人工智慧時代的競爭

人工智慧時代的競爭

 

封人瘋語： 閉上眼睛，想想明天的世界吧，執汽車行業牛耳者是百度、谷歌還是豐田、沃爾沃？資料和演算法已經成為整個世界的底層，基於物質世界資源稀缺、非此即彼和人類大腦有限理性的傳統邏輯似乎正在被徹底顛覆，資料越多、演算法越強、強者恆強，智者通吃。這是一幅非常可怕的圖景，也是一幅令人激動向往的圖景。斯密用分工描述世界發展，馬克思用階級分析人類未來，在這個嶄新時代到來之際，我們需要新的思維邏輯，資料和演算法是我們理解明天的關鍵。

 

 

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In 2019, just five years after the Ant Financial Services Group was launched, the number of consumers using its services passed the one billion mark. Spun out of Alibaba, Ant Financial uses artificial intelligence and data from Alipay—its core mobile-payments platform—to run an extraordinary variety of businesses, including consumer lending, money market funds, wealth management, health insurance, credit-rating services, and even an online game that encourages people to reduce their carbon footprint. The company serves more than 10 times as many customers as the largest U.S. banks—with less than one-tenth the number of employees. At its last round of funding, in 2018, it had a valuation of $150 billion—almost half that of JPMorgan Chase, the world’s most valuable financial-services company.

2019 年，螞蟻金服成立才5年，客戶數突破10億大關。脫胎於阿里巴巴，螞蟻金服利用人工智慧和支付寶的資料（阿里巴巴的核心移動支付平臺）來運營各種不同的業務，包括消費貸款、貨幣市場基金、財富管理、醫療保險、信用評級服務，甚至還有一款鼓勵人們減少碳排放的線上遊戲。螞蟻金服的客戶數是美國最大銀行的10倍多，而員工卻不到十分之一。在2018年它的最近一輪融資中，估值達到了1500億美元——差不多是世界上最有價值的金融服務公司摩根大通的一半。

 

Unlike traditional banks, investment institutions, and insurance companies, Ant Financial is built on a digital core. There are no workers in its “critical path” of operating activities. AI runs the show. There is no manager approving loans, no employee providing financial advice, no representative authorizing consumer medical expenses. And without the operating constraints that limit traditional firms, Ant Financial can compete in unprecedented ways and achieve unbridled growth and impact across a variety of industries.

與傳統銀行、投資機構和保險公司不同，螞蟻金服建立在數字核心之上。在其經營活動的“關鍵路徑”上沒有工人，AI主宰了一切。沒有經理批准貸款，沒有員工提供財務建議，沒有代表審批消費者的醫療費用。沒有了限制傳統企業的運營約束，螞蟻金服能夠以前所未有的方式展開競爭，實現無約束的增長，並跨越多個行業產生影響。

 

The age of AI is being ushered in by the emergence of this new kind of firm. Ant Financial’s cohort includes giants like Google, Facebook, Alibaba, and Tencent, and many smaller, rapidly growing firms, from Zebra Medical Vision and Wayfair to Indigo Ag and Ocado. Every time we use a service from one of those companies, the same remarkable thing happens: Rather than relying on traditional business processes operated by workers, managers, process engineers, supervisors, or customer service representatives, the value we get is served up by algorithms. Microsoft’s CEO, Satya Nadella, refers to AI as the new “runtime” of the firm. True, managers and engineers design the AI and the software that makes the algorithms work, but after that, the system delivers value on its own, through digital automation or by leveraging an ecosystem of providers outside the firm. AI sets the prices on Amazon, recommends songs on Spotify, matches buyers and sellers on Indigo’s marketplace, and qualifies borrowers for an Ant Financial loan.

這種新型公司的出現引領著人工智慧時代的到來。類似螞蟻金服這樣的公司有很多，巨頭如谷歌、Facebook、阿里巴巴和騰訊，以及許多規模較小、發展迅速的公司，從斑馬醫療(Zebra Medical Vision)、Wayfair到Indigo Ag和Ocado。每次當我們使用這些公司提供的服務時，都會見到同樣的、令人非常難忘的一幕：與依賴工人、經理、工程師、主管或客戶服務代表運營傳統業務流程不同，我們獲得的價值是由演算法提供的。微軟執行長薩蒂亞·納德拉把人工智慧稱作是公司新的“執行時”（runtime）。誠然，是管理人員和工程師設計了人工智慧，開發了讓演算法工作的軟體，但在此之後，卻是智慧系統透過自動化的程式或利用外部供應商生態，自行實現價值。AI在亞馬遜上定價，在Spotify上推薦歌曲，在Indigo上撮合買家和賣家，為螞蟻金服篩選合格貸款人。

 

The elimination of traditional constraints transforms the rules of competition. As digital networks and algorithms are woven into the fabric of firms, industries begin to function differently and the lines between them blur. The changes extend well beyond born-digital firms, as more-traditional organizations, confronted by new rivals, move toward AI-based models too. Walmart, Fidelity, Honeywell, and Comcast are now tapping extensively into data, algorithms, and digital networks to compete convincingly in this new era. Whether you’re leading a digital start-up or working to revamp a traditional enterprise, it’s essential to understand the revolutionary impact AI has on operations, strategy, and competition.

消除傳統約束無疑改變了競爭規則。隨著數字網路和演算法被匯入企業的體系結構之中，行業開始以不同的方式運作，行業之間的界限開始變得模糊。這些變化不只是由這些新型的數字公司帶來的，面對新的競爭對手，傳統組織也開始轉向基於人工智慧的運營模式。沃爾瑪、富達(Fidelity)、霍尼韋爾(Honeywell)和康卡斯特(Comcast)正在廣泛利用資料、演算法和數字網路，以贏得新時代的競爭。顯然，無論你是領導一家數字型初創企業，還是致力於改造一家傳統企業，理解人工智慧對企業運營、戰略和競爭的革命性影響都是至關重要的。

 

 

The AI Factory

人工智慧工廠

 

 

At the core of the new firm is a decision factory—what we call the “AI factory.” Its software runs the millions of daily ad auctions at Google and Baidu. Its algorithms decide which cars offer rides on Didi, Grab, Lyft, and Uber. It sets the prices of headphones and polo shirts on Amazon and runs the robots that clean floors in some Walmart locations. It enables customer service bots at Fidelity and interprets X-rays at Zebra Medical. In each case the AI factory treats decision-making as a science. Analytics systematically convert internal and external data into predictions, insights, and choices, which in turn guide and automate operational workflows.

螞蟻金服這樣的新型公司的核心是一個決策工廠—— 我們稱之為“人工智慧工廠”。在谷歌和百度上，軟體每天運營著數百萬次廣告拍賣。在滴滴、Grab、Lyft和Uber上，演算法決定著哪些車可以提供服務。在亞馬遜上，智慧演算法為耳機和polo衫定價。在沃爾瑪的一些門店裡，機器人在清潔地板。富達使用機器人提供客戶服務，斑馬醫療利用機器人解釋x射線的影像。在每一個案例中，人工智慧工廠都將決策視為一門科學，資料分析軟體系統地將內外部資料轉化為預測、洞察和選擇，從而指導和自動化工作流程。

 

Oddly enough, the AI that can drive the explosive growth of a digital firm often isn’t even all that sophisticated. To bring about dramatic change, AI doesn’t need to be the stuff of science fiction—indistinguishable from human behavior or simulating human reasoning, a capability sometimes referred to as “strong AI.” You need only a computer system to be able to perform tasks traditionally handled by people—what is often referred to as “weak AI.”

奇怪的是，推動數字公司爆炸式增長的人工智慧往往並不複雜。儘管帶來了戲劇性的變化，但需要的人工智慧並不是科幻小說裡的那些東西——與人類行為或模擬人類推理沒有什麼區別的能力，這種能力有時被稱為“強人工智慧”。事實上，你只需要一個計算機系統就能完成傳統上由人來完成的任務——這通常被稱為“弱人工智慧”。

 

With weak AI, the AI factory can already take on a range of critical decisions. In some cases it might manage information businesses (such as Google and Facebook). In other cases it will guide how the company builds, delivers, or operates actual physical products (like Amazon’s warehouse robots or Waymo, Google’s self-driving car service). But in all cases digital decision factories handle some of the most critical processes and operating decisions. Software makes up the core of the firm, while humans are moved to the edge.

擁有弱人工智慧，AI工廠能夠做出一系列關鍵決策。在某些情況下，它管理資訊類業務（如谷歌和Facebook）。在其他情況下，它指導公司如何構建、交付或運營實體產品（如亞馬遜的倉儲機器人或谷歌的自動駕駛汽車）。在所有的情況下，數字決策工廠處理最關鍵的流程和運營決策，軟體構成了公司的核心，而人則被移到了邊緣。

 

Four components are essential to every factory. The first is the data pipeline, the semiautomated process that gathers, cleans, integrates, and safeguards data in a systematic, sustainable, and scalable way. The second is algorithms, which generate predictions about future states or actions of the business. The third is an experimentation platform, on which hypotheses regarding new algorithms are tested to ensure that their suggestions are having the intended effect. The fourth is infrastructure, the systems that embed this process in software and connect it to internal and external users.

對於人工智慧工廠，有四個要素必不可少。一是資料管道，這是一個半自動化的過程，用一種系統的、可持續和可擴充套件的方式收集、清理、整合和保護資料。二是演算法，生成關於業務未來狀態或行動的預測值。三是實驗平臺，在這個平臺上測試新演算法的假設，確保具有預期的效果。四是基礎設施，將人工智慧嵌入軟體平臺，並將其連線到內外部使用者的系統上。

 

 

The AI that drives explosive growth often isn't  even all that sophisticated

推動爆炸性增長的人工智慧通常不是很複雜

 

 

Take a search engine like Google or Bing. As soon as someone starts to type a few letters into the search box, algorithms dynamically predict the full search term on the basis of terms that many users have typed in before and this particular user’s past actions. These predictions are captured in a drop-down menu (the “autosuggest box”) that helps the user zero in quickly on a relevant search. Every keystroke and every click are captured as data points, and every data point improves the predictions for future searches. AI also generates the organic search results, which are drawn from a previously assembled index of the web and optimized according to the clicks generated on the results of previous searches. The entry of the term also sets off an automated auction for the ads most relevant to the user’s search, the results of which are shaped by additional experimentation and learning loops. Any click on or away from the search query or search results page provides useful data. The more searches, the better the predictions, and the better the predictions, the more the search engine is used.

以谷歌或Bing這樣的搜尋引擎為例。一旦有人開始在搜尋框中輸入幾個字母，演算法就會根據許多使用者之前輸入的詞彙和該使用者過去的行為動態預測整個搜尋詞。這些預測值會在下拉選單（“自動建議框”）中顯示出來，幫助使用者快速鎖定相關搜尋。每一個擊鍵和每一次點選都被捕獲為資料點，每一個資料點都改進了對未來搜尋的預測。人工智慧還能生成有機的搜尋結果，這些搜尋結果來自於以前收集的web索引，並根據以前搜尋結果產生的點選進行最佳化。這個詞的加入也引發了與使用者搜尋最相關的廣告的自動拍賣，這個結果是由其它的實驗和學習迴圈形成的。任何點選或離開搜尋查詢或搜尋結果頁面都會提供有用的資料。搜尋越多，預測效果越好，預測效果越好，搜尋引擎的使用率就越高。

 

Removing Limits to Scale, Scope, and Learning

消除規模、範圍和學習等因素對企業增長影響的限制  

 

 

The concept of scale has been central in business since at least the Industrial Revolution. The great Alfred Chandler described how modern industrial firms could reach unprecedented levels of production at much lower unit cost, giving large firms an important edge over smaller rivals. He also highlighted the benefits companies could reap from the ability to achieve greater production scope, or variety. The push for improvement and innovation added a third requirement for firms: learning. Scale, scope, and learning have come to be considered the essential drivers of a firm’s operating performance. And for a long time they’ve been enabled by carefully defined business processes that rely on labor and management to deliver products and services to customers—and that are reinforced by traditional IT systems.

工業革命以來，規模概念一直是商業的核心。偉大的阿爾弗雷德•錢德勒曾經描述過，現代工業企業是怎樣以低得多的單位成本達到前所未有的生產水平，從而使大型企業相對於規模較小的競爭對手擁有重要優勢。他還強調了企業能夠從擴大生產範圍或增加品種中獲得的好處。隨著創新重要性的與日俱增，對企業又增加了學習能力的要求。規模、範圍和學習能力被認為是一個公司經營業績的主要驅動力。很長一段時間以來，它們都是透過精心定義的業務流程來實現的，這些業務流程依賴於勞動力和管理人員向客戶交付產品和服務，並由傳統的IT系統加以強化。

 

After hundreds of years of incremental improvements to the industrial model, the digital firm is now radically changing the scale, scope, and learning paradigm. AI-driven processes can be scaled up much more rapidly than traditional processes can, allow for much greater scope because they can easily be connected with other digitized businesses, and create incredibly powerful opportunities for learning and improvement—like the ability to produce ever more accurate and sophisticated customer-behavior models and then tailor services accordingly.

雖然經歷了數百年，企業的競爭模式只是在緩慢改變。現在數字公司徹底改變了規模、範圍和學習的競爭正規化。AI驅動的業務流程相比傳統業務流程，以快得多的速度擴大服務能力，擴充服務範圍，他們可以很容易的與其他數字化業務實現連線，創造令人難以置信的強大的學習和改進機會，產生更精確和複雜的客戶行為模型，定製相應的服務。

 

In traditional operating models, scale inevitably reaches a point at which it delivers diminishing returns. But we don’t necessarily see this with AI-driven models, in which the return on scale can continue to climb to previously unheard-of levels. Now imagine what happens when an AI-driven firm competes with a traditional firm by serving the same customers with a similar (or better) value proposition and a much more scalable operating model.

在傳統運營模式中，規模會達到一個均衡點，之後，回報開始遞減。但在人工智慧驅動的運營模式下，這種情況可能不會出現，規模回報可能會持續攀升至前所未有的水平。現在，想象一下，當一個人工智慧驅動的公司與一個傳統公司競爭，人工智慧驅動的公司用極具可擴充套件性的運營模式為相同的客戶提供類似（或更好）的價值服務，結果會怎樣呢？

 

How AI-Driven Companies Can Outstrip Traditional Firms

人工智慧驅動的公司如何超越傳統公司

 

 

The value that scale delivers eventually tapers off in traditional operating models, but in digital operating models, it can climb much higher.

在傳統的運營模式中，這種規模增長帶來的價值最終會逐漸減少，但在數字運營模式中，它可以爬升得更高。

 

We call this kind of confrontation a “collision.” As both learning and network effects amplify volume’s impact on value creation, firms built on a digital core can overwhelm traditional organizations. Consider the outcome when Amazon collides with traditional retailers, Ant Financial with traditional banks, and Didi and Uber with traditional taxi services. As Clayton Christensen, Michael Raynor, and Rory McDonald argued in “What Is Disruptive Innovation?” (HBR, December 2015), such competitive upsets don’t fit the disruption model. Collisions are not caused by a particular innovation in a technology or a business model. They’re the result of the emergence of a completely different kind of firm. And they can fundamentally alter industries and reshape the nature of competitive advantage.

我們稱人工智慧驅動的公司與傳統公司之間的對抗為“衝突”。由於學習和網路效應放大了數量對價值創造的影響，建立在數字核心之上的公司可以超越傳統組織。考慮一下亞馬遜與傳統零售商、螞蟻金服與傳統銀行、滴滴和優步與傳統計程車服務發生衝突的後果。正如克萊頓·克里斯坦森、邁克爾·雷諾和羅裡·麥克唐納在《什麼是顛覆性創新》（哈佛商業評論，2015年12月）中指出的，這樣的競爭性顛覆不符合顛覆創新模式。衝突不是由技術或商業模式中的特定創新引起的。它們是一種完全不同的公司出現的結果。它們可以從根本上改變行業，重塑競爭優勢的本質。

 

 

 

Note that it can take quite a while for AI-driven operating models to generate economic value anywhere near the value that traditional operating models generate at scale. Network effects produce little value before they reach critical mass, and most newly applied algorithms suffer from a “cold start” before acquiring adequate data. Ant Financial grew rapidly, but its core payment service, Alipay, which had been launched in 2004 by Alibaba, took years to reach its current volume. This explains why executives ensconced in the traditional model have a difficult time at first believing that the digital model will ever catch up. But once the digital operating model really gets going, it can deliver far superior value and quickly overtake traditional firms.

請注意（如上圖），人工智慧驅動的運營模式產生的經濟價值，可能需要相當長的一段時間才能接近傳統運營模式在規模上產生的價值。網路效應在達到臨界規模之前產生的價值很小，而大多數新應用的演算法在獲得足夠的資料之前都遭遇了“冷啟動”。螞蟻金服發展迅速，但其核心支付服務——阿里巴巴於2004年推出的支付寶——花了多年時間才達到目前的規模。這就解釋了為什麼那些安坐在傳統模式下的高管們一開始很難相信數字模式會迎頭趕上。但一旦數字運營模式真正開始運作，它就能帶來遠超傳統企業的價值，並迅速超越傳統企業。

 

Collisions between AI-driven and traditional firms are happening across industries: software, financial services, retail, telecommunications, media, health care, automobiles, and even agribusiness. It’s hard to think of a business that isn’t facing the pressing need to digitize its operating model and respond to the new threats.

在軟體、金融服務、零售、電信、媒體、醫療、汽車甚至農業綜合企業等行業，人工智慧驅動的企業與傳統企業之間的衝突正在發生。很難想象一個企業不面臨著將其運營模式數字化和應對新威脅的迫切需要。

 

Rebuilding Traditional Enterprises

重建傳統企業

 

 

For leaders of traditional firms, competing with digital rivals involves more than deploying enterprise software or even building data pipelines, understanding algorithms, and experimenting. It requires rearchitecting the firm’s organization and operating model. For a very, very long time, companies have optimized their scale, scope, and learning through greater focus and specialization, which led to the siloed structures that the vast majority of enterprises today have. Generations of information technology didn’t change this pattern. For decades, IT was used to enhance the performance of specific functions and organizational units. Traditional enterprise systems often even reinforced silos and the divisions across functions and products.

對於傳統企業的領導者來說，同數字企業的競爭不只是部署企業軟體，或者是建立資料管道、理解演算法和進行實驗。它需要重新架構公司的組織和運營模式。很長一段時間以來，公司透過歸核化和專業化在不斷最佳化它們的規模、範圍和學習模式，形成了今天絕大多數企業所擁有的煙囪結構。雖然資訊科技經歷了幾代的發展，但並沒有改變這種模式。幾十年來，資訊科技只是被用來提高某些特定功能和組織單元的績效。這反而強化了傳統企業的煙囪結構，促進了企業功能和產品的分散化。

 

Silos, however, are the enemy of AI-powered growth. Indeed, businesses like Google Ads and Ant Financial’s MyBank deliberately forgo them and are designed to leverage an integrated core of data and a unified, consistent code base. When each silo in a firm has its own data and code, internal development is fragmented, and it’s nearly impossible to build connections across the silos or with external business networks or ecosystems. It’s also nearly impossible to develop a 360-degree understanding of the customer that both serves and draws from every department and function. So when firms set up a new digital core, they should avoid creating deep organizational divisions within it.

然而，煙囪結構是人工智慧驅動的增長模式的大敵。事實上，像谷歌Ads和螞蟻金服的MyBank這樣的企業有意的放棄了這些服務，它們的目的是利用一個整合的資料核心和統一一致的程式碼庫。當公司中的每個煙囪都有自己的資料和程式碼時，內部的資源、能力和發展就會分散化，幾乎不可能跨煙囪或者與外部業務網路或生態系統建立連線。想對客戶進行全方位的瞭解，既要服務客戶，又要從各個部門和功能單元獲取資訊，也幾乎是不可能的。因此，當公司建立一個新的數字核心時，應該避免在其內部產生深層次的組織分歧。

 

While the transition to an AI-driven model is challenging, many traditional firms—some of which we’ve worked with—have begun to make the shift. In fact, in a recent study we looked at more than 350 traditional enterprises in both service and manufacturing sectors and found that the majority had started building a greater focus on data and analytics into their organizations. Many—including Nordstrom, Vodafone, Comcast, and Visa—had already made important inroads, digitizing and redesigning key components of their operating models and developing sophisticated data platforms and AI capabilities. You don’t have to be a software start-up to digitize critical elements of your business—but you do have to confront silos and fragmented legacy systems, add capabilities, and retool your culture.

雖然向人工智慧驅動模式轉變充滿挑戰，但許多傳統公司——其中一些與我們有過合作——已經開始做出轉變。事實上，在最近的一項研究中，我們研究了350多家服務和製造行業的傳統企業，發現大多數企業都開始更加註重資料和分析。包括諾德斯特龍、沃達豐、康卡斯特和visa在內的許多公司已經取得了重要進展，他們將運營模式的關鍵元件進行了數字化和重新設計，並開發了複雜的資料平臺和人工智慧。你不必成為一個軟體初創公司來數字化你的關鍵業務元素，但你必須面對煙囪式的、分散的傳統資訊系統，給它賦能，並重構公司文化。

 

Fidelity Investments is using AI to enable processes in important areas, including customer service, customer insights, and investment recommendations. Its AI initiatives build on a multiyear effort to integrate data assets into one digital core and redesign the organization around it. The work is by no means finished, but the impact of AI is already evident in many high-value use cases across the company. To take on Amazon, Walmart is rebuilding its operating model around AI and replacing traditional siloed enterprise software systems with an integrated, cloud-based architecture. That will allow Walmart to use its unique data assets in a variety of powerful new applications and automate or enhance a growing number of operating tasks with AI and analytics. At Microsoft, Nadella is betting the company’s future on a wholesale transformation of its operating model.

富達投資正在利用人工智慧為重要領域的業務流程賦能，包括客戶服務、客戶洞察和投資建議。它的人工智慧計劃建立在多年的努力之上，將資料資產整合到一個數字核中，並圍繞它重新設計組織。雖然這項工作並沒有結束，但是人工智慧的影響已經在公司的許多高價值應用案例中得到了明顯的體現。為了與亞馬遜競爭，沃爾瑪正圍繞人工智慧重建其運營模式，以整合的、基於雲的架構取代傳統的、煙囪式的企業軟體系統。這將使沃爾瑪能夠在各種強大的新應用程式中使用其獨特的資料資產，透過人工智慧和資料分析讓越來越多的任務自動化、並提升效率。在微軟，納德拉正將公司的未來押注於運營模式的整體轉型。

 

 

Rethinking Strategy and Capabilities

重新思考戰略和能力  

 

As AI-powered firms collide with traditional businesses, competitive advantage is increasingly defined by the ability to shape and control digital networks. (See “Why Some Platforms Thrive and Others Don’t,” HBR, January–February 2019.) Organizations that excel at connecting businesses, aggregating the data that flows among them, and extracting its value through analytics and AI will have the upper hand. Traditional network effects and AI-driven learning curves will reinforce each other, multiplying each other’s impact. You can see this dynamic in companies such as Google, Facebook, Tencent, and Alibaba, which have become powerful “hub” firms by accumulating data through their many network connections and building the algorithms necessary to heighten competitive advantages across disparate industries.

隨著以人工智慧為驅動的企業與傳統企業發生碰撞，塑造和控制數字網路的能力越來越能定義競爭優勢。（參見2019年1 - 2月的《哈佛商業評論》，“為什麼有些平臺蓬勃發展，而有些卻不能”）。擅長連線企業、聚合資料、並透過分析和人工智慧提取其價值的組織將佔據上風。傳統的網路效應和人工智慧驅動的學習曲線會相互強化，相互促進。你可以在谷歌、Facebook、騰訊和阿里巴巴等公司看到這種動態。這些公司已經成為強大的“中心”企業，它們透過許多網路連線來積累資料，構建必要的演算法，以增強不同行業的競爭優勢。

 

Meanwhile, conventional approaches to strategy that focus on traditional industry analysis are becoming increasingly ineffective. Take automotive companies. They’re facing a variety of new digital threats, from Uber to Waymo, each coming from outside traditional industry boundaries. But if auto executives think of cars beyond their traditional industry context, as a highly connected, AI-enabled service, they can not only defend themselves but also unleash new value—through local commerce opportunities, ads, news and entertainment feeds, location-based services, and so on.

與此同時，聚焦傳統行業分析的傳統戰略分析方法正變得越來越無效。以汽車企業為例，他們正面臨著各種新的數字威脅，從優步到Waymo，每一種威脅都來自傳統行業的邊界之外。但是，如果汽車行業的高管們能超越傳統思維，把汽車看作是高度互聯的、由人工智慧驅動的服務，那麼他們不僅可以保護好自己，還可以透過車內的商業機會、廣告、新聞和娛樂資訊、基於位置的服務等來釋放新的價值。

 

The advice to executives was once to stick with businesses they knew, in industries they understood. But synergies in algorithms and data flows do not respect industry boundaries. And organizations that can’t leverage customers and data across those boundaries are likely to be at a big disadvantage. Instead of focusing on industry analysis and on the management of companies’ internal resources, strategy needs to focus on the connections firms create across industries and the flow of data through the networks the firms use.

曾經給高管們的建議是，在熟悉的行業裡，堅持做自己熟悉的生意。但演算法和資料流的協同效應並不尊重行業邊界。而那些不能跨越這些邊界利用客戶和資料的組織可能會處於很大的劣勢。戰略需要聚焦的不是行業分析和公司內部資源的管理，而是公司跨行業建立的聯絡和公司正在使用的網路中的資料流。

 

All this has major implications for organizations and their employees. Machine learning will transform the nature of almost every job, regardless of occupation, income level, or specialization. Undoubtedly, AI-based operating models can exact a real human toll. Several studies suggest that perhaps half of current work activities may be replaced by AI-enabled systems. We shouldn’t be too surprised by that. After all, operating models have long been designed to make many tasks predictable and repeatable. Processes for scanning products at checkout, making lattes, and removing hernias, for instance, benefit from standardization and don’t require too much human creativity. While AI improvements will enrich many jobs and generate a variety of interesting opportunities, it seems inevitable that they will also cause widespread dislocation in many occupations.

所有這些變化對組織及其僱員都有重大影響。機器學習將改變幾乎所有工作的性質，無論職業、收入水平或專業領域。毫無疑問，基於人工智慧的運營模式將會對就業造成實實在在的影響。幾項研究表明，目前的工作可能有一半將被人工智慧系統取代。對此我們不應該感到太驚訝。畢竟，長期以來，運營模式已經被設計成讓許多工作任務是可預測和可重複的。例如，檢查時掃描產品、製作拿鐵和去除疝氣的流程都可以標準化，不需要太多的人類創造力。雖然人工智慧將使很多工作變得更加豐富，併產生各種有趣的機會，但似乎不可避免的是，它們也將在許多職業中造成廣泛的混亂與調整。

 

The dislocations will include not only job replacement but also the erosion of traditional capabilities. In almost every setting, AI-powered firms are taking on highly specialized organizations. In an AI-driven world, the requirements for competition have less to do with specialization and more to do with a universal set of capabilities in data sourcing, processing, analytics, and algorithm development. These new universal capabilities are reshaping strategy, business design, and even leadership. Strategies in very diverse digital and networked businesses now look similar, as do the drivers of operating performance. Industry expertise has become less critical. When Uber looked for a new CEO, the board hired someone who had previously run a digital firm—Expedia—not a limousine services company.

這種混亂與調整不僅包括工作的替代，還包括傳統能力的削弱。在幾乎每一種情況下，人工智慧公司都在挑戰高度專業化的組織。在人工智慧驅動的世界中，競爭能力與專門化關係不大，而更多地與資料來源、處理、分析和演算法開發方面的通用功能有關。這些新的通用能力正在重塑戰略、業務設計，甚至領導力。如今，在非常多樣化的數字和網路化公司中，戰略看起來都很相似，經營業績的驅動因素也是如此。行業專長變得不那麼重要了。當優步尋找新的執行長時，董事會聘請的是一位曾運營過數字公司的人，運營的是艾派迪公司，而不是一家豪華轎車服務公司。

 

We’re moving from an era of core competencies that differ from industry to industry to an age shaped by data and analytics and powered by algorithms—all hosted in the cloud for anyone to use. This is why Alibaba and Amazon are able to compete in industries as disparate as retail and financial services, and health care and credit scoring. These sectors now have many similar technological foundations and employ common methods and tools. Strategies are shifting away from traditional differentiation based on cost, quality, and brand equity and specialized, vertical expertise and toward advantages like business network position, the accumulation of unique data, and the deployment of sophisticated analytics.

我們正在從一個不同行業擁有不同核心競爭力的時代，進入一個由資料和分析塑造、由演算法驅動的核心競爭力時代——所有這些都託管在雲端，任何人都可以使用。這就是為什麼阿里巴巴和亞馬遜能夠在零售和金融服務、醫療保健和信用評分等完全不同的行業展開競爭。這些部門現在有許多類似的技術基礎，並使用共同的方法和工具。戰略正從傳統的構建基於成本、質量、品牌價值、專門化和垂直專長等方面的差異，轉向打造基於商業網路位置、獨特資料積累和複雜分析部署等方面的優勢。

 

The Leadership Challenge

對領導力挑戰

 

 

Though it can unleash enormous growth, the removal of operating constraints isn’t always a good thing. Frictionless systems are prone to instability and hard to stop once they’re in motion. Think of a car without brakes or a skier who can’t slow down. A digital signal—a viral meme, for instance—can spread rapidly through networks and can be just about impossible to halt, even for the organization that launched it in the first place or an entity that controls the key hubs in a network. Without friction, a video inciting violence or a phony or manipulative headline can quickly spread to billions of people on a variety of networks, even morphing to optimize click-throughs and downloads. If you have a message to send, AI offers a fantastic way to reach vast numbers of people and personalize that message for them. But the marketer’s paradise can be a citizen’s nightmare.

儘管它可以釋放出巨大的增長，但消除運營約束並不總是一件好事。無摩擦系統容易不穩定，一旦執行就很難停止。想想一輛沒有剎車的汽車或者一個不能減速的滑雪者。數字訊號——例如，病毒式的模因（meme）—可以透過網路迅速傳播，而且幾乎不可能被阻止，即使是最初發布它的組織或控制網路關鍵樞紐的實體也不例外。在沒有摩擦的情況下，一個煽動暴力的影片，或者一個虛假或被操縱的標題，都可以透過各種各樣的網路迅速傳播到數十億人的手中，甚至可以透過變形來最佳化點選率和下載。如果你有資訊要傳送，人工智慧提供了一種奇妙的方式來接觸大量的人，併為他們個性化資訊。但市場營銷者的天堂可能是公民的噩夢。

 

Digital operating models can aggregate harm along with value. Even when the intent is positive, the potential downside can be significant. A mistake can expose a large digital network to a destructive cyberattack. Algorithms, if left unchecked, can exacerbate bias and misinformation on a massive scale. Risks can be greatly magnified. Consider the way that digital banks are aggregating consumer savings in an unprecedented fashion. Ant Financial, which now operates one of the largest money market funds in the world, is entrusted with the savings of hundreds of millions of Chinese consumers. The risks that presents are significant, especially for a relatively unproven institution.

數字運營模式在創造價值的同時也可能聚集與放大傷害。即使意圖是積極的，潛在的負面影響也是巨大的。一個錯誤就能使一個龐大的數字網路遭受毀滅性的網路攻擊。如果不加以檢查，演算法可能會在大規模範圍內加劇偏見和錯誤資訊。風險可能被大大放大。想想數字銀行正以一種前所未有的方式聚合消費者儲蓄。螞蟻金服目前管理著全球最大的貨幣市場基金之一，它受託管理數億中國消費者的儲蓄。由此帶來的風險是巨大的，尤其是對於一個相對未經驗證的機構而言。

 

Digital scale, scope, and learning create a slew of new challenges—not just privacy and cybersecurity problems, but social turbulence resulting from market concentration, dislocations, and increased inequality. The institutions designed to keep an eye on business—regulatory bodies, for example—are struggling to keep up with all the rapid change.

數字的規模、範圍和學習創造了一系列新的挑戰——不僅僅是隱私和網路安全問題，還有由市場集中、就業調整和不平等加劇造成的社會動盪。例如，那些監督企業的機構，也就是監管機構，正在努力跟上所有這些快速的改變。

 

In an AI-driven world, once an offering’s fit with a market is ensured, user numbers, engagement, and revenues can skyrocket. Yet it’s increasingly obvious that unconstrained growth is dangerous. The potential for businesses that embrace digital operating models is huge, but the capacity to inflict widespread harm needs to be explicitly considered. Navigating these opportunities and threats will be a real test of leadership for both businesses and public institutions.

在人工智慧驅動的世界裡，一旦產品與市場相匹配，使用者數、參與度和收入就會飆升。然而，越來越明顯的是，無約束的增長是危險的。擁抱數字運營模式的企業潛力巨大，對它們造成廣泛傷害的能力也需要認真對待。平衡好這些機遇和威脅將是對企業和公共機構領導力的真正考驗。

 

作者介紹  

 

Marco Iansiti is the David Sarnoff Professor of Business Administration at Harvard Business School, where he heads the Technology and Operations Management Unit and the Digital Initiative. He has advised many companies in the technology sector, including Microsoft, Facebook, and Amazon. He is a coauthor (with Karim Lakhani) of the book Competing in the Age of AI (Harvard Business Review Press, 2020).

Marco Iansiti 哈佛商學院企業管理教授，負責技術、運營管理部門和數字創新，為許多科技公司提供諮詢服務，包括微軟、Facebook和亞馬遜等，與卡里姆·拉克哈尼(Karim Lakhani)合著了《人工智慧時代的競爭》。

Karim R. Lakhani is the Charles Edward Wilson Professor of Business Administration and the Dorothy and Michael Hintze Fellow at Harvard Business School and the founder and codirector of the Laboratory for Innovation Science at Harvard. He is a coauthor (with Marco Iansiti) of the book Competing in the Age of AI (Harvard Business Review Press, 2020).

卡里姆·r·拉克哈尼(Karim R. Lakhani) 哈佛商學院工商管理教授，哈佛大學創新科學實驗室的創始人和聯合主任。他是《人工智慧時代的競爭》一書的合著者之一。

 

 

https://mp.weixin.qq.com/s/owrtYgWgRuiqAV6VUTk8Xw