人工智能时代的竞争-创新互联
人工智能时代的竞争
封人疯语： 闭上眼睛，想想明天的世界吧，执汽车行业牛耳者是百度、谷歌还是丰田、沃尔沃？数据和算法已经成为整个世界的底层，基于物质世界资源稀缺、非此即彼和人类大脑有限理性的传统逻辑似乎正在被彻底颠覆，数据越多、算法越强、强者恒强，智者通吃。这是一幅非常可怕的图景，也是一幅令人激动向往的图景。斯密用分工描述世界发展，马克思用阶级分析人类未来，在这个崭新时代到来之际，我们需要新的思维逻辑，数据和算法是我们理解明天的关键。
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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，蚂蚁金服成立才5年，客户数突破10亿大关。脱胎于阿里巴巴，蚂蚁金服利用人工智能和支付宝的数据（阿里巴巴的核心移动支付平台）来运营各种不同的业务，包括消费贷款、货币市场基金、财富管理、医疗保险、信用评级服务，甚至还有一款鼓励人们减少碳排放的在线游戏。蚂蚁金服的客户数是美国大银行的10倍多，而员工却不到十分之一。在2018年它的最近一轮融资中，估值达到了1500亿美元——差不多是世界上最有价值的金融服务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.
与传统银行、投资机构和保险公司不同，蚂蚁金服建立在数字核心之上。在其经营活动的“关键路径”上没有工人，AI主宰了一切。没有经理批准贷款，没有员工提供财务建议，没有代表审批消费者的医疗费用。没有了限制传统企业的运营约束，蚂蚁金服能够以前所未有的方式展开竞争，实现无约束的增长，并跨越多个行业产生影响。
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.
这种新型公司的出现引领着人工智能时代的到来。类似蚂蚁金服这样的公司有很多，巨头如谷歌、Facebook、阿里巴巴和腾讯，以及许多规模较小、发展迅速的公司，从斑马医疗(Zebra Medical Vision)、Wayfair到Indigo Ag和Ocado。每次当我们使用这些公司提供的服务时，都会见到同样的、令人非常难忘的一幕：与依赖工人、经理、工程师、主管或客户服务代表运营传统业务流程不同，我们获得的价值是由算法提供的。微软首席执行官萨蒂亚·纳德拉把人工智能称作是公司新的“运行时”（runtime）。诚然，是管理人员和工程师设计了人工智能，开发了让算法工作的软件，但在此之后，却是智能系统通过自动化的程序或利用外部供应商生态，自行实现价值。AI在亚马逊上定价，在Spotify上推荐歌曲，在Indigo上撮合买家和卖家，为蚂蚁金服筛选合格贷款人。
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.
消除传统约束无疑改变了竞争规则。随着数字网络和算法被导入企业的体系结构之中，行业开始以不同的方式运作，行业之间的界限开始变得模糊。这些变化不只是由这些新型的数字公司带来的，面对新的竞争对手，传统组织也开始转向基于人工智能的运营模式。沃尔玛、富达(Fidelity)、霍尼韦尔(Honeywell)和康卡斯特(Comcast)正在广泛利用数据、算法和数字网络，以赢得新时代的竞争。显然，无论你是领导一家数字型初创企业，还是致力于改造一家传统企业，理解人工智能对企业运营、战略和竞争的革命性影响都是至关重要的。
The AI Factory
人工智能工厂
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.
蚂蚁金服这样的新型公司的核心是一个决策工厂—— 我们称之为“人工智能工厂”。在谷歌和百度上，软件每天运营着数百万次广告拍卖。在滴滴、Grab、Lyft和Uber上，算法决定着哪些车可以提供服务。在亚马逊上，智能算法为耳机和polo衫定价。在沃尔玛的一些门店里，机器人在清洁地板。富达使用机器人提供客户服务，斑马医疗利用机器人解释x射线的图像。在每一个案例中，人工智能工厂都将决策视为一门科学，数据分析软件系统地将内外部数据转化为预测、洞察和选择，从而指导和自动化工作流程。
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，推动数字公司爆炸式增长的人工智能往往并不复杂。尽管带来了戏剧性的变化，但需要的人工智能并不是科幻小说里的那些东西——与人类行为或模拟人类推理没有什么区别的能力，这种能力有时被称为“强人工智能”。事实上，你只需要一个计算机系统就能完成传统上由人来完成的任务——这通常被称为“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.
拥有弱人工智能，AI工厂能够做出一系列关键决策。在某些情况下，它管理信息类业务（如谷歌和Facebook）。在其他情况下，它指导公司如何构建、交付或运营实体产品（如亚马逊的仓储机器人或谷歌的自动驾驶汽车）。在所有的情况下，数字决策工厂处理最关键的流程和运营决策，软件构成了公司的核心，而人则被移到了边缘。
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.
对于人工智能工厂，有四个要素必不可少。一是数据管道，这是一个半自动化的过程，用一种系统的、可持续和可扩展的方式收集、清理、集成和保护数据。二是算法，生成关于业务未来状态或行动的预测值。三是实验平台，在这个平台上测试新算法的假设，确保具有预期的效果。四是基础设施，将人工智能嵌入软件平台，并将其连接到内外部用户的系统上。
The AI that drives explosive growth often isn't even all that sophisticated
推动爆炸性增长的人工智能通常不是很复杂
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这样的搜索引擎为例。一旦有人开始在搜索框中输入几个字母，算法就会根据许多用户之前输入的词汇和该用户过去的行为动态预测整个搜索词。这些预测值会在下拉菜单（“自动建议框”）中显示出来，帮助用户快速锁定相关搜索。每一个击键和每一次点击都被捕获为数据点，每一个数据点都改进了对未来搜索的预测。人工智能还能生成有机的搜索结果，这些搜索结果来自于以前收集的web索引，并根据以前搜索结果产生的点击进行优化。这个词的加入也引发了与用户搜索最相关的广告的自动拍卖，这个结果是由其它的实验和学习循环形成的。任何点击或离开搜索查询或搜索结果页面都会提供有用的数据。搜索越多，预测效果越好，预测效果越好，搜索引擎的使用率就越高。
Removing Limits to Scale, Scope, and Learning
消除规模、范围和学习等因素对企业增长影响的限制
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.
工业革命以来，规模概念一直是商业的核心。伟大的阿尔弗雷德•钱德勒曾经描述过，现代工业企业是怎样以低得多的单位成本达到前所未有的生产水平，从而使大型企业相对于规模较小的竞争对手拥有重要优势。他还强调了企业能够从扩大生产范围或增加品种中获得的好处。随着创新重要性的与日俱增，对企业又增加了学习能力的要求。规模、范围和学习能力被认为是一个公司经营业绩的主要驱动力。很长一段时间以来，它们都是通过精心定义的业务流程来实现的，这些业务流程依赖于劳动力和管理人员向客户交付产品和服务，并由传统的IT系统加以强化。
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.
虽然经历了数百年，企业的竞争模式只是在缓慢改变。现在数字公司彻底改变了规模、范围和学习的竞争范式。AI驱动的业务流程相比传统业务流程，以快得多的速度扩大服务能力，拓展服务范围，他们可以很容易的与其他数字化业务实现连接，创造令人难以置信的强大的学习和改进机会，产生更精确和复杂的客户行为模型，定制相应的服务。
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.
在传统运营模式中，规模会达到一个均衡点，之后，回报开始递减。但在人工智能驱动的运营模式下，这种情况可能不会出现，规模回报可能会持续攀升至前所未有的水平。现在，想象一下，当一个人工智能驱动的公司与一个传统公司竞争，人工智能驱动的公司用极具可扩展性的运营模式为相同的客户提供类似（或更好）的价值服务，结果会怎样呢？
How AI-Driven Companies Can Outstrip Traditional Firms
人工智能驱动的公司如何超越传统公司
The value that scale delivers eventually tapers off in traditional operating models, but in digital operating models, it can climb much higher.
在传统的运营模式中，这种规模增长带来的价值最终会逐渐减少，但在数字运营模式中，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.
我们称人工智能驱动的公司与传统公司之间的对抗为“冲突”。由于学习和网络效应放大了数量对价值创造的影响，建立在数字核心之上的公司可以超越传统组织。考虑一下亚马逊与传统零售商、蚂蚁金服与传统银行、滴滴和优步与传统出租车服务发生冲突的后果。正如克莱顿·克里斯坦森、迈克尔·雷诺和罗里·麦克唐纳在《什么是颠覆性创新》（哈佛商业评论，2015年12月）中指出的，这样的竞争性颠覆不符合颠覆创新模式。冲突不是由技术或商业模式中的特定创新引起的。它们是一种完全不同的公司出现的结果。它们可以从根本上改变行业，重塑竞争优势的本质。
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.
请注意（如上图），人工智能驱动的运营模式产生的经济价值，可能需要相当长的一段时间才能接近传统运营模式在规模上产生的价值。网络效应在达到临界规模之前产生的价值很小，而大多数新应用的算法在获得足够的数据之前都遭遇了“冷启动”。蚂蚁金服发展迅速，但其核心支付服务——阿里巴巴于2004年推出的支付宝——花了多年时间才达到目前的规模。这就解释了为什么那些安坐在传统模式下的高管们一开始很难相信数字模式会迎头赶上。但一旦数字运营模式真正开始运作，它就能带来远超传统企业的价值，并迅速超越传统企业。
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.
在软件、金融服务、零售、电信、媒体、医疗、汽车甚至农业综合企业等行业，人工智能驱动的企业与传统企业之间的冲突正在发生。很难想象一个企业不面临着将其运营模式数字化和应对新威胁的迫切需要。
Rebuilding Traditional Enterprises
重建传统企业
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.
对于传统企业的领导者来说，同数字企业的竞争不只是部署企业软件，或者是建立数据管道、理解算法和进行实验。它需要重新架构公司的组织和运营模式。很长一段时间以来，公司通过归核化和专业化在不断优化它们的规模、范围和学习模式，形成了今天绝大多数企业所拥有的烟囱结构。虽然信息技术经历了几代的发展，但并没有改变这种模式。几十年来，信息技术只是被用来提高某些特定功能和组织单元的绩效。这反而强化了传统企业的烟囱结构，促进了企业功能和产品的分散化。
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，烟囱结构是人工智能驱动的增长模式的大敌。事实上，像谷歌Ads和蚂蚁金服的MyBank这样的企业有意的放弃了这些服务，它们的目的是利用一个集成的数据核心和统一一致的代码库。当公司中的每个烟囱都有自己的数据和代码时，内部的资源、能力和发展就会分散化，几乎不可能跨烟囱或者与外部业务网络或生态系统建立连接。想对客户进行全方位的了解，既要服务客户，又要从各个部门和功能单元获取信息，也几乎是不可能的。因此，当公司建立一个新的数字核心时，应该避免在其内部产生深层次的组织分歧。
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.
虽然向人工智能驱动模式转变充满挑战，但许多传统公司——其中一些与我们有过合作——已经开始做出转变。事实上，在最近的一项研究中，我们研究了350多家服务和制造行业的传统企业，发现大多数企业都开始更加注重数据和分析。包括诺德斯特龙、沃达丰、康卡斯特和visa在内的许多公司已经取得了重要进展，他们将运营模式的关键组件进行了数字化和重新设计，并开发了复杂的数据平台和人工智能。你不必成为一个软件初创公司来数字化你的关键业务元素，但你必须面对烟囱式的、分散的传统信息系统，给它赋能，并重构公司文化。
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.
富达投资正在利用人工智能为重要领域的业务流程赋能，包括客户服务、客户洞察和投资建议。它的人工智能计划建立在多年的努力之上，将数据资产整合到一个数字核中，并围绕它重新设计组织。虽然这项工作并没有结束，但是人工智能的影响已经在公司的许多高价值应用案例中得到了明显的体现。为了与亚马逊竞争，沃尔玛正围绕人工智能重建其运营模式，以集成的、基于云的架构取代传统的、烟囱式的企业软件系统。这将使沃尔玛能够在各种强大的新应用程序中使用其独特的数据资产，通过人工智能和数据分析让越来越多的任务自动化、并提升效率。在微软，纳德拉正将公司的未来押注于运营模式的整体转型。
Rethinking Strategy and Capabilities
重新思考战略和能力
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.
随着以人工智能为驱动的企业与传统企业发生碰撞，塑造和控制数字网络的能力越来越能定义竞争优势。（参见2019年1 - 2月的《哈佛商业评论》，“为什么有些平台蓬勃发展，而有些却不能”）。擅长连接企业、聚合数据、并通过分析和人工智能提取其价值的组织将占据上风。传统的网络效应和人工智能驱动的学习曲线会相互强化，相互促进。你可以在谷歌、Facebook、腾讯和阿里巴巴等公司看到这种动态。这些公司已经成为强大的“中心”企业，它们通过许多网络连接来积累数据，构建必要的算法，以增强不同行业的竞争优势。
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.
与此同时，聚焦传统行业分析的传统战略分析方法正变得越来越无效。以汽车企业为例，他们正面临着各种新的数字威胁，从优步到Waymo，每一种威胁都来自传统行业的边界之外。但是，如果汽车行业的高管们能超越传统思维，把汽车看作是高度互联的、由人工智能驱动的服务，那么他们不仅可以保护好自己，还可以通过车内的商业机会、广告、新闻和娱乐信息、基于位置的服务等来释放新的价值。
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.
曾经给高管们的建议是，在熟悉的行业里，坚持做自己熟悉的生意。但算法和数据流的协同效应并不尊重行业边界。而那些不能跨越这些边界利用客户和数据的组织可能会处于很大的劣势。战略需要聚焦的不是行业分析和公司内部资源的管理，而是公司跨行业建立的联系和公司正在使用的网络中的数据流。
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.
所有这些变化对组织及其雇员都有重大影响。机器学习将改变几乎所有工作的性质，无论职业、收入水平或专业领域。毫无疑问，基于人工智能的运营模式将会对就业造成实实在在的影响。几项研究表明，目前的工作可能有一半将被人工智能系统取代。对此我们不应该感到太惊讶。毕竟，长期以来，运营模式已经被设计成让许多工作任务是可预测和可重复的。例如，检查时扫描产品、制作拿铁和去除疝气的流程都可以标准化，不需要太多的人类创造力。虽然人工智能将使很多工作变得更加丰富，并产生各种有趣的机会，但似乎不可避免的是，它们也将在许多职业中造成广泛的混乱与调整。
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.
这种混乱与调整不仅包括工作的替代，还包括传统能力的削弱。在几乎每一种情况下，人工智能公司都在挑战高度专业化的组织。在人工智能驱动的世界中，竞争能力与专门化关系不大，而更多地与数据来源、处理、分析和算法开发方面的通用功能有关。这些新的通用能力正在重塑战略、业务设计，甚至领导力。如今，在非常多样化的数字和网络化公司中，战略看起来都很相似，经营业绩的驱动因素也是如此。行业专长变得不那么重要了。当优步寻找新的首席执行官时，董事会聘请的是一位曾运营过数字公司的人，运营的是艾派迪公司，而不是一家豪华轿车服务公司。
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.
我们正在从一个不同行业拥有不同核心竞争力的时代，进入一个由数据和分析塑造、由算法驱动的核心竞争力时代——所有这些都托管在云端，任何人都可以使用。这就是为什么阿里巴巴和亚马逊能够在零售和金融服务、医疗保健和信用评分等完全不同的行业展开竞争。这些部门现在有许多类似的技术基础，并使用共同的方法和工具。战略正从传统的构建基于成本、质量、品牌价值、专门化和垂直专长等方面的差异，转向打造基于商业网络位置、独特数据积累和复杂分析部署等方面的优势。
The Leadership Challenge
对领导力挑战
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.
尽管它可以释放出巨大的增长，但消除运营约束并不总是一件好事。无摩擦系统容易不稳定，一旦运行就很难停止。想想一辆没有刹车的汽车或者一个不能减速的滑雪者。数字信号——例如，病毒式的模因（meme）—可以通过网络迅速传播，而且几乎不可能被阻止，即使是最初发布它的组织或控制网络关键枢纽的实体也不例外。在没有摩擦的情况下，一个煽动暴力的视频，或者一个虚假或被操纵的标题，都可以通过各种各样的网络迅速传播到数十亿人的手中，甚至可以通过变形来优化点击率和下载。如果你有信息要发送，人工智能提供了一种奇妙的方式来接触大量的人，并为他们个性化信息。但市场营销者的天堂可能是公民的噩梦。
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.
数字运营模式在创造价值的同时也可能聚集与放大伤害。即使意图是积极的，潜在的负面影响也是巨大的。一个错误就能使一个庞大的数字网络遭受毁灭性的网络攻击。如果不加以检查，算法可能会在大规模范围内加剧偏见和错误信息。风险可能被大大放大。想想数字银行正以一种前所未有的方式聚合消费者储蓄。蚂蚁金服目前管理着全球大的货币市场基金之一，它受托管理数亿中国消费者的储蓄。由此带来的风险是巨大的，尤其是对于一个相对未经验证的机构而言。
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.
数字的规模、范围和学习创造了一系列新的挑战——不仅仅是隐私和网络安全问题，还有由市场集中、就业调整和不平等加剧造成的社会动荡。例如，那些监督企业的机构，也就是监管机构，正在努力跟上所有这些快速的改变。
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.
在人工智能驱动的世界里，一旦产品与市场相匹配，用户数、参与度和收入就会飙升。然而，越来越明显的是，无约束的增长是危险的。拥抱数字运营模式的企业潜力巨大，对它们造成广泛伤害的能力也需要认真对待。平衡好这些机遇和威胁将是对企业和公共机构领导力的真正考验。
作者介绍
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 哈佛商学院企业管理教授，负责技术、运营管理部门和数字创新，为许多科技公司提供咨询服务，包括微软、Facebook和亚马逊等，与卡里姆·拉克哈尼(Karim Lakhani)合著了《人工智能时代的竞争》。
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) 哈佛商学院工商管理教授，哈佛大学创新科学实验室的创始人和联合主任。他是《人工智能时代的竞争》一书的合著者之一。
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