, and decision rights become a larger part of the workflow design. 人们仍然负责决定哪些机会重要、Exa 应该做出哪些承诺，以及如何管理外部关系。测试和审核点确保了智能体的工作在发布前是可见的。测试结果和人工审核可以向团队展示在下一次运行前应在何处调整工作流程。随着工作变得越来越重要，权限、证据和决策权在工作流程设计中占据了更大的比重。

Together, Basis, Clay, and Exa put the patterns we see across Enterprise Signals into operational terms. Basis turns a proven process into a reusable skill. Clay gives an agent the context and persistence to keep an evolving body of work current. Exa adds tools, tests, and review so an agent can carry a signal into bounded execution. All three make improvement part of the workflow: onboarding exceptions reveal where a skill needs refinement; new account activity and seller validation keep deal context current; and tests and human review sharpen the boundaries for future execution. Each starts with a specific job and enough room to test it. The division of labor becomes clearer through use, and responsibility expands as the workflow proves itself.Basis、Clay 和 Exa 共同将我们在《企业信号》中观察到的模式转化为可操作的术语。Basis 将成熟的流程转化为可复用的技能；Clay 为智能体提供背景信息和持久性，以保持不断演进的工作内容处于最新状态；Exa 增加了工具、测试和审核环节，使智能体能够将信号转化为有边界的执行。这三者都将改进纳入了工作流程：入职过程中的异常情况揭示了技能需要优化的方向；新的账户活动和销售人员验证确保了交易背景的实时性；测试和人工审核则为未来的执行划定了更清晰的界限。每一个案例都始于一项具体的工作，并留出了足够的测试空间。通过使用，分工变得更加明确，随着工作流程的成熟，责任范围也随之扩大。

With the frontier gap widening, enterprise leaders need to give employees room to test consequential workflows, measure success, and turn the strongest experiments into repeatable practice. 随着前沿差距的扩大，企业领导者需要为员工留出空间，去测试重要的工作流程、衡量成功与否，并将最有效的实验转化为可重复的实践。

• Choose one consequential value surface. Start with an end-to-end workflow where a strategic priority, systems, handoffs, controls, and measurable stakes meet. It should repeat often enough to learn from and matter enough to justify redesign.选择一个具有重要价值的领域。从一个端到端的工作流程开始，将战略优先级、系统、交接、控制和可衡量的风险结合起来。它应该足够频繁地重复以便从中学习，并足够重要以证明重新设计的必要性。
• Define the outcome and how you will measure it. Name the accountable owner, KPI, baseline, and guardrails. Track depth through completed tasks, connected context and tools, exceptions, and review load. Track value through cycle time, quality, cost, revenue, or risk. Output volume can show that people are asking AI to do more; workflow outcomes show whether it matters.定义结果及其衡量方式。确定负责人、关键绩效指标 (KPI)、基准和护栏。通过已完成的任务、连接的背景和工具、异常情况及审核工作量来追踪深度。通过周期时间、质量、成本、收入或风险来追踪价值。输出量可以显示人们正在让 AI 做更多的事情；而工作流程的结果则显示这些事情是否重要。
• Write the agent’s job description. Define what triggers the work, the outcome, required context, tools, permissions, and how persistently the agent should work toward completion. Specify what evidence it must produce and where it must stop for human review.编写智能体的职位描述。定义触发工作的条件、预期结果、所需背景信息、工具、权限，以及智能体应以多高的持久性来推进工作直至完成。明确它必须产生什么证据，以及在何处必须停止以等待人工审核。
• Build the human system around the agent. Put the people closest to the workflow in the design loop. Name who owns the business outcome, domain logic, access and controls, adoption, and daily use. Startups compress these responsibilities into a few people; enterprises need explicit decision rights as the workflow scales.围绕智能体构建人工系统。将最了解工作流程的人员纳入设计环节。明确谁负责业务成果、领域逻辑、访问和控制、采用率以及日常使用。初创公司将这些职责压缩在少数人身上；而随着工作流程的扩展，企业需要明确的决策权。
• Make experimentation visible and reusable. OpenAI research finds that six months after adoption, early-career employees sent 13 more messages per week than executives. Give employees room to test new use cases, then capture the process and evidence behind what works and package it as skills, Plugins, or shared workspaces. Chat, Work, and Codex⁠(opens in a new window) support different modes: Chat for questions and quick collaboration, Work for multi-step knowledge work and finished deliverables, and Codex for technical execution.让实验过程可见且可复用。OpenAI 的研究发现，在采用 AI 六个月后，初级员工每周发送的消息比高管多 13 条。给予员工空间去测试新的用例，然后捕获成功实践背后的流程和证据，并将其打包为技能、插件或共享工作空间。Chat、Work 和 Codex (在新窗口中打开) 支持不同的模式：Chat 用于提问和快速协作，Work 用于多步骤知识工作和成品交付，Codex 用于技术执行。
• Carry the operating pattern forward. Preserve the context, permissions, evaluations, review points, owners, measures, and enablement that worked, then apply them to the next value surface. Each new experiment should give the next team a better place to start.将运营模式传承下去。保留那些行之有效的背景信息、权限、评估、审核点、负责人、衡量标准和赋能措施，然后将其应用于下一个价值领域。每一次新的实验都应为下一个团队提供更好的起点。