导语：
企业砸重金投入AI，为何财报却迟迟不见改善？
近日，零一万物CEO、创新工场董事长李开复博士在美国《时代》（TIME）发表署名文章，直指当前企业AI转型的一大困境：AI应用看似遍地开花，却未能真正转化为经营成果。
李开复博士指出，问题的关键在于，许多企业将AI部署在容易落地的外围场景，却迟迟没有触及最能创造价值的核心经营与决策。他结合自己使用“老板AI”的亲身实践，分享AI如何帮助CEO提前识别业务风险、及时调整经营策略，以及发现组织中被忽视的高绩效人才。
在李开复看来，企业AI转型必须是“一号位工程”，CEO首先要成为AI原生的领导者。 CEO不能只做AI预算的审批者，更要亲自使用AI，甚至让AI审视自己的管理表现，挑战固有判断，发现认知盲区。
企业AI转型的成败，最终要看AI能否真正进入经营决策，改善业务表现，并体现在财报上。
以下为文章译文：
为什么大多数企业AI转型会失败
文｜李开复博士
李开复博士是零一万物（01.AI）创始人兼CEO、创新工场董事长。他曾担任谷歌大中华区总裁、微软全球副总裁及苹果公司高管。本文内容来自他的英文新书AI Native: The Mandate to Transform Your Company（中文版为《AI未来已来》）。
如果要在下一次财报电话会上，用一句话向股东说明公司的AI转型究竟创造了多大价值，而且要回答他们真正关心的问题，你会怎么说？
我接触过的大多数CEO，都答不上来。
他们通常会列举公司围绕AI转型开展的一长串工作：启动了多少试点、采购了多少软件、培训了多少员工、宣布了多少战略合作。但管理层并没意识到，这些行动并不一定能带来结果。多项调查也显示，绝大多数企业AI转型带来的实际回报仍然不尽如人意。
在我看来，企业AI转型的问题不在于模型、数据或人才，而在于AI被部署在了错误的场景。
今天的AI已经能够围绕整个业务进行推理，我们却让它们去处理工单、总结会议纪要等。这就好比聘请了一位精通企业运营的天才，却只让他做助理。AI最有可能为企业创造巨大价值的地方，不在客服中心，而在CEO的办公室里。
01
AI最大的价值
是让CEO看清企业
平心而论，CEO管理一家大型企业并不容易。企业本质上是一套庞大的信息系统，信息在层层传递的过程中，难免发生损耗和失真。向上汇报时，坏消息和早期预警信号容易被过滤，最终送到CEO面前的，往往是经过修饰、却并不完整的摘要。向下传达时，决策又可能被层层弱化、重新解读或挪作他用，甚至遭遇“口袋否决”：员工在会上点头答应，会后却不了了之。
结果，万人公司的CEO，反而不如十人公司的CEO了解公司的真实情况。
一位优秀的CEO特助可以帮助CEO缓解这些问题。但个人精力终究有限，不可能读完公司的每份文件、参加每场会议、听完每通电话。
如今，AI正在改变这一切。它能够让管理层以前所未有的深度和速度看清组织的真实运作，发现过往难以察觉的问题。这是人类员工、传统商业智能系统难以实现的能力。
企业内部通常有两类信息记录。一类是正式记录，包括：季度报告、项目进展汇报、绩效评估等。这些内容经过层层打磨，看起来条理清晰、逻辑严密。另一类则是第一手材料：会议逐字稿、聊天记录、文档修改记录、销售笔记、代码提交记录等。
过去，管理者主要依赖正式汇报，因为原始记录数量庞大、分散各处，根本无暇阅读。有了 AI 和智能体，公司里最关键的问题、最激烈的争论，每天的业务运作情况和流转节点，都被一一记录下来。过往没有人类员工能高效逐一提取这些深层信息，使其成为沉睡的数据，CEO主要通过汇报了解公司；现在，AI让CEO有机会更直接地看见公司如何运转。
为此，我让团队开发了几个用于公司管理的智能体，帮我跟进会议、追踪承诺、分析数据。随着我越用越顺手，智能体的数量不断增加，能力也持续迭代进步。有些同事把这套系统叫做叫“开复的老板AI”，这个名字就沿用了下来。后来，我们把这些多智能体整合成了正式产品。
在最初使用的几周里，老板AI就发现了几个问题。
一个重点项目，每周都被汇报为“进展正常”，却已经连续五周没有提交代码。
某客户被标记为“续约意向良好”，客户经理却已经两个月没有联系对方。
还有一份合同草案，如果按相关承诺履约，项目根本无法盈利。AI将销售笔记、已签署的条款和产品需求文档核查后发现，有员工曾向客户承诺免费定制服务，却没有将这项承诺写进正式文件。
AI让管理者更清楚地看见企业的真实运营：提前识别交付延期的风险，挽回濒临流失的客户，在承诺兑现前堵住利润流失的漏洞。这些都是AI能够为企业创造的实际经营价值。
让AI处理行政琐事，固然能提高几个点的生产力，但它的价值远不止于此。AI应该进入企业经营核心，帮助企业解决真正影响经营结果的问题。
02
AI转型
首先要改变CEO自己
AI 的第二项价值，是发现企业里可能被忽视的积极信号。其中最重要的，就是发掘优秀人才。
去年，我换了一种方式做绩效评估：不首先看简历、 KPI 汇报，也不依赖部门负责人的举荐。在AI已经能够完成大量编程工作的时代，我们重新明确了衡量工程能力的标准，再依据这些标准，分析员工过去一年的代码仓库记录。最终脱颖而出的员工中，有好几位我甚至从未留意过。
其中一位副总裁，过去一个月与AI协作，完成了近30万行代码。20年前，他就是我认识的最高产的程序员之一，当时每月最多只能写3万行。如今，他的产出竟然是年轻时最佳水平的10倍。
他并不是打字变快了，而是指挥AI完成了工作。AI时代，优秀人才的价值，将越来越取决于他们驾驭AI的能力，而不只是亲手完成多少工作。而当AI能够承担收集进度、传递信息等工作时，那些主要依靠信息传递创造价值的岗位，也将面临重新定义。
我的英文新书名为《AI Native》（中文版为《AI未来已来》），意为“AI原生”。对CEO而言，成为“AI原生”的领导者，不只是审批AI预算，更要从自己开始，让AI审视自己的管理表现、时间安排和认知盲区。
我经常问自己的老板AI：我今天在会议上有哪些地方做得不够好？我的时间安排，和我反复强调的战略重点对得上吗？公司里有哪些重要的事，是下属不敢对我直说的？
员工回答老板的问题时，往往会顾及情面、措辞委婉；AI 却不需要对我客气，能够把真相不经粉饰地直接告诉我。
企业的AI转型，必须从CEO自身的改变开始。
当CEO借助AI看清公司的真实运营情况，提出更精准、更切中要害的问题时，团队也会受到带动，逐步迭代自己的工作方式。
当然，转型难免遇到阻力。一些中层管理者担心AI会引发团队调整，甚至削弱自身的岗位价值，因此对AI产生抵触。在我看来，有些员工抗拒AI，并不是因为他们怀疑AI有没有用，恰恰是因为他们意识到，AI真的太有用了，甚至会重新定义自身的工作价值。
因此，企业AI转型不能再停留于表面。CEO必须亲自推动，让AI进入真正影响企业经营的核心环节，帮助解决那些最棘手、最需要向董事会交代的问题。
到了下一次财报电话会，CEO就能自信地向股东说明：AI改善了哪些经营指标，谁推动了这些变化，以及AI算力在其中发挥了什么作用。
企业AI转型的最终答卷，不在试点报告里，而在经营结果和财报里。
本文摘编翻译自《时代》（TIME），原文如下：
Why Almost Every Corporate AI Transformation Fails
By Kai-Fu Lee
November 3, 2026 | TIME
If you had to describe the result of your company's AI program on your next earnings call--one sentence, of the kind a shareholder would care about--what would that sentence be?
Most chief executives I meet cannot name it. They can produce activity: pilots run, licenses purchased, employees trained, a partnership announced. None of that is a result. Survey after survey finds that most corporate AI programs deliver disappointing measurable returns.
In my view, this is not because of the models, the data, or the talent. It is because of where companies are pointing the AI itself. Today's systems can reason across an entire business, and we have assigned them to handling tickets and summarizing meetings. This is like hiring a business operations genius and putting him to work as an assistant. The place where AI can move the most money is not the call center. It is the corner office.
In fairness to CEOs, the cards are often stacked against them. A company is an information system, and the quality of information degrades at every level it passes through. Going up the chain, uncomfortable findings and early warning signs get discarded; what often arrives is a clean, but incomplete, summary. Going downwards, management directives are often diluted, re-interpreted, re-purposed, or quietly "pocket-vetoed"--nodded through in the meeting and left to die. The result? A company of 10,000 ends up knowing less than a company of 10.
A good chief of staff can help compensate for these challenges. But none can read every document the company produces, sit in every meeting, and hear every customer call.
Now, AI can expose and accelerate radical visibility in organizations at a depth and speed that far surpass any human capacity and any business intelligence system. Every company keeps two sets of records. One is official: quarterly reports, project updates, performance reviews, polished at each level until it is clean and coherent. The other is what actually happened: meeting transcripts, chat threads, edit trails, sales notes, code check-ins. Managers have always worked from the first, because the second was too numerous and scattered to read. No longer. Every company writes down the most critical issues, the hottest debates, and the truth about itself every day. Almost none of them read it. A CEO-level agentic system can wake up that "dormant data."
So at my company I asked my team to build management agents for me: to sit in meetings, track commitments, and read the data underneath what I was being told. The more I used them, the more we added. People called it "Kai-Fu's Boss AI," and the name stuck. We eventually turned it into a product.
What did my boss AI find in its first weeks? A priority project reported on track every week, with no code committed in five. A customer marked highly likely to renew, while the account manager had not called in two months. A draft contract nobody could have made profitable, as reading relevant sales notes, the signed terms, and product requirement documents together showed free customization promised in a room but never written down.
Those are not minor administrative matters. They are a delivery slip caught early, a renewal saved, a margin leak stopped before it had to be honored. That is the connection to the bottom line every AI pilot has missed: not a productivity percentage, but a number that moved because AI caught it in time.
AI's second contribution is to surface hidden good news that might have been under the radar. Chief among these is talent and identifying star performers.
Last year I ran our performance review differently: no resumes, no KPI decks, no department heads recommending their own. We agreed first on what "engineering excellence" means when a machine can now write most of the code, then scored a year of repository history against it. Several of the strongest names were people I could not have picked out of a meeting. One vice president wrote close to 300,000 lines with AI in a month, against his own ceiling of 30,000 20 years ago, when he was the most prolific coder I knew. He is not typing faster; he is directing AI. And the reverse: once the machine does the status-collecting and information-passing, the employees whose only output was status-collecting and information-passing are obsolete.
This is what an AI-native chief executive is: not someone who just signs off on an AI budget, but someone who puts his own meetings, calendar, and blind spots under the machine's scrutiny first. I regularly ask my agent what I got wrong in a meeting, whether my time went where I said my priorities were, and what people are afraid to tell me. Subordinates answer those questions politely. The machine can tell me the blunt truth.
This is how a company can be truly transformed by AI. The chief executive asks sharper questions, and everyone in the room tends to follow. Often, middle managers resist AI because they fear it will put their teams at risk. In fact, I would argue that employees resist AI not because they doubt it works but because they suspect it does.
So: no more cosmetics. We should use AI to address businesses' hardest problems in the building: the ones on the CEO's desk. Then, the words the CEO proudly proclaims on the next earnings call will have three parts: a business number that moved, the person who moved it, and the compute that did the work.
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