# How to become "AI-Native"
**作者**: GREG ISENBERG
**日期**: 2026-05-11T14:23:53.000Z
**来源**: [https://x.com/gregisenberg/status/2053843542020063489](https://x.com/gregisenberg/status/2053843542020063489)
---
The truth about being AI native. I'll break it down.
Thinking about AI workflows
Everyone is walking around saying they’re “AI-native” now, which mostly means someone on the team has a ChatGPT tab open and the head of marketing made a custom GPT called “Brand Voice Assistant.”
Cute.
Useful, even.
But not AI-native.
That’s the difference people keep missing. An AI-native company is not a company that uses AI. It is a company that has been rebuilt so AI can actually operate inside it. The business is structured, documented, permissioned, and instrumented in a way that agents can understand. The company has made itself legible to machines.
That sounds boring until you realize it might be the single biggest business advantage of the next decade.
Because most companies are not legible to machines. Most companies are barely legible to their own employees.
The CRM says one thing. The Slack thread says another. The real customer history lives in someone’s inbox. The pricing logic is in a spreadsheet called “Final_v7_NEW.” The refund policy is in a Notion doc nobody trusts. The sales process is “talk to Sarah, she knows how we do enterprise.” The onboarding flow is five tools, three humans, two approval steps, and one founder who still gets pulled into random edge cases because nobody ever turned judgment into a system.
Then these companies ask, “Why can’t AI do more for us?”
Because AI cannot run on vibes.
It can’t operate a business where the truth is scattered across people, tools, habits, exceptions, and institutional memory. Agents need context. They need clean inputs. They need rules. They need access. They need boundaries. They need to know what good looks like. They need to know when to act and when to ask.
Most companies have spent twenty years buying software, but they have not spent twenty years designing an operating system. They have a pile of tools, not a machine.
That is why the number of truly AI-native companies is probably shockingly small. My guess is there are maybe 1,000 companies on earth doing $5M+ ARR that are actually AI-native in the real sense. Not “we use copilots.” Not “we automated some emails.” I mean companies where the core workflows are designed for agents to execute and humans to supervise.
Maybe the number is 500. Maybe it’s 2,000. The exact number matters less than the conclusion.
Almost nobody is doing this yet.
Despite all the noise, despite all the funding announcements, despite every SaaS homepage being rewritten with the word “agentic,” the field is basically empty.
The first useful distinction is this: AI-assisted companies use AI at the edges. AI-native companies redesign the center.
An AI-assisted company asks, “Where can we add AI to save time?”
An AI-native company asks, “How should this workflow exist if agents are doing the first 80%?”
That second question changes everything.
Take customer support. In a normal company, a support ticket arrives, a human reads it, searches for context, checks the account, remembers policy, writes a response, maybe asks engineering, maybe escalates, maybe forgets to tag the reason properly. It’s a human-driven process with software sprinkled around it.
In an AI-native company, the ticket enters a system an agent can understand. The agent reads the customer history, checks plan limits, reviews prior tickets, consults policy, drafts a response, recommends an action, and either resolves the issue or sends it to a human with the exact reason it needs judgment. The human is not the search engine, router, and copywriter. The human is the reviewer of ambiguity.
That is a very different company.
Now apply the same logic to sales. The old way is an SDR Googling a prospect, guessing at personalization, writing a mediocre email, updating Salesforce because their manager nags them, then passing half-context to an AE. The AI-native way is an agent that monitors buying signals, enriches accounts, maps stakeholders, drafts outreach, learns which hooks convert, updates the CRM automatically, and gives the human seller a prepared conversation instead of a blank page.
Legal is the same. Recruiting is the same. Finance is the same. Claims processing is the same. Account management is the same. Research is the same.
The pattern repeats everywhere: agents do the structured work, humans handle taste, trust, judgment, relationships, and exceptions.
That is not a small productivity improvement. That is a new management model.
For the last hundred years, the default way to scale a company was to hire more people, create departments, add managers, buy software, and invent processes to coordinate the mess. Every new layer solved one problem and created three more. The company got bigger, but it also got slower. More meetings. More handoffs. More “who owns this?” More internal gravity.
AI-native companies will scale differently.
They will not look like traditional companies with a chatbot bolted on. They will look like small teams operating large fleets of specialized agents. A 12-person company will do what used to require 80 people. A 40-person company will compete with a 400-person incumbent. Revenue per employee will become one of the clearest signals that a company is actually built for the new era.
This is where a lot of people get defensive. They hear “agents do the work” and assume it means humans disappear.
That’s not the point.
The better way to think about it is that modern companies have been wasting human intelligence on machine-shaped tasks. We use humans to move information between tools. We use humans to remember process. We use humans to search folders. We use humans to rewrite the same email. We use humans to chase approvals. We use humans to summarize calls, fill in fields, copy data, classify requests, and ask other humans where something lives.
A lot of work is not really “work.” It is organizational friction wearing a fake mustache.
AI-native companies strip that out.
They preserve the human parts that matter and automate the parts that only existed because software was too dumb to understand context. That means the human role becomes more leveraged, not less important. A great operator becomes the supervisor of ten workflows. A great salesperson becomes the closer of conversations agents helped create. A great support lead becomes the designer of escalation logic and customer experience quality. A great founder becomes the architect of how the company thinks.
That founder point is important.
The AI-native founder is not just building a product. They are designing a company that can be understood by agents.
That means the founder has to make the implicit explicit. What is our refund policy? When do we break it? What makes a lead qualified? What tone do we use with angry customers? What should never be automated? Which actions require approval? What is a good answer? What is a dangerous answer? Which data source is the source of truth? What do we do when two systems disagree? How does the agent learn from corrections?
This is the unsexy work that will separate real AI-native companies from LinkedIn theater.
Everyone wants the magic. Nobody wants to clean the kitchen.
But the kitchen is the company.
The companies that win will do boring, foundational things with unusual seriousness. They will clean their data. They will document their workflows. They will create agent-readable SOPs. They will build permissions and audit trails. They will structure customer records so context is not trapped in human memory. They will create evaluation loops so agents get better over time. They will turn every repeated decision into a decision system.
Then, once the operating layer is clean, they will move absurdly fast.
This is why “AI-native” is not really a tech label. It is an organizational label.
A company can use the best models in the world and still be structurally incapable of benefiting from them. If the agent has to guess where the truth lives, if it cannot access the right systems, if nobody has defined the decision rules, if every workflow depends on exceptions buried in someone’s head, then the AI will remain a toy. It will draft things. It will summarize things. It will make people feel faster. But it will not transform the business.
The transformation happens when agents become part of the operating fabric.
Imagine a home services company that is truly AI-native. Every inbound request is classified automatically. Every quote is generated from structured pricing rules. Every technician gets a job summary before arrival. Every customer receives proactive updates. Every review request is personalized. Every missed appointment creates an automatic recovery workflow. Every operational pattern feeds back into routing, pricing, and staffing.
Now imagine an insurance brokerage. Agents gather documents, pre-check submissions, compare policies, flag missing details, draft client explanations, prepare renewal options, and monitor accounts for changes. Humans build trust and handle complexity, but the machinery underneath is doing the repetitive intelligence work all day.
Now imagine a recruiting firm. Agents source candidates, enrich profiles, compare against role requirements, draft outreach, summarize interviews, check references, update pipelines, and alert humans when a candidate is unusually strong. The recruiter stops being a data janitor and becomes a relationship closer.
These are not sci-fi companies. These are normal businesses with the guts rebuilt.
That’s the opportunity people are underestimating. The obvious AI companies are crowded. Horizontal copilots, writing tools, meeting bots, code assistants, image generators, customer support wrappers. Fine businesses, but obvious. The less obvious opportunity is taking boring, profitable, fragmented industries and rebuilding the operating model around agents.
AI-native agencies. AI-native brokerages. AI-native law-adjacent services. AI-native accounting firms. AI-native compliance shops. AI-native healthcare admin companies. AI-native real estate operations. AI-native education services. AI-native logistics coordinators. AI-native BPOs that don’t look like BPOs.
The world is full of industries where customers pay for outcomes, but the provider’s cost structure is mostly repetitive knowledge work. That is exactly where AI-native companies can wedge in.
The best opportunities will not always look like software companies at first. Some will look like services businesses with software margins hiding inside. That will confuse investors and competitors, which is useful. While everyone else is looking for the next SaaS dashboard, the real winners may be quietly building AI-native service companies that produce better outcomes with dramatically lower labor intensity.
This is a very Greg thing to say, but I think the next wave of internet businesses may look less like “startups” and more like weird little money machines.
Small teams. Narrow markets. Proprietary workflows. High automation. High trust. Clear customer pain. Boring category. Beautiful margins.
Not sexy from the outside.
Extremely sexy in the bank account.
And because these companies are built differently from day one, incumbents will struggle to copy them. An old company cannot become AI-native by announcing an AI initiative. That is like trying to turn a cruise ship into a speedboat by buying a new steering wheel.
The hard part is not access to models. Everyone has that.
The hard part is that incumbents are full of old process debt. Their data is messy. Their policies conflict. Their teams protect turf. Their workflows were built around headcount. Their software stack is stitched together with duct tape and quarterly planning rituals. Their operating system assumes humans are the default processors of information.
A new company has the advantage of having no furniture to move.
It can start clean. It can build every process with the question: “Could an agent do the first pass on this?” It can document from day one. It can make every data object usable. It can design human review points before errors become disasters. It can build feedback loops before the company calcifies.
This is why the “only 1,000 companies” idea matters. It creates urgency, but it also creates permission.
The field is empty because most people are still mistaking AI adoption for AI architecture.
They think the game is prompt engineering. It’s not.
They think the game is picking the right model. It’s not.
They think the game is adding a chatbot to the website. It’s definitely not.
The game is redesigning the company so intelligence can flow through it.
There is a practical playbook here.
First, pick a narrow workflow with obvious economic value. Don’t start with “make the company AI-native.” That’s too abstract. Start with support resolution, outbound prospecting, onboarding, claims intake, document review, renewal management, or reporting. Choose a workflow where volume is high, rules exist, and humans are currently doing too much coordination.
Second, map the workflow like a machine. What triggers it? What data is needed? What decisions happen? Which decisions are reversible? Which require approval? What does success look like? Where do errors happen? What does a human know that the system does not?
Third, structure the knowledge. If the agent needs a policy, write the policy. If it needs pricing rules, make them explicit. If it needs customer history, clean the customer object. If it needs examples, create examples. If it needs tone, define tone. This is where most teams quit, because it feels like documentation. It is not documentation. It is infrastructure.
Fourth, put agents in the workflow with boundaries. Let them draft, classify, recommend, enrich, summarize, and prepare. Give them actions only where the risk is understood. Require approval where judgment matters. Log everything. Review outputs. Track quality. Improve the system.
Fifth, measure the business impact. Not “hours saved” in some fake spreadsheet. Measure resolution time, conversion rate, gross margin, revenue per employee, error rate, customer satisfaction, sales velocity, onboarding time, renewal rate. AI-native companies should show up in the numbers.
That is the part I’m most interested in. In a few years, “AI-native” will not be a vibe. It will be visible in the metrics.
Revenue per employee will look different.
Gross margins will look different.
Speed of execution will look different.
Customer experience will look different.
The best companies will feel strangely responsive, like the whole business is awake. Customers will get answers faster. Sales teams will follow up with better timing. Ops problems will surface earlier. Founders will see the business more clearly. Managers will spend less time asking for updates and more time improving the system.
The company will have less drag.
That is the real advantage.
Not AI as a party trick. AI as organizational metabolism.
So yes, there are probably only around 1,000 truly AI-native companies on earth doing meaningful revenue today.
And that should make you want to build one immediately.
Because when a market is loud, people assume it is mature. But noise is not maturity. Noise is usually what happens right before the real builders figure out what matters.
Right now, everyone is loud about AI.
Very few companies are structurally ready for it.
That is the gap.
That is the opportunity.
The next great companies will be the ones whose data, workflows, policies, and teams are rebuilt around agents from the inside out. They will look smaller than they should. They will move faster than makes sense. They will have fewer employees doing more valuable work. They will turn messy services into scalable systems. They will make incumbents look like they are running Windows 95 with a nicer login screen.
Most people are still asking, “How do I use AI at work?”
The better question is, “How do I build a company AI can work inside?”
That question is the doorway.
And right now, almost nobody has walked through it.
Despite what you read, the field is empty. Maybe consider sharing this with a friend.
I’m rooting for you.
- Greg Isenberg
Note: I don't know about it often because we're swamped, but my firm LCA is world class at helping companies go AI native. Because they do really good work. We work with Fortune 500s and your favorite brands on building AI native products and AI native orgs.
If your company wants to go AI native, consider contacting them up here.
And if you're looking for startup ideas, consider grabbing some validated ideas you can build with AI at Ideabrowser.com
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---
*导出时间: 2026/5/12 09:26:53*
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## 中文翻译
# 如何成为“AI 原生”
**作者**： GREG ISENBERG
**日期**： 2026-05-11T14:23:53.000Z
**来源**： [https://x.com/gregisenberg/status/2053843542020063489](https://x.com/gregisenberg/status/2053843542020063489)
---
关于成为 AI 原生的真相。我来拆解一下。
思考 AI 工作流
如今每个人都在说自己“AI 原生”，这大多意味着团队里有人开着 ChatGPT 标签页，营销负责人做了一个叫“品牌语调助手”的自定义 GPT。
挺可爱的。
甚至有点用。
但这不是 AI 原生。
这就是人们一直忽略的区别。AI 原生公司不是使用 AI 的公司，而是经过重构、让 AI 能够真正在其中运转的公司。业务以智能体能够理解的方式被结构化、文档化、授权和监测。公司让自己对机器变得“可读”。
这听起来很无聊，直到你意识到这可能是未来十年最大的商业优势。
因为大多数公司对机器并不可读。大多数公司对自己的员工都几乎不可读。
CRM 里说的是一回事。Slack 讨论串里是另一回事。真实的客户历史存在于某个人的收件箱里。定价逻辑在一个叫“Final_v7_NEW”的电子表格里。退款政策在一个没人信任的 Notion 文档里。销售流程是“去问 Sarah，她知道我们怎么做企业客户的”。入职流程是五个工具、三个人、两道审批环节，外加一位仍会被拉进各种随机边缘案例的创始人，因为从来没人把判断力变成一个系统。
然后这些公司会问：“为什么 AI 不能为我们做更多事情？”
因为 AI 无法靠感觉运转。
它无法运营一个真相散落在人、工具、习惯、例外和组织记忆之中的企业。智能体需要上下文。它们需要干净的输入。它们需要规则。它们需要权限。它们需要边界。它们需要知道什么是好的结果。它们需要知道何时行动、何时请示。
大多数公司花了二十年购买软件，却没有花二十年设计一个操作系统。他们拥有一堆工具，而不是一台机器。
这就是为什么真正 AI 原生的公司数量可能少得惊人。我猜测，地球上做 500 万美元以上 ARR 的公司中，真正意义上 AI 原生的可能只有 1000 家左右。不是“我们用了 copilot”，不是“我们自动化了一些邮件”。我指的是那些核心工作流是围绕智能体执行、人类监督而设计的公司。
也许数字是 500。也许是 2000。确切数字没有结论重要。
几乎还没有人在做这件事。
尽管喧嚣不断，尽管融资公告频发，尽管每个 SaaS 首页都改写成了“智能体”字眼，这片领域基本上还是空的。
第一个有用的区分是：AI 辅助公司在边缘使用 AI。AI 原生公司重新设计中心。
AI 辅助公司会问：“我们可以在哪里加入 AI 来节省时间？”
AI 原生公司会问：“如果智能体完成前 80%，这个工作流应该以怎样的形态存在？”
第二个问题改变一切。
拿客户支持来说。在普通公司，一个支持工单到达，一个人阅读它，搜索上下文，检查账户，回忆政策，写一个回复，也许去问工程部门，也许升级处理，也许忘了正确标记原因。这是一个以人为驱动的流程，软件只是点缀。
在 AI 原生公司，工单进入一个智能体可以理解的系统。智能体阅读客户历史，检查套餐限额，回顾过往工单，查阅政策，起草回复，推荐行动方案，然后要么解决问题，要么带着“为什么需要人工判断”的确切理由转给人类。人类不是搜索引擎、路由器和文案写手。人类是处理模糊性的审核者。
这是非常不同的公司。
现在把同样的逻辑应用到销售。老方法是 SDR 用谷歌搜索潜在客户，猜测如何个性化，写一封平庸的邮件，因为经理催促才更新 Salesforce，然后把一半的上下文交给 AE。AI 原生方法是让智能体监控购买信号，丰富账户信息，绘制利益相关者图谱，起草外联内容，学习哪些开场白能带来转化，自动更新 CRM，并为人类销售准备一场有备而来的对话，而不是一张白纸。
法务也一样。招聘也一样。财务也一样。理赔处理也一样。客户管理也一样。研究也一样。
这个模式在所有地方重复出现：智能体做结构化的工作，人类处理品味、信任、判断、关系和例外。
这不是小的生产力提升。这是一种全新的管理模式。
在过去一百年里，扩张公司的默认方式是雇佣更多人，创建部门，增加经理，购买软件，发明流程来协调混乱。每增加一层解决了其中一个问题，又制造了三个新问题。公司变大了，但也变慢了。更多会议。更多交接。更多“这个谁负责？”更多内部摩擦。
AI 原生公司将以不同的方式扩张。
它们看起来不会是加装了聊天机器人的传统公司。它们看起来像运营着庞大专业化智能体舰队的小团队。一家 12 人的公司将做到过去需要 80 人的事情。一家 40 人的公司将能与 400 人的老牌企业竞争。人均营收将成为判断一家公司是否真正为新时代而建的最清晰信号之一。
在这里很多人会产生抵触情绪。他们听到“智能体做工作”，就以为这意味着人类会消失。
这不是重点。
更好的思考方式是：现代公司一直在把人类智慧浪费在“机器形态”的任务上。我们用人类在工具之间搬运信息。我们用人类记住流程。我们用人类搜索文件夹。我们用人类重写同一封邮件。我们用人类追着审批跑。我们用人类总结通话、填写字段、复制数据、分类请求，以及询问其他人类“东西放在哪里”。
很多工作其实并不是真正的“工作”。它只是戴着假胡子的组织摩擦。
AI 原生公司会把这一层剥离掉。
它们保留真正重要的人类部分，把那些仅仅因为软件太笨、无法理解上下文才存在的部分自动化。这意味着人类角色变得更有杠杆，而不是更不重要。一个优秀的运营者成为十套工作流的监督者。一个优秀的销售人员成为成交者，而对话是由智能体帮助创造出来的。一个优秀的客服主管成为升级逻辑和客户体验质量的设计者。一个优秀的创始人成为公司思考方式的架构师。
关于创始人这一点很重要。
AI 原生创始人不仅仅在打造产品。他们在设计一家能够被智能体理解的公司。
这意味着创始人必须把隐性的东西显性化。我们的退款政策是什么？什么时候可以破例？什么才算一个合格的线索？面对愤怒的客户我们用什么语气？哪些事情永远不该自动化？哪些操作需要审批？什么是一个好答案？什么是一个危险答案？哪个数据源是唯一真相来源？当两个系统互相矛盾时我们怎么办？智能体如何从纠错中学习？
这些不起眼的工作，将把真正的 AI 原生公司与领英上的表演区分开来。
人人都想要魔法。没人想打扫厨房。
但厨房就是公司本身。
胜出的公司会以异乎寻常的认真态度去做那些枯燥的基础工作。它们会清理数据。它们会把工作流记录成文档。它们会创建智能体可读的 SOP。它们会建立权限体系和审计追踪。它们会结构化客户记录，让上下文不再被困在某个人的记忆里。它们会建立评估循环，让智能体随时间变得更好。它们会把每一个重复出现的决策变成一套决策系统。
然后，一旦运营层变得干净，它们就会以荒谬的速度前进。
这就是为什么“AI 原生”其实不是一个技术标签。它是一个组织标签。
一家公司可以使用世界上最好的模型，但在结构上却无法从中受益。如果智能体必须猜测真相在哪里，如果它无法访问正确的系统，如果没人定义过决策规则，如果每套工作流都依赖于藏在某人脑子里的例外情况，那么 AI 就只会是一个玩具。它会起草东西。它会总结东西。它会让人觉得变快了。但它不会改变业务。
真正的转变发生在智能体成为运营肌理的一部分之时。
想象一家真正 AI 原生的家政服务公司。每一个入站请求都被自动分类。每一份报价都由结构化的定价规则生成。每一位技师在到达前都会收到工作摘要。每一位客户都会收到主动更新的信息。每一次索评请求都是个性化的。每一次爽约都会触发一个自动挽回工作流。每一个运营模式都会反哺到派单、定价和人员配置中。
再想象一家保险经纪公司。智能体收集文件、预检投保材料、比较保单、标记缺失信息、起草给客户的解释、准备续保方案，并监控账户变动。人类负责建立信任和处理复杂情况，而底层的机器一整天都在做重复性的智力工作。
再想象一家招聘公司。智能体搜寻候选人、丰富简历信息、与岗位要求进行比对、起草外联信息、总结面试、核实推荐人、更新候选人管道，并在出现特别出色的候选人时提醒人类。招聘人员不再是数据清洁工，而是成为促成关系的人。
这些不是科幻公司。这些是换掉了内脏的普通企业。
这就是人们低估的机会。显而易见的 AI 公司已经人满为患。横向通用副驾驶、写作工具、会议机器人、代码助手、图像生成器、客服套壳。生意不错，但一眼就能看到。不那么显眼的机会是：切入枯燥、盈利、碎片化的行业，围绕智能体重建运营模式。
AI 原生代理公司。AI 原生经纪公司。AI 原生准法律服务。AI 原生会计事务所。AI 原生合规公司。AI 原生医疗行政管理公司。AI 原生房地产运营。AI 原生教育服务。AI 原生物流协调商。AI 原生 BPO——只是看起来不像 BPO。
世界上到处都是这样的行业：客户为结果付费，但供应商的成本结构主要是重复性的知识工作。这恰恰是 AI 原生公司能够楔入的地方。
最好的机会起初并不一定长得像软件公司。有些看起来像服务企业，里面却藏着软件级的利润率。这会让投资人和竞争对手感到困惑，而这很有用。当所有人都在寻找下一个 SaaS 仪表盘时，真正的赢家可能正在悄悄打造 AI 原生服务公司，以大幅更低的劳动强度产出更好的结果。
这是很 Greg 风格的说法，但我认为下一波互联网生意可能不太像“初创公司”，而更像奇怪的小型赚钱机器。
小团队。细分市场。专有工作流。高度自动化。高度信任。清晰的客户痛点。枯燥的品类。漂亮的利润率。
外表毫不起眼。
银行账户里极度性感。
这些公司从第一天起就采用了不同的构建方式，因此传统巨头很难模仿它们。一家老公司无法通过宣布一项 AI 计划就变成 AI 原生公司。这就像想通过买一个新方向盘把游轮变成快艇一样。
难点不在于能否使用模型。这一点人人都有。
难点在于传统公司充满了陈旧的流程债务。它们的数据一团糟。它们的政策相互冲突。它们的团队固守地盘。它们的工作流是围绕人头数量设计的。它们的软件栈是用胶带和季度规划仪式拼接起来的。它们的操作系统默认人类是信息处理者。
新公司则拥有“没有家具需要搬动”的优势。
它可以从零开始。它可以在构建每个流程时提出这样一个问题：“智能体能否先做第一遍处理？”它可以从第一天起就做好文档记录。它可以让每个数据对象都可用。它可以在错误酿成灾难之前设计好人工审核节点。它可以在公司僵化之前建立反馈循环。
这就是为什么“只有 1000 家公司”这个说法很重要。它制造了紧迫感，同时也赋予了人们行动的许可。
这个领域是空的，因为大多数人仍然把 AI 的应用误认为是 AI 的架构。
他们认为这场游戏是提示词工程。不是。
他们认为这场游戏是挑选合适的模型。不是。
他们认为这场游戏是在网站上添加一个聊天机器人。更不是。
这场游戏是重新设计公司，让智能能够在其中流动。
这里有一套切实可行的打法。
第一，选择一个经济价值明显、范围较窄的工作流。不要从“让公司变成 AI 原生”开始，那太抽象了。可以从支持问题解决、外呼销售线索开发、客户入驻、理赔受理、文档审核、续约管理或报告生成入手。选择一个人流量大、规则明确、目前人类在做过多协调工作的工作流。
第二，像分析机器一样梳理工作流。什么触发了它？需要什么数据？会发生哪些决策？哪些决策是可逆的？哪些需要审批？成功的样子是什么？错误发生在哪里？人类知道哪些系统不知道的东西？
第三，把知识结构化。如果智能体需要某项政策，就把政策写出来。如果它需要定价规则，就把规则明确化。如果它需要客户历史，就把客户数据对象清理干净。如果它需要示例，就创建示例。如果它需要语气风格，就定义语气风格。大多数团队在这一步放弃了，因为感觉像是在写文档。这不是文档，这是基础设施。
第四，在边界范围内把智能体放入工作流。让它们起草、分类、推荐、丰富、总结和准备。只在风险可控的环节赋予它们执行操作的权限。在需要判断力的地方要求人工审批。记录一切。审核输出。跟踪质量。持续改进系统。
第五，衡量业务影响。不是在虚假表格里统计“节省的小时数”，而是衡量解决时间、转化率、毛利率、人均收入、错误率、客户满意度、销售速度、入驻时间、续约率。AI 原生公司应该能在这些数字中体现出来。
这是我最感兴趣的部分。几年后，“AI 原生”将不再是一种感觉，它会体现在指标中。
人均收入将呈现不同的面貌。
毛利率将呈现不同的面貌。
执行速度将呈现不同的面貌。
客户体验将呈现不同的面貌。
最优秀的公司会给人一种异乎寻常的敏捷感，仿佛整个业务都是清醒的。客户能更快得到答案。销售团队会以更好的时机跟进。运营问题会更早浮出水面。创始人会更清晰地看到业务全貌。管理者会把更少的时间花在询问进展上，而把更多时间花在改进系统上。
公司的阻力会更小。
这才是真正的优势。
不是把 AI 当作聚会上的炫技，而是把 AI 作为组织的新陈代谢。
所以是的，如今地球上可能只有大约 1000 家真正的 AI 原生公司在创造可观的收入。
这应该会让你立刻想要创建一家这样的公司。
因为当一个市场很喧嚣时，人们会以为它已经成熟。但喧嚣不等于成熟。喧嚣通常正是在真正的建设者弄清楚什么才是重点之前发生的事情。
现在，每个人都在大谈 AI。
但几乎没有公司在结构上做好了准备。
这就是差距。
这就是机会。
下一批伟大的公司将是那些从内到外围绕智能体重建数据、工作流、政策和团队的公司。它们的规模看起来会比实际小。它们的行动速度会快得不可思议。它们会用更少的员工做更有价值的工作。它们会把混乱的服务转化为可扩展的系统。它们会让传统巨头看起来像是在运行 Windows 95，只不过换了个更漂亮的登录界面。
大多数人仍在问：“我该如何在工作中使用 AI？”
更好的问题是：“我该如何打造一家 AI 可以在其中工作的公司？”
这个问题就是那扇门。
而现在，几乎还没有人走进去。
尽管你读到的可能不是这样，但这个领域仍是空白的。也许可以考虑分享给朋友。
我为你加油。
- Greg Isenberg
注：我不常提起这个，因为我们实在太忙了，但我的公司 LCA 在帮助企业实现 AI 原生化方面是世界一流的。因为他们做的工作确实很出色。我们与财富 500 强以及你喜爱的品牌合作，共同打造 AI 原生产品和 AI 原生组织。
如果你的公司想要实现 AI 原生化，可以考虑通过这里联系他们。
如果你正在寻找创业点子，可以考虑去 Ideabrowser.com 获取一些可以用 AI 构建的经过验证的点子。
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*导出时间： 2026/5/12 09:26:53*
