人工智能時(shí)代的競(jìng)爭(zhēng)

創(chuàng)新互聯(lián)公司是一家專業(yè)提供沐川企業(yè)網(wǎng)站建設(shè),專注與成都做網(wǎng)站、網(wǎng)站設(shè)計(jì)、H5場(chǎng)景定制、小程序制作等業(yè)務(wù)。10年已為沐川眾多企業(yè)、政府機(jī)構(gòu)等服務(wù)。創(chuàng)新互聯(lián)專業(yè)網(wǎng)站設(shè)計(jì)公司優(yōu)惠進(jìn)行中。

封人瘋語(yǔ)： 閉上眼睛，想想明天的世界吧，執(zhí)汽車行業(yè)牛耳者是百度、谷歌還是豐田、沃爾沃？數(shù)據(jù)和算法已經(jīng)成為整個(gè)世界的底層，基于物質(zhì)世界資源稀缺、非此即彼和人類大腦有限理性的傳統(tǒng)邏輯似乎正在被徹底顛覆，數(shù)據(jù)越多、算法越強(qiáng)、強(qiáng)者恒強(qiáng)，智者通吃。這是一幅非常可怕的圖景，也是一幅令人激動(dòng)向往的圖景。斯密用分工描述世界發(fā)展，馬克思用階級(jí)分析人類未來(lái)，在這個(gè)嶄新時(shí)代到來(lái)之際，我們需要新的思維邏輯，數(shù)據(jù)和算法是我們理解明天的關(guān)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(shù)突破10億大關(guān)。脫胎于阿里巴巴，螞蟻金服利用人工智能和支付寶的數(shù)據(jù)（阿里巴巴的核心移動(dòng)支付平臺(tái)）來(lái)運(yùn)營(yíng)各種不同的業(yè)務(wù)，包括消費(fèi)貸款、貨幣市場(chǎng)基金、財(cái)富管理、醫(yī)療保險(xiǎn)、信用評(píng)級(jí)服務(wù)，甚至還有一款鼓勵(lì)人們減少碳排放的在線游戲。螞蟻金服的客戶數(shù)是美國(guó)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(dá)到了1500億美元——差不多是世界上最有價(jià)值的金融服務(wù)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(tǒng)銀行、投資機(jī)構(gòu)和保險(xiǎn)公司不同，螞蟻金服建立在數(shù)字核心之上。在其經(jīng)營(yíng)活動(dòng)的“關(guān)鍵路徑”上沒有工人，AI主宰了一切。沒有經(jīng)理批準(zhǔn)貸款，沒有員工提供財(cái)務(wù)建議，沒有代表審批消費(fèi)者的醫(yī)療費(fèi)用。沒有了限制傳統(tǒng)企業(yè)的運(yùn)營(yíng)約束，螞蟻金服能夠以前所未有的方式展開競(jìng)爭(zhēng)，實(shí)現(xiàn)無(wú)約束的增長(zhǎng)，并跨越多個(gè)行業(yè)產(chǎn)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(xiàn)引領(lǐng)著人工智能時(shí)代的到來(lái)。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(guī)模較小、發(fā)展迅速的公司，從斑馬醫(yī)療(Zebra Medical Vision)、Wayfair到Indigo Ag和Ocado。每次當(dāng)我們使用這些公司提供的服務(wù)時(shí)，都會(huì)見到同樣的、令人非常難忘的一幕：與依賴工人、經(jīng)理、工程師、主管或客戶服務(wù)代表運(yùn)營(yíng)傳統(tǒng)業(yè)務(wù)流程不同，我們獲得的價(jià)值是由算法提供的。微軟首席執(zhí)行官薩蒂亞·納德拉把人工智能稱作是公司新的“運(yùn)行時(shí)”（runtime）。誠(chéng)然，是管理人員和工程師設(shè)計(jì)了人工智能，開發(fā)了讓算法工作的軟件，但在此之后，卻是智能系統(tǒng)通過自動(dòng)化的程序或利用外部供應(yīng)商生態(tài)，自行實(shí)現(xiàn)價(jià)值。AI在亞馬遜上定價(jià)，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(tǒng)約束無(wú)疑改變了競(jìng)爭(zhēng)規(guī)則。隨著數(shù)字網(wǎng)絡(luò)和算法被導(dǎo)入企業(yè)的體系結(jié)構(gòu)之中，行業(yè)開始以不同的方式運(yùn)作，行業(yè)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(shù)字公司帶來(lái)的，面對(duì)新的競(jìng)爭(zhēng)對(duì)手，傳統(tǒng)組織也開始轉(zhuǎn)向基于人工智能的運(yùn)營(yíng)模式。沃爾瑪、富達(dá)(Fidelity)、霍尼韋爾(Honeywell)和康卡斯特(Comcast)正在廣泛利用數(shù)據(jù)、算法和數(shù)字網(wǎng)絡(luò)，以贏得新時(shí)代的競(jìng)爭(zhēng)。顯然，無(wú)論你是領(lǐng)導(dǎo)一家數(shù)字型初創(chuàng)企業(yè)，還是致力于改造一家傳統(tǒng)企業(yè)，理解人工智能對(duì)企業(yè)運(yùn)營(yíng)、戰(zhàn)略和競(jìng)爭(zhēng)的革命性影響都是至關(guān)重要的。

The AI Factory

人工智能工廠

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(gè)決策工廠—— 我們稱之為“人工智能工廠”。在谷歌和百度上，軟件每天運(yùn)營(yíng)著數(shù)百萬(wàn)次廣告拍賣。在滴滴、Grab、Lyft和Uber上，算法決定著哪些車可以提供服務(wù)。在亞馬遜上，智能算法為耳機(jī)和polo衫定價(jià)。在沃爾瑪?shù)囊恍╅T店里，機(jī)器人在清潔地板。富達(dá)使用機(jī)器人提供客戶服務(wù)，斑馬醫(yī)療利用機(jī)器人解釋x射線的圖像。在每一個(gè)案例中，人工智能工廠都將決策視為一門科學(xué)，數(shù)據(jù)分析軟件系統(tǒng)地將內(nèi)外部數(shù)據(jù)轉(zhuǎn)化為預(yù)測(cè)、洞察和選擇，從而指導(dǎo)和自動(dòng)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(dòng)數(shù)字公司爆炸式增長(zhǎng)的人工智能往往并不復(fù)雜。盡管帶來(lái)了戲劇性的變化，但需要的人工智能并不是科幻小說里的那些東西——與人類行為或模擬人類推理沒有什么區(qū)別的能力，這種能力有時(shí)被稱為“強(qiáng)人工智能”。事實(shí)上，你只需要一個(gè)計(jì)算機(jī)系統(tǒng)就能完成傳統(tǒng)上由人來(lái)完成的任務(wù)——這通常被稱為“弱人工智能”。

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.

擁有弱人工智能，AI工廠能夠做出一系列關(guān)鍵決策。在某些情況下，它管理信息類業(yè)務(wù)（如谷歌和Facebook）。在其他情況下，它指導(dǎo)公司如何構(gòu)建、交付或運(yùn)營(yíng)實(shí)體產(chǎn)品（如亞馬遜的倉(cāng)儲(chǔ)機(jī)器人或谷歌的自動(dòng)駕駛汽車）。在所有的情況下，數(shù)字決策工廠處理最關(guān)鍵的流程和運(yùn)營(yíng)決策，軟件構(gòu)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(duì)于人工智能工廠，有四個(gè)要素必不可少。一是數(shù)據(jù)管道，這是一個(gè)半自動(dòng)化的過程，用一種系統(tǒng)的、可持續(xù)和可擴(kuò)展的方式收集、清理、集成和保護(hù)數(shù)據(jù)。二是算法，生成關(guān)于業(yè)務(wù)未來(lái)狀態(tài)或行動(dòng)的預(yù)測(cè)值。三是實(shí)驗(yàn)平臺(tái)，在這個(gè)平臺(tái)上測(cè)試新算法的假設(shè)，確保具有預(yù)期的效果。四是基礎(chǔ)設(shè)施，將人工智能嵌入軟件平臺(tái)，并將其連接到內(nèi)外部用戶的系統(tǒng)上。

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

推動(dòng)爆炸性增長(zhǎng)的人工智能通常不是很復(fù)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這樣的搜索引擎為例。一旦有人開始在搜索框中輸入幾個(gè)字母，算法就會(huì)根據(jù)許多用戶之前輸入的詞匯和該用戶過去的行為動(dòng)態(tài)預(yù)測(cè)整個(gè)搜索詞。這些預(yù)測(cè)值會(huì)在下拉菜單（“自動(dòng)建議框”）中顯示出來(lái)，幫助用戶快速鎖定相關(guān)搜索。每一個(gè)擊鍵和每一次點(diǎn)擊都被捕獲為數(shù)據(jù)點(diǎn)，每一個(gè)數(shù)據(jù)點(diǎn)都改進(jìn)了對(duì)未來(lái)搜索的預(yù)測(cè)。人工智能還能生成有機(jī)的搜索結(jié)果，這些搜索結(jié)果來(lái)自于以前收集的web索引，并根據(jù)以前搜索結(jié)果產(chǎn)生的點(diǎn)擊進(jìn)行優(yōu)化。這個(gè)詞的加入也引發(fā)了與用戶搜索最相關(guān)的廣告的自動(dòng)拍賣，這個(gè)結(jié)果是由其它的實(shí)驗(yàn)和學(xué)習(xí)循環(huán)形成的。任何點(diǎn)擊或離開搜索查詢或搜索結(jié)果頁(yè)面都會(huì)提供有用的數(shù)據(jù)。搜索越多，預(yù)測(cè)效果越好，預(yù)測(cè)效果越好，搜索引擎的使用率就越高。

Removing Limits to Scale, Scope, and Learning

消除規(guī)模、范圍和學(xué)習(xí)等因素對(duì)企業(yè)增長(zhǎng)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(yè)革命以來(lái)，規(guī)模概念一直是商業(yè)的核心。偉大的阿爾弗雷德?錢德勒曾經(jīng)描述過，現(xiàn)代工業(yè)企業(yè)是怎樣以低得多的單位成本達(dá)到前所未有的生產(chǎn)水平，從而使大型企業(yè)相對(duì)于規(guī)模較小的競(jìng)爭(zhēng)對(duì)手擁有重要優(yōu)勢(shì)。他還強(qiáng)調(diào)了企業(yè)能夠從擴(kuò)大生產(chǎn)范圍或增加品種中獲得的好處。隨著創(chuàng)新重要性的與日俱增，對(duì)企業(yè)又增加了學(xué)習(xí)能力的要求。規(guī)模、范圍和學(xué)習(xí)能力被認(rèn)為是一個(gè)公司經(jīng)營(yíng)業(yè)績(jī)的主要驅(qū)動(dòng)力。很長(zhǎng)一段時(shí)間以來(lái)，它們都是通過精心定義的業(yè)務(wù)流程來(lái)實(shí)現(xiàn)的，這些業(yè)務(wù)流程依賴于勞動(dòng)力和管理人員向客戶交付產(chǎn)品和服務(wù)，并由傳統(tǒng)的IT系統(tǒng)加以強(qiáng)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(jīng)歷了數(shù)百年，企業(yè)的競(jìng)爭(zhēng)模式只是在緩慢改變?，F(xiàn)在數(shù)字公司徹底改變了規(guī)模、范圍和學(xué)習(xí)的競(jìng)爭(zhēng)范式。AI驅(qū)動(dòng)的業(yè)務(wù)流程相比傳統(tǒng)業(yè)務(wù)流程，以快得多的速度擴(kuò)大服務(wù)能力，拓展服務(wù)范圍，他們可以很容易的與其他數(shù)字化業(yè)務(wù)實(shí)現(xiàn)連接，創(chuàng)造令人難以置信的強(qiáng)大的學(xué)習(xí)和改進(jìn)機(jī)會(huì)，產(chǎn)生更精確和復(fù)雜的客戶行為模型，定制相應(yīng)的服務(wù)。

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(tǒng)運(yùn)營(yíng)模式中，規(guī)模會(huì)達(dá)到一個(gè)均衡點(diǎn)，之后，回報(bào)開始遞減。但在人工智能驅(qū)動(dòng)的運(yùn)營(yíng)模式下，這種情況可能不會(huì)出現(xiàn)，規(guī)?；貓?bào)可能會(huì)持續(xù)攀升至前所未有的水平?，F(xiàn)在，想象一下，當(dāng)一個(gè)人工智能驅(qū)動(dòng)的公司與一個(gè)傳統(tǒng)公司競(jìng)爭(zhēng)，人工智能驅(qū)動(dòng)的公司用極具可擴(kuò)展性的運(yùn)營(yíng)模式為相同的客戶提供類似（或更好）的價(jià)值服務(wù)，結(jié)果會(huì)怎樣呢？

How AI-Driven Companies Can Outstrip Traditional Firms

人工智能驅(qū)動(dòng)的公司如何超越傳統(tǒng)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(tǒng)的運(yùn)營(yíng)模式中，這種規(guī)模增長(zhǎng)帶來(lái)的價(jià)值最終會(huì)逐漸減少，但在數(shù)字運(yùn)營(yíng)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.

我們稱人工智能驅(qū)動(dòng)的公司與傳統(tǒng)公司之間的對(duì)抗為“沖突”。由于學(xué)習(xí)和網(wǎng)絡(luò)效應(yīng)放大了數(shù)量對(duì)價(jià)值創(chuàng)造的影響，建立在數(shù)字核心之上的公司可以超越傳統(tǒng)組織?？紤]一下亞馬遜與傳統(tǒng)零售商、螞蟻金服與傳統(tǒng)銀行、滴滴和優(yōu)步與傳統(tǒng)出租車服務(wù)發(fā)生沖突的后果。正如克萊頓·克里斯坦森、邁克爾·雷諾和羅里·麥克唐納在《什么是顛覆性創(chuàng)新》（哈佛商業(yè)評(píng)論，2015年12月）中指出的，這樣的競(jìng)爭(zhēng)性顛覆不符合顛覆創(chuàng)新模式。沖突不是由技術(shù)或商業(yè)模式中的特定創(chuàng)新引起的。它們是一種完全不同的公司出現(xiàn)的結(jié)果。它們可以從根本上改變行業(yè)，重塑競(jìng)爭(zhēng)優(yōu)勢(shì)的本質(zhì)。

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(qǐng)注意（如上圖），人工智能驅(qū)動(dòng)的運(yùn)營(yíng)模式產(chǎn)生的經(jīng)濟(jì)價(jià)值，可能需要相當(dāng)長(zhǎng)的一段時(shí)間才能接近傳統(tǒng)運(yùn)營(yíng)模式在規(guī)模上產(chǎn)生的價(jià)值。網(wǎng)絡(luò)效應(yīng)在達(dá)到臨界規(guī)模之前產(chǎn)生的價(jià)值很小，而大多數(shù)新應(yīng)用的算法在獲得足夠的數(shù)據(jù)之前都遭遇了“冷啟動(dòng)”。螞蟻金服發(fā)展迅速，但其核心支付服務(wù)——阿里巴巴于2004年推出的支付寶——花了多年時(shí)間才達(dá)到目前的規(guī)模。這就解釋了為什么那些安坐在傳統(tǒng)模式下的高管們一開始很難相信數(shù)字模式會(huì)迎頭趕上。但一旦數(shù)字運(yùn)營(yíng)模式真正開始運(yùn)作，它就能帶來(lái)遠(yuǎn)超傳統(tǒng)企業(yè)的價(jià)值，并迅速超越傳統(tǒng)企業(yè)。

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.

在軟件、金融服務(wù)、零售、電信、媒體、醫(yī)療、汽車甚至農(nóng)業(yè)綜合企業(yè)等行業(yè)，人工智能驅(qū)動(dòng)的企業(yè)與傳統(tǒng)企業(yè)之間的沖突正在發(fā)生。很難想象一個(gè)企業(yè)不面臨著將其運(yùn)營(yíng)模式數(shù)字化和應(yīng)對(duì)新威脅的迫切需要。

Rebuilding Traditional Enterprises

重建傳統(tǒng)企業(yè)

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(duì)于傳統(tǒng)企業(yè)的領(lǐng)導(dǎo)者來(lái)說，同數(shù)字企業(yè)的競(jìng)爭(zhēng)不只是部署企業(yè)軟件，或者是建立數(shù)據(jù)管道、理解算法和進(jìn)行實(shí)驗(yàn)。它需要重新架構(gòu)公司的組織和運(yùn)營(yíng)模式。很長(zhǎng)一段時(shí)間以來(lái)，公司通過歸核化和專業(yè)化在不斷優(yōu)化它們的規(guī)模、范圍和學(xué)習(xí)模式，形成了今天絕大多數(shù)企業(yè)所擁有的煙囪結(jié)構(gòu)。雖然信息技術(shù)經(jīng)歷了幾代的發(fā)展，但并沒有改變這種模式。幾十年來(lái)，信息技術(shù)只是被用來(lái)提高某些特定功能和組織單元的績(jī)效。這反而強(qiáng)化了傳統(tǒng)企業(yè)的煙囪結(jié)構(gòu)，促進(jìn)了企業(yè)功能和產(chǎn)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(jié)構(gòu)是人工智能驅(qū)動(dòng)的增長(zhǎng)模式的大敵。事實(shí)上，像谷歌Ads和螞蟻金服的MyBank這樣的企業(yè)有意的放棄了這些服務(wù)，它們的目的是利用一個(gè)集成的數(shù)據(jù)核心和統(tǒng)一一致的代碼庫(kù)。當(dāng)公司中的每個(gè)煙囪都有自己的數(shù)據(jù)和代碼時(shí)，內(nèi)部的資源、能力和發(fā)展就會(huì)分散化，幾乎不可能跨煙囪或者與外部業(yè)務(wù)網(wǎng)絡(luò)或生態(tài)系統(tǒng)建立連接。想對(duì)客戶進(jìn)行全方位的了解，既要服務(wù)客戶，又要從各個(gè)部門和功能單元獲取信息，也幾乎是不可能的。因此，當(dāng)公司建立一個(gè)新的數(shù)字核心時(shí)，應(yīng)該避免在其內(nèi)部產(chǎn)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.

雖然向人工智能驅(qū)動(dòng)模式轉(zhuǎn)變充滿挑戰(zhàn)，但許多傳統(tǒng)公司——其中一些與我們有過合作——已經(jīng)開始做出轉(zhuǎn)變。事實(shí)上，在最近的一項(xiàng)研究中，我們研究了350多家服務(wù)和制造行業(yè)的傳統(tǒng)企業(yè)，發(fā)現(xiàn)大多數(shù)企業(yè)都開始更加注重?cái)?shù)據(jù)和分析。包括諾德斯特龍、沃達(dá)豐、康卡斯特和visa在內(nèi)的許多公司已經(jīng)取得了重要進(jìn)展，他們將運(yùn)營(yíng)模式的關(guān)鍵組件進(jìn)行了數(shù)字化和重新設(shè)計(jì)，并開發(fā)了復(fù)雜的數(shù)據(jù)平臺(tái)和人工智能。你不必成為一個(gè)軟件初創(chuàng)公司來(lái)數(shù)字化你的關(guān)鍵業(yè)務(wù)元素，但你必須面對(duì)煙囪式的、分散的傳統(tǒng)信息系統(tǒng)，給它賦能，并重構(gòu)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(dá)投資正在利用人工智能為重要領(lǐng)域的業(yè)務(wù)流程賦能，包括客戶服務(wù)、客戶洞察和投資建議。它的人工智能計(jì)劃建立在多年的努力之上，將數(shù)據(jù)資產(chǎn)整合到一個(gè)數(shù)字核中，并圍繞它重新設(shè)計(jì)組織。雖然這項(xiàng)工作并沒有結(jié)束，但是人工智能的影響已經(jīng)在公司的許多高價(jià)值應(yīng)用案例中得到了明顯的體現(xiàn)。為了與亞馬遜競(jìng)爭(zhēng)，沃爾瑪正圍繞人工智能重建其運(yùn)營(yíng)模式，以集成的、基于云的架構(gòu)取代傳統(tǒng)的、煙囪式的企業(yè)軟件系統(tǒng)。這將使沃爾瑪能夠在各種強(qiáng)大的新應(yīng)用程序中使用其獨(dú)特的數(shù)據(jù)資產(chǎn)，通過人工智能和數(shù)據(jù)分析讓越來(lái)越多的任務(wù)自動(dòng)化、并提升效率。在微軟，納德拉正將公司的未來(lái)押注于運(yùn)營(yíng)模式的整體轉(zhuǎn)型。

Rethinking Strategy and Capabilities

重新思考戰(zhàn)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.

隨著以人工智能為驅(qū)動(dòng)的企業(yè)與傳統(tǒng)企業(yè)發(fā)生碰撞，塑造和控制數(shù)字網(wǎng)絡(luò)的能力越來(lái)越能定義競(jìng)爭(zhēng)優(yōu)勢(shì)。（參見2019年1 - 2月的《哈佛商業(yè)評(píng)論》，“為什么有些平臺(tái)蓬勃發(fā)展，而有些卻不能”）。擅長(zhǎng)連接企業(yè)、聚合數(shù)據(jù)、并通過分析和人工智能提取其價(jià)值的組織將占據(jù)上風(fēng)。傳統(tǒng)的網(wǎng)絡(luò)效應(yīng)和人工智能驅(qū)動(dòng)的學(xué)習(xí)曲線會(huì)相互強(qiáng)化，相互促進(jìn)。你可以在谷歌、Facebook、騰訊和阿里巴巴等公司看到這種動(dòng)態(tài)。這些公司已經(jīng)成為強(qiáng)大的“中心”企業(yè)，它們通過許多網(wǎng)絡(luò)連接來(lái)積累數(shù)據(jù)，構(gòu)建必要的算法，以增強(qiáng)不同行業(yè)的競(jìng)爭(zhēng)優(yōu)勢(shì)。

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(shí)，聚焦傳統(tǒng)行業(yè)分析的傳統(tǒng)戰(zhàn)略分析方法正變得越來(lái)越無(wú)效。以汽車企業(yè)為例，他們正面臨著各種新的數(shù)字威脅，從優(yōu)步到Waymo，每一種威脅都來(lái)自傳統(tǒng)行業(yè)的邊界之外。但是，如果汽車行業(yè)的高管們能超越傳統(tǒng)思維，把汽車看作是高度互聯(lián)的、由人工智能驅(qū)動(dòng)的服務(wù)，那么他們不僅可以保護(hù)好自己，還可以通過車內(nèi)的商業(yè)機(jī)會(huì)、廣告、新聞和娛樂信息、基于位置的服務(wù)等來(lái)釋放新的價(jià)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(jīng)給高管們的建議是，在熟悉的行業(yè)里，堅(jiān)持做自己熟悉的生意。但算法和數(shù)據(jù)流的協(xié)同效應(yīng)并不尊重行業(yè)邊界。而那些不能跨越這些邊界利用客戶和數(shù)據(jù)的組織可能會(huì)處于很大的劣勢(shì)。戰(zhàn)略需要聚焦的不是行業(yè)分析和公司內(nèi)部資源的管理，而是公司跨行業(yè)建立的聯(lián)系和公司正在使用的網(wǎng)絡(luò)中的數(shù)據(jù)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(duì)組織及其雇員都有重大影響。機(jī)器學(xué)習(xí)將改變幾乎所有工作的性質(zhì)，無(wú)論職業(yè)、收入水平或?qū)I(yè)領(lǐng)域。毫無(wú)疑問，基于人工智能的運(yùn)營(yíng)模式將會(huì)對(duì)就業(yè)造成實(shí)實(shí)在在的影響。幾項(xiàng)研究表明，目前的工作可能有一半將被人工智能系統(tǒng)取代。對(duì)此我們不應(yīng)該感到太驚訝。畢竟，長(zhǎng)期以來(lái)，運(yùn)營(yíng)模式已經(jīng)被設(shè)計(jì)成讓許多工作任務(wù)是可預(yù)測(cè)和可重復(fù)的。例如，檢查時(shí)掃描產(chǎn)品、制作拿鐵和去除疝氣的流程都可以標(biāo)準(zhǔn)化，不需要太多的人類創(chuàng)造力。雖然人工智能將使很多工作變得更加豐富，并產(chǎn)生各種有趣的機(jī)會(huì)，但似乎不可避免的是，它們也將在許多職業(yè)中造成廣泛的混亂與調(diào)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(diào)整不僅包括工作的替代，還包括傳統(tǒng)能力的削弱。在幾乎每一種情況下，人工智能公司都在挑戰(zhàn)高度專業(yè)化的組織。在人工智能驅(qū)動(dòng)的世界中，競(jìng)爭(zhēng)能力與專門化關(guān)系不大，而更多地與數(shù)據(jù)來(lái)源、處理、分析和算法開發(fā)方面的通用功能有關(guān)。這些新的通用能力正在重塑戰(zhàn)略、業(yè)務(wù)設(shè)計(jì)，甚至領(lǐng)導(dǎo)力。如今，在非常多樣化的數(shù)字和網(wǎng)絡(luò)化公司中，戰(zhàn)略看起來(lái)都很相似，經(jīng)營(yíng)業(yè)績(jī)的驅(qū)動(dòng)因素也是如此。行業(yè)專長(zhǎng)變得不那么重要了。當(dāng)優(yōu)步尋找新的首席執(zhí)行官時(shí)，董事會(huì)聘請(qǐng)的是一位曾運(yùn)營(yíng)過數(shù)字公司的人，運(yùn)營(yíng)的是艾派迪公司，而不是一家豪華轎車服務(wù)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(gè)不同行業(yè)擁有不同核心競(jìng)爭(zhēng)力的時(shí)代，進(jìn)入一個(gè)由數(shù)據(jù)和分析塑造、由算法驅(qū)動(dòng)的核心競(jìng)爭(zhēng)力時(shí)代——所有這些都托管在云端，任何人都可以使用。這就是為什么阿里巴巴和亞馬遜能夠在零售和金融服務(wù)、醫(yī)療保健和信用評(píng)分等完全不同的行業(yè)展開競(jìng)爭(zhēng)。這些部門現(xiàn)在有許多類似的技術(shù)基礎(chǔ)，并使用共同的方法和工具。戰(zhàn)略正從傳統(tǒng)的構(gòu)建基于成本、質(zhì)量、品牌價(jià)值、專門化和垂直專長(zhǎng)等方面的差異，轉(zhuǎn)向打造基于商業(yè)網(wǎng)絡(luò)位置、獨(dú)特?cái)?shù)據(jù)積累和復(fù)雜分析部署等方面的優(yōu)勢(shì)。

The Leadership Challenge

對(duì)領(lǐng)導(dǎo)力挑戰(zhàn)

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.

盡管它可以釋放出巨大的增長(zhǎng)，但消除運(yùn)營(yíng)約束并不總是一件好事。無(wú)摩擦系統(tǒng)容易不穩(wěn)定，一旦運(yùn)行就很難停止。想想一輛沒有剎車的汽車或者一個(gè)不能減速的滑雪者。數(shù)字信號(hào)——例如，病毒式的模因（meme）—可以通過網(wǎng)絡(luò)迅速傳播，而且?guī)缀醪豢赡鼙蛔柚?，即使是最初發(fā)布它的組織或控制網(wǎng)絡(luò)關(guān)鍵樞紐的實(shí)體也不例外。在沒有摩擦的情況下，一個(gè)煽動(dòng)暴力的視頻，或者一個(gè)虛假或被操縱的標(biāo)題，都可以通過各種各樣的網(wǎng)絡(luò)迅速傳播到數(shù)十億人的手中，甚至可以通過變形來(lái)優(yōu)化點(diǎn)擊率和下載。如果你有信息要發(fā)送，人工智能提供了一種奇妙的方式來(lái)接觸大量的人，并為他們個(gè)性化信息。但市場(chǎng)營(yíng)銷者的天堂可能是公民的噩夢(mèng)。

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(shù)字運(yùn)營(yíng)模式在創(chuàng)造價(jià)值的同時(shí)也可能聚集與放大傷害。即使意圖是積極的，潛在的負(fù)面影響也是巨大的。一個(gè)錯(cuò)誤就能使一個(gè)龐大的數(shù)字網(wǎng)絡(luò)遭受毀滅性的網(wǎng)絡(luò)攻擊。如果不加以檢查，算法可能會(huì)在大規(guī)模范圍內(nèi)加劇偏見和錯(cuò)誤信息。風(fēng)險(xiǎn)可能被大大放大。想想數(shù)字銀行正以一種前所未有的方式聚合消費(fèi)者儲(chǔ)蓄。螞蟻金服目前管理著全球大的貨幣市場(chǎng)基金之一，它受托管理數(shù)億中國(guó)消費(fèi)者的儲(chǔ)蓄。由此帶來(lái)的風(fēng)險(xiǎn)是巨大的，尤其是對(duì)于一個(gè)相對(duì)未經(jīng)驗(yàn)證的機(jī)構(gòu)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(shù)字的規(guī)模、范圍和學(xué)習(xí)創(chuàng)造了一系列新的挑戰(zhàn)——不僅僅是隱私和網(wǎng)絡(luò)安全問題，還有由市場(chǎng)集中、就業(yè)調(diào)整和不平等加劇造成的社會(huì)動(dòng)蕩。例如，那些監(jiān)督企業(yè)的機(jī)構(gòu)，也就是監(jiān)管機(jī)構(gòu)，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.

在人工智能驅(qū)動(dòng)的世界里，一旦產(chǎn)品與市場(chǎng)相匹配，用戶數(shù)、參與度和收入就會(huì)飆升。然而，越來(lái)越明顯的是，無(wú)約束的增長(zhǎng)是危險(xiǎn)的。擁抱數(shù)字運(yùn)營(yíng)模式的企業(yè)潛力巨大，對(duì)它們?cè)斐蓮V泛傷害的能力也需要認(rèn)真對(duì)待。平衡好這些機(jī)遇和威脅將是對(duì)企業(yè)和公共機(jī)構(gòu)領(lǐng)導(dǎo)力的真正考驗(yàn)。

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(xué)院企業(yè)管理教授，負(fù)責(zé)技術(shù)、運(yùn)營(yíng)管理部門和數(shù)字創(chuàng)新，為許多科技公司提供咨詢服務(wù)，包括微軟、Facebook和亞馬遜等，與卡里姆·拉克哈尼(Karim Lakhani)合著了《人工智能時(shí)代的競(jìng)爭(zhēng)》。

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(xué)院工商管理教授，哈佛大學(xué)創(chuàng)新科學(xué)實(shí)驗(yàn)室的創(chuàng)始人和聯(lián)合主任。他是《人工智能時(shí)代的競(jìng)爭(zhēng)》一書的合著者之一。

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