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          領英高管:AI時代,組織架構(gòu)圖已消亡?

          Nicholas Gordon
          2026-04-04

          領英高管阿尼什·拉曼認為,隨著企業(yè)紛紛推動員工應用AI,這些定義大多數(shù)職場關系的結(jié)構(gòu)體系,恰恰成了創(chuàng)新的掣肘。

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          領英首席經(jīng)濟機會官阿尼什·拉曼表示,僵化的組織架構(gòu)正在抑制AI創(chuàng)新,企業(yè)必須轉(zhuǎn)向由員工主導的探索模式,方能保持競爭力。圖片來源:Getty Images

          平庸的組織架構(gòu),很少被視為阻礙創(chuàng)新的罪魁禍首。但領英(LinkedIn)高管阿尼什·拉曼認為,隨著企業(yè)紛紛推動員工應用AI,這些定義大多數(shù)職場關系的結(jié)構(gòu)體系,恰恰成了創(chuàng)新的掣肘。

          拉曼表示:“組織架構(gòu)圖誕生于工業(yè)時代,其目的是為快速擴張的組織帶來秩序、可預測性和穩(wěn)定性。企業(yè)需要擺脫這種模式,因為它將阻礙創(chuàng)新。”拉曼目前擔任領英首席經(jīng)濟機會官,并與他人合著了一本探討未來工作的書。

          拉曼認為,高管們與其等待自上而下的轉(zhuǎn)型項目,不如學會放權,讓員工在實踐中自行探索AI的使用方式,即便這些嘗試會打破部門壁壘、跨越崗位職責也無妨。他表示:“真正能夠釋放AI價值的,不只是圍繞AI重構(gòu)流程,而是圍繞人的能力,創(chuàng)造新的工作模式。”

          拉曼曾任CNN戰(zhàn)地記者,并擔任奧巴馬總統(tǒng)的演講撰稿人。他與領英首席執(zhí)行官瑞安·羅斯蘭斯基合著了《開放工作:AI時代如何脫穎而出》(Open to Work: How to Get Ahead in the Age of AI)一書。該書基于領英數(shù)據(jù)和早期AI采用者案例,總結(jié)出一套他稱為“人類如何與AI協(xié)作”的行動指南,旨在打破當前圍繞AI對就業(yè)影響的“宿命論”論調(diào)。

          圖片來源:Courtesy of LinkedIn

          他建議員工將自身工作以及與AI的關系分為三類。第一類涵蓋AI已經(jīng)能夠勝任的任務,例如生成代碼、進行快速分析,或撰寫初稿以激發(fā)創(chuàng)作靈感。第二類是借助AI創(chuàng)造新事物的探索性嘗試。第三類則是結(jié)合第一類節(jié)省下來的時間和第二類積累的經(jīng)驗,讓AI在團隊層面發(fā)揮作用。他問道:“關鍵在于,你正在與同事一起做什么?”

          拉曼表示:“這將是一場由員工主導的轉(zhuǎn)型,因此企業(yè)需要思考如何賦能個體,讓他們在日常工作中平穩(wěn)過渡到這一新時代。在重塑工作模式、追求卓越產(chǎn)出方面,我們擁有的自主權往往超乎想象。”

          AI時代,哪些技能更勝一籌?

          領英正推動招聘與用工模式向“以技能為先”轉(zhuǎn)型。理論上,企業(yè)在招聘時更關注具體技能和能力,以及應聘者具備這些技能的證明,而非只看簡歷上的一串職位頭銜。與此同時,領英也在將AI整合進自身產(chǎn)品,例如推出用于輔助招聘的新AI智能體。

          不過,隨著AI實現(xiàn)知識型工作自動化的能力不斷提升,員工究竟需要具備哪些技能,仍存在不確定性。以編程為例,過去十多年間,高校與政策制定者一直告訴年輕人,“學會編程”是通往高薪職業(yè)的最穩(wěn)妥途徑。但在“氛圍式編程”時代,這一判斷開始動搖。Claude的開發(fā)公司Anthropic認為,在當前及未來可能被AI覆蓋的職業(yè)中,計算機與數(shù)學相關職業(yè)首當其沖。

          拉曼并不認為計算機科學已經(jīng)過時。相反,企業(yè)需要重新審視計算機科學等學位所培養(yǎng)的更廣泛技能。他指出,“計算機科學學位不僅僅教授編程,它還培養(yǎng)復雜思維、組織設計以及系統(tǒng)結(jié)構(gòu)等能力。”

          至少在美國,員工并不確信自己能從中受益。CBS News上周發(fā)布的一項調(diào)查顯示,三分之二的美國人認為AI將減少就業(yè)崗位;比例相近的受訪者也不相信科技公司會以妥當?shù)姆绞绞褂肁I。

          相比之下,AI在亞洲或許更容易普及,當?shù)厝巳簩@一技術的接受度更高。皮尤研究中心(Pew Research Center)去年10月的一項調(diào)查顯示,亞洲受訪者的擔憂程度整體低于西方。例如,在皮尤調(diào)查覆蓋的25個國家中,僅有16%的韓國受訪者表示對AI“擔憂多于興奮”,為最低水平;而美國占比最高,這一比例達到50%。

          最近,中國消費者紛紛在設備上安裝開源AI代理框架OpenClaw,各地政府也競相支持“單人公司”——即借助AI開發(fā)新產(chǎn)品的創(chuàng)業(yè)項目。

          拉曼表示:“在亞洲,無論是企業(yè)還是員工,都渴望學習并應用這些工具。許多亞洲國家本身就具備濃厚的創(chuàng)業(yè)文化。”

          是時候順勢而為了

          不過,拉曼也理解員工對自動化的擔憂。他表示:“過去存在一條清晰的職業(yè)晉升階梯,要登上每一級階梯需要做什么,路徑都極為清晰。”

          但他仍然保持樂觀。在他看來,隨著AI逐步打破企業(yè)傳統(tǒng)的組織方式與激勵機制,員工最終反而會從中受益。他表示:“真正能夠掌控自己職業(yè)路徑的人少之又少。但在AI的推動下,未來的職場人,可能將比以往任何一代都擁有更強的掌控力。”

          但如果有人并不想在工作中成為創(chuàng)新者呢?如果有人只想各司其職,獲得一份穩(wěn)定收入呢?

          拉曼對這些人的回答很直接:“沒有人會來拯救你,除了你自己。”

          無論你喜歡與否,變革即將到來。他表示:“問題只在于,這種變革何時會降臨到你身上,以及沖擊會有多大。”(財富中文網(wǎng))

          譯者:劉進龍

          審校:汪皓

          平庸的組織架構(gòu),很少被視為阻礙創(chuàng)新的罪魁禍首。但領英(LinkedIn)高管阿尼什·拉曼認為,隨著企業(yè)紛紛推動員工應用AI,這些定義大多數(shù)職場關系的結(jié)構(gòu)體系,恰恰成了創(chuàng)新的掣肘。

          拉曼表示:“組織架構(gòu)圖誕生于工業(yè)時代,其目的是為快速擴張的組織帶來秩序、可預測性和穩(wěn)定性。企業(yè)需要擺脫這種模式,因為它將阻礙創(chuàng)新。”拉曼目前擔任領英首席經(jīng)濟機會官,并與他人合著了一本探討未來工作的書。

          拉曼認為,高管們與其等待自上而下的轉(zhuǎn)型項目,不如學會放權,讓員工在實踐中自行探索AI的使用方式,即便這些嘗試會打破部門壁壘、跨越崗位職責也無妨。他表示:“真正能夠釋放AI價值的,不只是圍繞AI重構(gòu)流程,而是圍繞人的能力,創(chuàng)造新的工作模式。”

          拉曼曾任CNN戰(zhàn)地記者,并擔任奧巴馬總統(tǒng)的演講撰稿人。他與領英首席執(zhí)行官瑞安·羅斯蘭斯基合著了《開放工作:AI時代如何脫穎而出》(Open to Work: How to Get Ahead in the Age of AI)一書。該書基于領英數(shù)據(jù)和早期AI采用者案例,總結(jié)出一套他稱為“人類如何與AI協(xié)作”的行動指南,旨在打破當前圍繞AI對就業(yè)影響的“宿命論”論調(diào)。

          他建議員工將自身工作以及與AI的關系分為三類。第一類涵蓋AI已經(jīng)能夠勝任的任務,例如生成代碼、進行快速分析,或撰寫初稿以激發(fā)創(chuàng)作靈感。第二類是借助AI創(chuàng)造新事物的探索性嘗試。第三類則是結(jié)合第一類節(jié)省下來的時間和第二類積累的經(jīng)驗,讓AI在團隊層面發(fā)揮作用。他問道:“關鍵在于,你正在與同事一起做什么?”

          拉曼表示:“這將是一場由員工主導的轉(zhuǎn)型,因此企業(yè)需要思考如何賦能個體,讓他們在日常工作中平穩(wěn)過渡到這一新時代。在重塑工作模式、追求卓越產(chǎn)出方面,我們擁有的自主權往往超乎想象。”

          AI時代,哪些技能更勝一籌?

          領英正推動招聘與用工模式向“以技能為先”轉(zhuǎn)型。理論上,企業(yè)在招聘時更關注具體技能和能力,以及應聘者具備這些技能的證明,而非只看簡歷上的一串職位頭銜。與此同時,領英也在將AI整合進自身產(chǎn)品,例如推出用于輔助招聘的新AI智能體。

          不過,隨著AI實現(xiàn)知識型工作自動化的能力不斷提升,員工究竟需要具備哪些技能,仍存在不確定性。以編程為例,過去十多年間,高校與政策制定者一直告訴年輕人,“學會編程”是通往高薪職業(yè)的最穩(wěn)妥途徑。但在“氛圍式編程”時代,這一判斷開始動搖。Claude的開發(fā)公司Anthropic認為,在當前及未來可能被AI覆蓋的職業(yè)中,計算機與數(shù)學相關職業(yè)首當其沖。

          拉曼并不認為計算機科學已經(jīng)過時。相反,企業(yè)需要重新審視計算機科學等學位所培養(yǎng)的更廣泛技能。他指出,“計算機科學學位不僅僅教授編程,它還培養(yǎng)復雜思維、組織設計以及系統(tǒng)結(jié)構(gòu)等能力。”

          至少在美國,員工并不確信自己能從中受益。CBS News上周發(fā)布的一項調(diào)查顯示,三分之二的美國人認為AI將減少就業(yè)崗位;比例相近的受訪者也不相信科技公司會以妥當?shù)姆绞绞褂肁I。

          相比之下,AI在亞洲或許更容易普及,當?shù)厝巳簩@一技術的接受度更高。皮尤研究中心(Pew Research Center)去年10月的一項調(diào)查顯示,亞洲受訪者的擔憂程度整體低于西方。例如,在皮尤調(diào)查覆蓋的25個國家中,僅有16%的韓國受訪者表示對AI“擔憂多于興奮”,為最低水平;而美國占比最高,這一比例達到50%。

          最近,中國消費者紛紛在設備上安裝開源AI代理框架OpenClaw,各地政府也競相支持“單人公司”——即借助AI開發(fā)新產(chǎn)品的創(chuàng)業(yè)項目。

          拉曼表示:“在亞洲,無論是企業(yè)還是員工,都渴望學習并應用這些工具。許多亞洲國家本身就具備濃厚的創(chuàng)業(yè)文化。”

          是時候順勢而為了

          不過,拉曼也理解員工對自動化的擔憂。他表示:“過去存在一條清晰的職業(yè)晉升階梯,要登上每一級階梯需要做什么,路徑都極為清晰。”

          但他仍然保持樂觀。在他看來,隨著AI逐步打破企業(yè)傳統(tǒng)的組織方式與激勵機制,員工最終反而會從中受益。他表示:“真正能夠掌控自己職業(yè)路徑的人少之又少。但在AI的推動下,未來的職場人,可能將比以往任何一代都擁有更強的掌控力。”

          但如果有人并不想在工作中成為創(chuàng)新者呢?如果有人只想各司其職,獲得一份穩(wěn)定收入呢?

          拉曼對這些人的回答很直接:“沒有人會來拯救你,除了你自己。”

          無論你喜歡與否,變革即將到來。他表示:“問題只在于,這種變革何時會降臨到你身上,以及沖擊會有多大。”(財富中文網(wǎng))

          譯者:劉進龍

          審校:汪皓

          The humble org chart isn’t usually blamed for holding back innovation. But as companies push their employees to adopt AI, LinkedIn executive Aneesh Raman thinks the relationships that structure most workplaces are what’s holding things back.

          “The org chart was built in the industrial age to bring order, predictability, and stability to rapidly growing organizations,” says Raman, LinkedIn’s chief economic opportunity officer and coauthor of a new book on the future of work. “Companies need to let that go, as it’s going to hold back innovation.”

          Instead of waiting for top-down transformation programs, Raman argues, executives will need to get comfortable with workers figuring out AI on their own, even if those experiments cut across departments and job descriptions. “Where you’re going to see the real returns on AI isn’t just a new workflow around AI, but rather new work around human capability,” he says.

          Raman, a former CNN war correspondent and Obama speechwriter, is the coauthor of Open to Work: How to Get Ahead in the Age of AI, alongside LinkedIn CEO Ryan Roslansky. The book draws on LinkedIn data and case studies of early adopters to offer what he calls a “how-to-human-with-AI” playbook that tries to counter the “fatalism” dominating most conversations about AI’s effect on employment.

          He urges workers to think about their work, and how AI relates to it, in three categories. The first bucket covers activities AI already does today, like generating code, running quick analyses, or writing a first draft to inspire someone else’s writing. The second bucket are experiments to create something new with AI. The final bucket involves using the time saved from the first bucket, and the lessons learned from the second bucket, to start using AI as a group. “What are you doing with other people?” he asks.

          “It’s going to be a worker-led transition, and so companies are going to have to figure out how to let individuals start to move into this new era in their day-to-day work,” Raman says. “We have more autonomy than we often think in terms of pushing for what we want to do that might push our work to the next level.”

          What skills will matter in the AI workforce?

          LinkedIn is in the middle of a pivot to what it calls a “skills-first approach” to hiring and employment. In theory, employers are looking for specific skills and capabilities—and proof that potential hires have those skills—instead of just looking at a list of job titles on a résumé. LinkedIn is also integrating AI into its own product, such as a new AI agent to help with hiring.

          But as AI’s capacity to automate knowledge work grows, there’s still confusion over what skills employees will need. Take coding: For more than a decade, universities and policymakers told young people that learning to code was the surest path to a high-paying job. That advice looks less certain in the age of “vibe coding”: Claude developer Anthropic now sees computer and math careers as leading the way in terms of current and possible coverage by AI.

          Raman, for his part, thinks computer science isn’t obsolete. Instead, employers need to look at the broader skills a degree like computer science provides. “A computer science degree doesn’t just teach coding alone. It teaches complex thinking, organizational design, and structures of systems,” he points out.

          Workers, at least in the U.S., aren’t convinced they will come out ahead. A CBS News poll released last week reported that two-thirds of Americans believe AI will decrease the number of jobs; around the same share don’t believe that tech companies will use AI in appropriate ways.

          AI could get more traction in Asia, where populations are more comfortable with the technology. A Pew Research Center survey from October found lower rates of concern among Asia-based respondents than Western ones. For example, just 16% of South Koreans reported being “more concerned than excited” about AI, the lowest share among the 25 countries Pew surveyed; the U.S., in contrast, had the highest share, with 50% reporting concern.

          More recently, Chinese consumers have flocked to install OpenClaw, the open-source AI agent framework, on their devices, and local governments are rushing to support “one-person companies,” or AI startups trying to build new products.

          “There’s a hunger in Asia, not just among companies but also among workers, to learn about these tools and put them to use,” Raman says. “There’s an entrepreneurial culture in a lot of countries in Asia.”

          Time to adapt

          Still, Raman is sympathetic to workers concerned about automation. “There was a career ladder, and there was extreme clarity about what you had to do to get on each rung of that ladder,” he says.

          But he’s optimistic that, ultimately, employees will be better off as AI starts to dismantle the ways companies traditionally organize and reward their talent. “Very few people have ever had real control over their career,” he says. “Because of AI, I think we’re about to have the first generations at work that have more control over their career than any who’ve come before.”

          But what if someone doesn’t want to be an innovator at their job? What if someone wants to maintain their responsibilities and earn a stable wage?

          Raman’s answer to those people is direct: “Nobody is coming to save any individual but themselves.”

          Change is coming, like it or not. “It’s just a question of when this change hits you, and how hard it hits you,” he says.

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