From Prediction to Transformation

投资者：“贵公司的人工智能会为业务带来什么帮助？”

Investor: “What will your artificial intelligence do for businesses?”

 

初创企业创始人：“提供洞见。”

Start-up founder: “It will give them insights.”

 

我们希望每次有企业家向创造性颠覆实验室（Creative Destruction Lab）的导师和投资者做出这样的回答时，我们都能得到一毛钱。创造性颠覆实验室是我们在多伦多大学创立的一个全球种子期初创企业项目。

We wish we had a dime for every time an entrepreneur gave that answer to the mentors and investors at the Creative Destruction Lab, a global seed-stage start-up program we created at the University of Toronto.

 

尽管回答老套，但正因为以错误的方式思考人工智能（AI）的进步会创造的价值，才会得出“洞见”这个结论。事实上，我们感觉“洞见”通常代表着“我们不知道该如何处理我们AI的预测”。

Though it’s the stock response, “insights” is precisely the wrong way to think about how an advance in AI will create value. In fact, we feel that “insights” often is code for “We don’t know what to do with our AI’s predictions.”

 

一个远比这更好的答案是描述预测会改善的那些决策，因为AI只有在能够带来更好决策时才有价值。

A much better answer would be to describe the decisions that the predictions will improve, because AI has value only if it leads to better decision-making.

 

对企业家来说，好消息是AI发挥这一作用的机会数不胜数。企业所做的决策数量一直在增加，而做出正确决策的需求——在运营的每个领域——从未像现在这样大。根据哈佛大学肯尼迪学院（Harvard Kennedy School）戴维·戴明（David Deming）的研究，1960年，只有6%的工作需要用到解决问题、诊断、制定战略以及确定轻重缓急等核心决策技能。到2018年，这个数字已达34%。

The good news for entrepreneurs is that the opportunities for AI to do that are countless. The number of decisions businesses make has been rising, and the need to make the right ones—in every area of operations—has never been greater. Consider that in 1960 just 6% of jobs required core decision-making skills such as problem-solving, diagnosing, strategizing, and prioritizing, according to research by David Deming of the Harvard Kennedy School. By 2018 that number had reached 34%.

 

不过，正如我们在后续几页中所要展示的，运用AI并不仅仅关乎改善具体的决策。一家企业在某一领域的决策通常会对其他领域的决策产生影响，所以引入AI通常需要重新审视和重新设计整个决策系统。让我们首先看看某一举措的具体例子，在这一例子中，人们会看到AI是如何最终完全改变了相关系统创造价值的方式。

But as we’ll show in the following pages, implementing AI isn’t just about improving specific decisions. Decisions in one area of an organization usually have an impact on decisions made in others, so introducing AI often entails revisiting and redesigning whole systems of decision-making. Let’s begin by looking at a specific example of an initiative where that was the case and how AI ended up completely changing the way the system involved created value.

 

新西兰是如何赢得美洲杯的

How New Zealand Won the America’s Cup

 

帆船制造商和船员们五千年来一直在完善他们的技术。虽然商业航运不再依靠风力推进，但帆船运动的创新从未停止。

Sailboat makers and sailors have been refining their techniques for 5,000 years. Even though commercial shipping no longer relies on wind for propulsion, innovations in sailing have never stopped.

 

帆船运动的最高奖项（也是国际体育运动中最古老的大奖）是美洲杯。今天，这项比赛既是技术方面的较量，也是船员技能的角逐。数以百万计的资金投入到船只设计中。由于风、水和船的物理学知识已被人充分了解，参赛选手会使用模拟器来确定最有效的设计，并在不实际建造船只的情况下对船只进行测试。正如新西兰酋长队（Emirates Team New Zealand）在2017年赢得美洲杯时发现的那样，拥有最好模拟器的团队可以获得巨大优势。

The top prize in sailing (and the oldest trophy in international sports) is the America’s Cup. Today the race is as much about technology as about the skills of the crew. Millions of dollars go into boat design. Since the physics of wind, water, and ships are well understood, competitors use simulators to identify the most effective designs and to test boats without actually building them. The team with the best simulator gains a big advantage, as Emirates Team New Zealand discovered in 2017, when it won the cup.

 

当该队成员在筹划2021年的比赛时，他们想知道自己是否能加快设计进程。在与全球咨询公司麦肯锡（McKinsey）合作之后，他们发现了创新的主要瓶颈：人类船员。人类船员在模拟器中驾船需要的是时间；没有办法提高其成员对环境的反应速度并相应地操纵船只。船员们按照人类时间表工作，这还不够快。

As the members of the team planned for the 2021 race, they wondered if they could speed up the design process. Partnering with McKinsey, the global consultancy, they identified the main bottleneck to innovation: human sailors. It takes time for a human crew to sail a boat in the simulator; there’s no way to increase the pace at which its members react to conditions and maneuver the boat in response. The sailors work on a human time scale, and that isn’t fast enough.

 

该团队使用了类似于在围棋这一流行战略棋盘游戏中击败世界顶级选手的AI技术，教一个AI程序驾驶帆船。这个机器人不需要睡觉或吃饭，它可以在人类船员只能进行少量模拟的相同时间内进行数千次模拟。八周后，AI开始在模拟器中击败船员。

Using technology similar to the AI that beat the world’s top players of the popular strategy board game Go, the team taught an AI program to sail. The bot didn’t need to sleep or eat, and it could run thousands of simulations in the same time that it took the human crew to run just a handful. After eight weeks the AI started to beat the sailors in the simulator.

 

事情开始变得有趣。AI开始教人类船员新的技巧。正如开发团队的一名成员告诉《连线》（Wired）杂志的那样，“机器人实际上做的事情让船员们觉得有违常理，但他们会在水上进行试验，而且这些行为确实有效。”此前，船只设计师需要人类来测试创新。找到新设计船只的最佳使用方式可能需要数周时间。

That’s when things got interesting. The AI began teaching the human sailors new tricks. As a member of the development team told Wired magazine, “The bot was actually doing things that felt counterintuitive to the sailors, but they’d try them out on the water and they’d actually work.” Previously, the boat designers had needed humans to test out any innovation. Figuring out the best way to use a newly designed boat could take weeks.

 

相比之下，AI可以一天24小时同时试验船只的多种变化。它可以尝试不同的比赛战术。它加快了设计迭代的周期和新动作的开发。一旦AI发现了一个卓越的解决方案，人类船员就可以效法。正如一位队员所言：“加快学习进程极具价值，既能让设计团队尽可能多地探索设计空间，又能让船员最大程度发挥某一特定设计的性能。”那一年，新西兰酋长队以七比三的成绩夺魁。

The AI, in contrast, could experiment with multiple variations of the boat simultaneously, 24 hours a day. It could try different racing tactics. It sped up the cycle of design itera-tion and the development of new maneuvers. Once the AI figured out a superior solution, the human sailors could copy it. As one team member put it, “Accelerating the learning process is extremely valuable, both in terms of allowing the design team to explore as much of the design space as possible and the sailors to maximize performance for a given design.” That year Emirates Team New Zealand claimed the trophy, winning seven races to three.

 

为何AI的这一使用如此新颖？抛开允许在复杂环境中进行模拟这一令人印象深刻的技术不谈，关键的影响在于系统层面。AI并没有向新西兰酋长队提供一些洞见。相反，它被植入了决策系统。

Why was this use of AI so novel? Setting aside the impressive technology that allowed for simulations in complex environments, the key impact was at the system level. The AI wasn’t handing Emirates Team New Zealand some insights. Instead, it was being built into a system of decision-making.

 

备战赛事涉及两类决策：关于船只设计的决策和关于航行操作的决策。虽然模拟器长期以来一直用于船只设计，但操作一直是由人类来实现的。AI在比赛中并未实际驾驶船只——规则仍然要求真船由真人驾驶——但它加快了创新进程，并让船只设计与航行操作之间得到更好的协调。模拟船只和AI船员的完整系统使这两种决策都得到改进。

Race preparation involves two types of decisions: those about boat design and those about sailing maneuvers. While simulators had long been used for boat design, the maneuvers had always been worked out by humans. The AI didn’t actually pilot the boat in the race—the rules still require that real boats be piloted by real people—but it sped up the innovation process and allowed better coordination between boat design and sailing maneuvers. The complete system of the simulated boat and the AI sailor enabled improvements to both kinds of decisions.

 

为何系统改变需要时间

Why System Change Takes Time

 

一项新技术的系统性影响可能需要一段时间才能显现。当某项技术出现时，人们起初是在狭小的范围内应用它。比如，当人类用电力取代蒸汽作为动力时，企业是在蒸汽所需的水难以获得的地方使用电。在爱迪生发明灯泡20年后，只有3%的美国企业使用电力。同样，1987年，在计算机进入企业几十年后，经济学家罗伯特·索洛（Robert Solow）指出：“你可以在任何地方看到计算机时代，但生产力统计领域除外。”计算的潜力显而易见，但其影响仍然很弱。

It can take a while for the systemic impact of a new technology to become apparent. When a technology emerges, people initially apply it narrowly. When electric power was invented as a substitute for steam power, for instance, businesses used it where the water needed for steam was hard to come by. Two decades after Edison switched on the light bulb, only 3% of U.S. businesses used electricity. Similarly, in 1987, decades after the introduction of computers into businesses, the economist Robert Solow noted, “You can see the computer age everywhere but in the productivity statistics.” The potential of computing was clear, but the impact remained muted.

 

同样的事情也发生在AI身上。尽管存在一些其他标签，但新的AI技术基本上就是统计学的进步。它们让人有可能预测更多方面的结果，并且在这样做的过程中，利用原本可能没有用过的数据。而它们最初的应用集中在它们能够立即提供的东西上：比人类做得更好、更便宜的预测。

The same thing has happened with AI. Despite some alternative branding, new AI technologies are basically advances in statistics. They make it possible to predict more-multifaceted outcomes and, in doing so, take advantage of data that otherwise might be unexploited. And their initial applications were focused on what they could immediately deliver: better and cheaper predictions than humans were making.

 

翻译软件作为AI的一个早期应用，是一个很好的例子。它根据真人翻译以前文本的方式，预测人们会如何将某一特定文本从一种语言翻译成另一种语言。AI对医学影像进行分类是另一项早期应用，它可以预测放射科专家对扫描结果会有什么说法。这两项应用都利用了群体的智慧，通常能够比单人做出准确得多的预测。类似这样的应用可能具有巨大的商业价值。以加拿大公司Verafin为例。该公司被纳斯达克以27.5亿美元收购。原因何在？因为它由AI驱动的识别金融欺诈的技术正被数百家金融机构用来替代过去行使这一职责的安全团队。

Translation software, an early application of AI, is a good example. It predicts how people would translate a given text from one language to another on the basis of how real humans have translated previous texts. AI classifying medical images, another early application, predicted what expert radiologists would say that scans showed. Both applications leverage the wisdom of the crowd, which can often make far more accurate predictions than one person can. Applications like these can have enormous commercial value. Take the Canadian company Verafin, which was acquired by Nasdaq for $2.75 billion. Why? Because its AI-driven technologies for identifying financial fraud were being used by hundreds of financial institutions as a substitute for the security teams that used to perform that function.

 

这些新的应用可能会推动一些重大进步，但它们几乎不具有变革性质。它们不动声色就融入了现有的业务，恰好取代了传统上进行预测的人类。在其他所有方面，企业都没有发生改变。

These new applications may drive some important advances, but they’re hardly transformational. They slot into existing businesses without much fuss, precisely replacing the humans who traditionally made predictions. In all other respects, the businesses are unchanged.

 

可是，当我们考虑电力和计算机化的影响时，我们考虑的不是狭隘的应用；我们考虑的是变革。由于有了电力，工厂不再需要位于水源附近，也不再需要多个楼层来优化蒸汽的使用。它们可以位于距离水源数百英里远的地方，并在同一楼层铺展开来，使得一种新型的大规模生产系统具备了可行性。计算机产生了同样的影响。它们从美其名曰的计算机器演变为史蒂夫·乔布斯（Steve Jobs）所形容的“思维的自行车”——而不是思维的替代品。

But when we consider the impact of electricity and computerization, we don’t think of narrow applications; we think of transformation. Thanks to electricity, factories no longer had to be located near water and have multiple stories to optimize the use of steam. They could be located hundreds of miles away from a water source and spread out on the same floor, making a new type of mass-production system feasible. Computers had the same impact. They evolved from being glorified calculating machines into what Steve Jobs described as “bicycles for the mind”—not substitutes for it.

 

这就是从美洲杯获得的真正借鉴。新西兰酋长队并没有让人脱离这一过程。是的，人们有可能想象一个完全自动化的解决方案来做出每一项决策。但这种方法肯定十分罕见。AI预测提供的信息可以改善决策，而决策是由人做出的。有意思的是，由于有了AI，区别不在于机器是否做得更多，而在于谁是做决策的最佳人选。

And that’s the real lesson from the America’s Cup. Emirates Team New Zealand didn’t take the people out of the process. Yes, it is possible to imagine a fully automated solution that makes every decision. But that approach is surely a rarity. AI prediction provides information that improves decisions, which are made by people. Interestingly, with AI the difference is not so much whether machines do more but who the best people to make decisions are.

 

AI在如何改变决策

How AI Is Changing Decision-Making

 

当苹果公司发起智能手机革命时，没有人想过，出租车行业要完蛋了。但共享乘车之所以成为可能，只是因为连接互联网的手机允许人们通过应用程序叫车，而且可以廉价地获得导航信息。比如在伦敦，出租车司机需要三到四年的时间来了解该市所有的街道和路经它们的最佳路线。今天，智能手机上的AI让任何人都能预测去那里的最佳路线，交通状况也被考虑在内。假如智能手机不存在，出租车业务可能仍欣欣向荣。

When Apple launched the smartphone revolution, no one thought, It’s curtains for the taxi industry. But ride-sharing was possible only because internet-connected mobile phones allowed people to hail rides through an app and get navigation information cheaply. In London, for instance, it takes three to four years for cabdrivers to learn all the city’s streets and the best routes through them. Today AI on smartphones allows anyone to predict the best routes there, taking into account traffic conditions. If smartphones didn’t exist, the taxi business might still be thriving.

 

大多数决策对决策者有两点要求：预测某一决策可能产生结果的能力，以及判断力。预测主要的依据是数据。（考虑到可选的路线和交通状况，这次出行可能需要多长时间？）判断基本上是对环境因素的主观评估，这些因素不容易简化成数据。（这位客户更喜欢最近路线还是更喜欢观光路线？）

Most decisions require two things of the decision-maker: the ability to predict the possible outcomes of a decision, and judgment. Prediction is largely based on data. (Given the available routes and the traffic conditions, how long is the trip likely to take?) Judgment is basically a subjective assessment of contextual factors that are not easily reduced to data. (Will this customer prefer a quick trip or the scenic route?)

 

出租车司机同时具备这两种技能。普通司机的局限性更大；他们可以衡量乘客的喜好（判断），但不太擅长导航（预测）。可是，如果给普通司机配上导航软件，他们就与出租车司机不相上下了。如果再增加一个不需要里程表的平台、一种支付的方法、一个分配司机接单的中央调度员，任何有权访问这一平台的司机都可以提供乘车服务。

Taxi drivers have both skills. Ordinary drivers are more limited; they can gauge passenger preferences (judgment) but are less adept at navigation (prediction). But pair ordinary drivers with navigation software, and they match taxi drivers. Add a platform that eliminates the need for a mileage meter, a method of taking payment, and a central dispatcher who assigns drivers to fill pickup requests, and any driver with access to the platform can offer rides.

 

这些平台及其AI产生了两个重要影响。其一，更多的人现在可以参与到乘车决策中来，其二，司机对决策的控制权下降。由于共享乘车平台可以匹配司机和乘客，并确定最佳路线，司机需要做的唯一事情就是专注于提供舒适和愉快的搭乘服务，以满足分配给他们的客户。这两个影响削弱了传统出租车司机的实力，并改变了这个行业。

The platforms and their AI had two important effects. First, vastly more people could now be involved in making decisions about rides, and second, drivers’ control over decisions decreased. Because the ride-share platform could match drivers and passengers and identify the best routes, all the drivers had to do was focus on providing comfortable and pleasant rides that satisfied the customers assigned to them. Those two effects weakened the power of traditional taxi drivers and transformed the industry.

 

在某些情况下，AI只是集中了决策，而没有改变拥有控制权的人。让我们来看看招聘过程。在多数大型企业中，招聘过程是由人力资源部门管理的。传统上，招聘涉及大量的人力资源部门人员，他们会做出许多小的决定，特别是关于筛选申请人的决定，这可能需要几组人员翻阅数百份简历，筛选出前景看好的候选人进行面试。由于有了AI，一名人力资源主管就可以决定用什么标准来决定谁获得面试机会。基本流程和关键决策者保持不变，但需要的人更少。

In some cases, AI simply concentrates decision-making without changing who has control. Look at the hiring process, which in most large organizations is managed by the human resources department. Traditionally, hiring has involved a great many HR people who make a lot of small decisions, especially about screening applicants, which can require teams of people looking through hundreds of résumés in order to identify promising candidates to interview. Thanks to AI, one HR executive can decide what criteria to use to decide who gets an interview. The basic process and the key decision-maker remain the same, but fewer people are needed.

 

在其他情况下，AI会彻底集中决策，完全改变决策的方式和地点。信用卡验证就是一个典型的例子。在自动验证卡片的联网设备推出之前，商家会自己判断是否接受某人的信用卡。如果他们怀疑有欺诈——比如，如果某人的签名与卡上的签名不一致，或者某位客户没有身份证明——他们可以拒绝接受。他们对老顾客的卡片更容易接受。可是，最初由原始数据库核验、现在由AI预测驱动的那些系统已经将这个过程自动化。信用卡购物是根据一小群人（最有可能是一个委员会）制定的规则来批准的，这些人创建的风险参数会嵌入运行验证设备的程序。

In other cases, AI radically centralizes decision-making, completely changing how and where it happens. Credit card verification is a case in point. Before the rollout of connected devices that automatically validate cards, merchants would make their own judgments about whether to accept someone’s card. They could reject it if they suspected fraud—for instance, if someone’s signature didn’t match the one on the card or a customer didn’t have supporting ID. And they could readily accept cards from regular customers. But systems driven first by crude database checks and now by AI prediction have automated the process. Credit card purchases are approved according to rules set by a small group of people, most likely a committee, which creates the risk parameters embedded in the programs that run verification devices.

 

其他情况下，AI的引入不仅将决策权留给了现有的决策者，而且让他们（更分散）的判断更加重要。AI在医学影像中的应用就是一个典型的例子。

In still other cases the introduction of AI not only leaves decisions in the hands of existing decision-makers but makes their (more decentralized) judgment more important. The use of AI in medical imagery is a case in point.

 

由诊断产生的治疗决定一直都是由患者的医生做出的。可是，在AI预测出现之前，医生通常会请来放射科专家，执行核磁共振、超声波或X光等医学成像程序，并利用专家的判断来做出诊断。实际上，医生在做出决定时需要放射科医生的判断。随着AI支持的诊断取代了放射科医生的判断，现在治疗决定中涉及的唯一判断是患者医生的判断。因此，这令医生的重要性和影响力更大，而放射科医生的重要性和影响力则降低。

Treatment decisions arising from a diagnosis are and always have been made by the patient’s physician. But before the advent of AI prediction, a physician would often call in an expert radiologist, who would perform a medical imaging procedure like an MRI, ultrasound, or X-ray and use his or her judgment to make a diagnosis. In effect, the decisions of the radiologists were needed for the physicians to make their decisions. With AI-enabled diagnosis replacing the radiologist’s judgment, the only judgment now involved in treatment decisions is that of the patient’s physician. That consequently makes the physician more important and powerful and the radiologist less so.

 

在所有这些情况下，AI的应用都改变了制定决策的方式和人员。将AI引入你公司的决策并不仅仅影响到你。它还会影响到你在价值链中的合作伙伴和你业务所在的生态系统。对你有效的东西可能会给他们带来问题。现在让我们来看看原因。

In all these cases the application of AI has changed how and by whom decisions are made. But the introduction of AI into your company’s decision-making doesn’t affect just you. It also affects your partners in the value chain and the ecosystem you operate in. What works for you may create problems for them. Let’s look now at how that can happen.

 

AI如何转移不确定性

How AI Shifts Uncertainty

 

设想一下，你在经营一家餐馆。食客进来点餐，然后厨师们做这些菜。在任何时候，他们能做的菜肴都是有限制的，这是由厨师的技能、点餐的总数以及原料和设备的可用状况决定的。如果你允许你的顾客点他们可能喜欢的任何菜肴，那就会出现问题。

Imagine you’re running a restaurant. Diners come in and order meals. The cooks then make them. At any given time there are constraints on what dishes they can make, which are driven by the skill of the chefs, the total number of orders, and the availability of ingredients and equipment. If you allow your customers to order any dish they might fancy, there will be problems.

 

因此，你要做的是制定一个菜单。你限制了顾客的选择，这样你就能真正做出他们点的菜。从厨房的角度来看，菜单创造了可靠性，防止了始料不及的突发奇想。每周你都需要订购原料，这些原料都要依据菜单。如果菜单上有鳄梨色拉酱，你就需要鳄梨。你每周订购100磅。有时这个量太多，你会把多余的扔掉。在其他时候，100磅太少，你会错失销售机会。

What you do, therefore, is set a menu. You limit the choices of your customers so that you can actually make what they order. From the perspective of the kitchen, the menu creates reliability and prevents unexpected surprises. Every week you need to order ingredients, which are based on the menu. If guacamole is on the menu, you need avocados. You order 100 pounds every week. Sometimes that’s too much, and you throw out the excess. At other times 100 pounds is too little, and you miss out on sales.

 

假设你采用AI进行需求预测（客户会选择什么），你会发现它行之有效。现在，你在某些周的订单少至30磅。在其他周，你需要300磅。你的浪费减少，销售增加。盈利能力会提升。

Let’s say you adopt AI for demand forecasting (what customers will choose), and you find that it works. Now some weeks you order as little as 30 pounds. Other weeks you need 300 pounds. You waste less and sell more. Profitability rises.

 

可是，你的当地供应商已经习惯于每周为你采购100磅。现在它因你的原因而面临更多的不可预测性。它的其他客户也在使用AI进行需求预测，需求开始大幅波动。于是，该供应商决定采用AI进行自己的需求预测。它过去每周订购2.5万磅鳄梨。现在它的订单从5000磅到5万磅不等。你的供应商的水果货源相应地也需要开发AI，其订单也开始波动。这样一路追溯到种植者那里，他们需要提前一年或更长时间做出作物种植规模的决定。

But your local supplier has been used to buying 100 pounds for you each week. Now it faces more unpredictability because of you. Its other customers are also using AI for demand forecasting, and demand starts to fluctuate wildly. So the supplier decides to adopt AI for its own demand forecasting. It used to order 25,000 pounds of avocados a week. Now its order varies from 5,000 pounds to 50,000 pounds. Your supplier’s source of the fruit, in turn, needs to develop AI, and its orders begin to fluctuate too. And so it goes all the way to the growers who need to make crop-size decisions a year or more in advance.

 

这表明，虽然AI可以用来解决一个人的不确定性，但其效果并不会扩散到整个系统的决策中。根本问题——需求需要与供应相吻合——并没有真正得到解决。就像扔进池塘的一块石头一样，你自己的AI解决方案对系统中的其他决策产生了连锁反应。

What this shows is that while AI can be used to resolve one person’s uncertainty, that effect doesn’t spread to decisions throughout a system. The fundamental problem—that demand needs to be aligned with supply—hasn’t really been solved. Like a stone thrown into a pond, your own AI solution has ripple effects on other decisions in the system.

 

这让我们陷入悖论。AI的价值来自改善决策，改善方式是预测原本可能不确定的因素会发生什么情况。但造成的一个后果是，对其他人而言，你自己所做决定的可靠性降低。将AI引入价值链意味着你在其中的合作伙伴将不得不进行更多的协调，以消减这种不确定性。

That leaves us with something of a paradox. The value of AI comes from improving decisions by predicting what will happen with factors that might otherwise be uncertain. But a consequence is that your own decisions become less reliable for others. Introducing AI into the value chain means that your partners in it will have to coordinate a lot more to absorb that uncertainty.

 

协调各个系统，以让工作与资源保持一致

Coordinating Systems to Align Effort and Resources

 

除预测需求之外，餐厅经理还必须做出其他几个决定——比如，菜单上提供什么菜品。如果AI的连锁反应意味着种植户不能提供足够的鳄梨，那么餐厅就需要更改菜单。它可能不会这样做，除非它知道鳄梨无法供应，这需要不同决策者之间的协调。这种协调有两个方面：

The restaurant manager has to make several other decisions besides predicting demand—for example, what to offer on the menu. If the AI ripple effect means that the grower can’t supply enough avocados, then the restaurant needs to change the menu. It probably won’t do so unless it knows the avocados aren’t available, which requires coordination across decision-makers. That coordination has two aspects:

 

同步工作。让我们看看一个由8名桨手组成的赛艇队的运作。两件事决定了它在比赛中的表现如何：其成员划桨是否整齐一致，以及他们在比赛进程中如何调整划桨速度，以确保队中无人在终点前耗尽体力。坐在船尾的舵手对第二项工作至关重要，但对第一项工作并不重要。这似乎令人意想不到，因为舵手是通过高呼“划桨！划桨！划桨！”来协调桨手，以便保持时间同步。然而，这项任务并不单独需要一个人；其中一名桨手就可以做这事。事实上，这种情况就发生在没有舵手的赛事中。可是，当涉及在比赛中监控策略以及获得单个桨手状态方面的线索时——即，收集信息并汇总信息——舵手至关重要。舵手可以评估是否有必要改变团队的节奏，并相应地调整传达给桨手的信息。舵手之所以存在，是因为团队需要确保以同步的方式进行调整。

Synchronizing the work. Consider the operation of a crew team of eight rowers. Two things determine how it will perform in a race: whether its members are rowing in unison, and how they adjust rowing speed as the race progresses to ensure that no one on the team runs out of energy before the finish. The coxswain, who sits at the back of the boat, is essential for the second but not the first function. That might seem surprising, since the coxswain is coordinating the rowers to keep the same time by calling out, “Stroke! Stroke! Stroke!” But that task doesn’t require a separate person; one of the rowers could do it, and in fact, this occurs in races in which crew boats don’t have coxswains. But when it comes to monitoring strategy in a race and obtaining cues about the status of individual rowers—that is, gathering information and aggregating it—the coxswain is critical. The coxswain can assess the need for changes in the team’s rhythm and adjust the message to rowers accordingly. The coxswain is there because the team needs to ensure that adaptation is made in a synchronized fashion.

 

分配资源。协调的难题还涉及一类问题，保罗·米尔格龙（Paul Milgrom）和约翰·罗伯茨（John Roberts）称之为分配问题——你需要为一项活动分配资源的情形，但你知道只有一定数量的资源会被使用。再多一些就会浪费，再少一些又会不足。请想想救护车的调度。如果一个网络中的所有救护车都收到了一条急救信息，然后各自选择是否回应，通常，你最终要么得不到回应，要么回应者太多。为了确保只有一辆救护车回应，你需要一个中央调度员，无论是人还是软件。调度员会接收某一急救呼叫（即信息），然后分配一辆救护车来响应。在这种情况下，派出“错误的”救护车（一辆距离可能太过遥远或没有合适设备的救护车）相比未派车或派车太多的情形，问题要小得多。

Assigning resources. The coordination challenge also involves a class of problems that Paul Milgrom and John Roberts call assignment problems—situations in which you need to assign resources to an activity but you know that only a certain amount of them will be used. Any more would be wasted; any less would be insufficient. Consider ambulance dispatching. If all the ambulances in a network received an emergency message and then chose individually whether to respond, you would often end up with no responders or with too many. To ensure that only one responds, you need a central dispatcher, whether human or software, that receives calls (that is, information) regarding an emergency and then assigns one ambulance to respond. In this case sending the “wrong” ambulance (one that is perhaps too far away or doesn’t have the right equipment) is far less of a problem than sending none or sending too many.

 

舵手和调度员都是通信系统，可以确保不会出现因缺乏同步性或资源分配不当而可能产生的不良结果。同样地，当AI导致协调问题时，那就可能需要新的通信系统来克服这些问题。企业通过对协调的明智投资才能充分实现AI的承诺。

Both coxswains and dispatchers are communication systems that ensure that the bad outcomes that could arise from a lack of synchronization or poor resource assignments don’t occur. Similarly, when AI causes coordination problems, new communication systems may be required to overcome them. It is through smart investment in coordination that organizations will be able to fully realize the promise of AI.

 

那么，在这种情形下，“智能”是何模样？

So what does “smart” look like in this context?

 

把协调与模块化结合起来

Combining Coordination with Modularity

 

在理想的情况下，一个系统能够完全通过沟通来协调，就像赛艇队舵手和救护车调度员那样。可是，沟通并非总是足够。一家餐馆不可能仅仅通过沟通来实现供应链上的协调，因为它的供应链跨越了数千公里和许多月份。这种投资的成本会出奇地高昂，而且非常耗时。

Ideally, a system would be able to coordinate entirely through communication, as crew coxswains and ambulance dispatchers do. But communication isn’t always enough. A restaurant can’t create alignment along the supply chain through communication alone because its chain spans thousands of kilometers and many months. The investment would be prohibitively expensive and time-consuming.

 

解决之道何在？让我们看一看亚马逊的运作。它在世界各地供应数以百万计的产品。这涉及采购这些产品，将它们储存入库，获取客户的订单，并把产品运送给这些客户。不过，此事还涉及首先帮助客户想明白购买何物——即，向他们提供建议。亚马逊面临的问题与我们餐厅相同。它希望在顾客需要的时候为他们提供想要的东西，但产品不会神奇地出现，因为它们的供应链错综复杂。

What’s the solution? Let’s consider the operations of Amazon. It supplies millions of products all over the world. That involves procuring them, storing them in warehouses, capturing customer orders, and shipping items to those customers. But it also involves helping the customers work out what to purchase in the first place—that is, providing them with recommendations. Amazon faces the same problem our restaurant does. It wants to supply customers with what they want when they want it, but products don’t magically appear, because their supply chains are complex.

 

假设亚马逊基于AI的推荐引擎预测，向客户推荐的最佳产品可能缺货。亚马逊应该怎么做？

Let’s say that Amazon’s AI-based recommendation engine predicts that the best product to suggest to a customer is probably unavailable. What should Amazon do?

 

人们会情不自禁地认为，如果你的某种产品缺货，你就不应该把它推荐给客户。问题是，你怎么知道AI的预测是否正确，客户是否真的需要它？如果你只推荐你有的东西，你就会错过学习和成长的机会。

It’s tempting to think that if you don’t have a product available, you shouldn’t recommend it to a customer. The problem is, how do you know whether the AI’s prediction was correct, and the customer really wanted it? If you recommend only what you have, you miss opportunities to learn and grow.

 

这正是为何亚马逊的推荐包括缺货的、需要更长的时间才能到达客户手中的产品。亚马逊将可能的延迟传达给客户，从这个意义上讲，这些决定是协调的。顾客很可能会选择有货的产品，但偶尔也会不选择。然后，亚马逊了解到它需要付出多大的努力来为缺货的商品保有库存。

That’s precisely why Amazon includes recommendations for products that are out of stock and will take longer to reach its customers. The decisions are coordinated in the sense that Amazon communicates the likely delay to the customers. The customers may well choose products that are available, but occasionally they won’t. Amazon then learns how much effort it needs to make to carry inventory for the out-of-stock items.

 

实现这种平衡需要精心设计。亚马逊有一个模块化的组织，使其能够将更好的AI预测嵌入到推荐中，从而将对组织其他部分的影响降到最低。可是，它所做的库存和订购决策不能完全独立于AI推荐系统，正是因为客户的选择和反应产生了信息，物流部门需要根据这些信息采取行动。

Achieving this balance requires careful design. Amazon has a modular organization that has allowed it to slot better AI predictions into recommendations that minimize the impact on the rest of the organization. But the inventory and ordering decisions it makes cannot be fully independent from the AI recommendation system precisely because customers’ choices and reactions give rise to information that needs to be acted on by the logistics department.

 

AI应用通常需要一个系统，该系统在模块化与协调之间找到一个最佳平衡。模块化让企业某一部分的决策不具有多变性——连锁反应——而AI在其他部分造成了这种多变性。它减少了对可靠性的需求。相比之下，协调则会抵消AI的采用带来的可靠性不足。成功的AI系统能够在可能的情况下进行协调，并在必要时实现模块化。

The adoption of AI will often involve a system that finds an optimal balance of modularity and coordination. Modularity insulates decisions in one part of the organization from the variability—the ripple effects—that AI creates in others. It reduces the need for reliability. Coordination, in contrast, counters the lack of reliability that comes alongside AI adoption. Successful AI systems enable coordination where possible, and modularity where necessary.

 

正如我们所希望的，现在可以清楚地看到，AI预测技术的前景与电力和个人计算的前景类似。像它们一样，AI首先解决了几个当务之急，在孤立的、界限严格的应用中创造了价值。但随着人们与AI的接触，他们会发现创造解决方案或提高效率和生产力的新机会。比如，餐馆最有可能更深入地置身于他们自己的供应链中，也许在他们菜单的菜品上更具灵活性。随着这些机会的实现，它们会创造新的挑战，这些挑战反过来又会提供更多的机会。因此，随着AI在供应链和生态系统中的传播，我们会发现，所有我们认为理所当然的流程和做法正在被改变——不是被技术本身，而是被使用技术之人的创造力所改变。

As we hope will be clear by now, the promise of AI’s prediction technology is similar to that of electricity and personal computing. Like them, AI began by resolving a few immediate problems, creating value in isolated, tightly bounded applications. But as people engage with AI, they will spot new opportunities for creating solutions or improving efficiency and productivity. Restaurants, for example, will most likely become more deeply embedded in their own supply chains and perhaps more flexible in their menu offerings. As these opportunities are realized, they will create new challenges that in turn provide more opportunities. So as AI spreads across supply chains and ecosystems, we will find that all the processes and practices we took for granted are being transformed—not by the technology itself but by the creativity of the people who are using it.

 

阿贾伊·阿格拉沃尔（Ajay Agrawal）

乔舒亚·甘斯（Joshua Gans）

阿维·戈德法布（Avi Goldfarb）| 文  

阿贾伊·阿格拉沃尔是多伦多大学（University of Toronto）创业与创新学杰弗里·泰伯教授（Geoffrey Taber Chair），创造性颠覆实验室创始人。乔舒亚·甘斯是多伦多大学技术创新与创业学杰弗里·S·斯科尔教授（Jeffrey S. Skoll Chair），创造性颠覆实验室首席经济学家。阿维·戈德法布是多伦多大学人工智能与医疗保健学罗特曼教授（Rotman Chair），创造性颠覆实验室首席数据科学家。他们合著有《权力与预测：颠覆性的人工智能经济学》（Power and Prediction: The Disruptive Economics of Artificial Intelligence）（哈佛商业评论出版社，2022年）一书，本文改编自该著作。

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