Innovation and Design in the Age of Artificial Intelligence
人工智能时代的创新与设计
首次发表日期：2020 年 3 月 19 日 https://doi.org/10.1111/jpim.12523 引用次数：238
Abstract 摘要
At the heart of any innovation process lies a fundamental practice: the way people create ideas and solve problems. This “decision making” side of innovation is what scholars and practitioners refer to as “design.” Decisions in innovation processes have so far been taken by humans. What happens when they can be substituted by machines? Artificial Intelligence (AI) brings data and algorithms to the core of the innovation processes. What are the implications of this diffusion of AI for our understanding of design and innovation? Is AI just another digital technology that, akin to many others, will not significantly question what we know about design? Or will it create transformations in design that current theoretical frameworks cannot capture?
任何创新过程的核心都是一项基本实践：人们创造想法和解决问题的方式。创新的“决策”方面是学者和实践者所称之为“设计”的内容。到目前为止，创新过程中的决策一直由人类来做出。当它们可以被机器替代时会发生什么？人工智能（AI）将数据和算法带入创新过程的核心。AI 的普及对我们对设计和创新的理解有什么影响？AI 只是另一种数字技术，类似于许多其他技术，不会显著质疑我们对设计的认识吗？还是它会在设计方面产生变革，当前的理论框架无法捕捉到？
This paper proposes a framework for understanding the design and innovation in the age of AI. We discuss the implications for design and innovation theory. Specifically, we observe that, as creative problem-solving is significantly conducted by algorithms, human design increasingly becomes an activity of sensemaking, that is, understanding which problems should or could be addressed. This shift in focus calls for the new theories and brings design closer to leadership, which is, inherently, an activity of sensemaking.
本文提出了一个框架，用于理解人工智能时代的设计和创新。我们讨论了对设计和创新理论的影响。具体来说，我们观察到，随着创造性问题解决工作显著由算法完成，人类设计越来越成为一种感知活动，即理解应该或可以解决哪些问题。这种关注焦点的转变需要新的理论，并将设计与领导力更加紧密地联系在一起，因为领导力本质上是一种感知活动。
Our insights are derived from and illustrated with two cases at the frontier of AI—Netflix and Airbnb (complemented with analyses of Microsoft and Tesla)—which point to two directions for the evolution of design and innovation in firms. First, AI enables an organization to overcome many past limitations of human-intensive design processes, by improving the scalability of the process, broadening its scope across traditional boundaries, and enhancing its ability to learn and adapt on the fly. Second, and maybe more surprising, while removing these limitations, AI also appears to deeply enact several popular design principles. AI thus reinforces the principles of Design Thinking, namely: being people-centered, abductive, and iterative. In fact, AI enables the creation of solutions that are more highly user centered than human-based approaches (i.e., to an extreme level of granularity, designed for every single person); that are potentially more creative; and that are continuously updated through learning iterations across the entire product life cycle.
我们的见解源自并以 AI 前沿的两个案例为例—Netflix 和 Airbnb（辅以对 Microsoft 和 Tesla 的分析）—这指向了公司设计和创新演变的两个方向。首先，AI 使组织能够克服许多人力密集型设计过程的过去局限，通过提高流程的可扩展性，拓宽其跨越传统边界的范围，并增强其在实时学习和适应方面的能力。其次，也许更令人惊讶的是，虽然消除了这些限制，AI 似乎也深刻地实施了几个流行的设计原则。因此，AI 强化了设计思维的原则，即：以人为中心、诱导性和迭代性。事实上，AI 使得创造的解决方案比基于人类方法更加以用户为中心（即，达到极端细粒度水平，为每个人设计）；可能更具创造性；并通过整个产品生命周期的学习迭代持续更新。
In sum, while AI does not undermine the basic principles of design, it profoundly changes the practice of design. Problem-solving tasks, traditionally carried out by designers, are now automated into learning loops that operate without limitations of volume and speed. The algorithms embedded in these loops think in a radically different way than a designer who handles the complex problems holistically with a systemic perspective. Algorithms instead handle complexity through very simple tasks, which are iterated continuously. This paper discusses the implications of these insights for design and innovation management scholars and practitioners.
总的来说，虽然人工智能并不会削弱设计的基本原则，但它深刻地改变了设计实践。传统上由设计师完成的问题解决任务现在被自动化为学习循环，这些循环在体积和速度上没有限制。嵌入在这些循环中的算法以一种与以系统视角处理复杂问题的设计师截然不同的方式进行思考。算法通过非常简单的任务处理复杂性，这些任务不断迭代。本文讨论了这些见解对设计和创新管理学者和实践者的影响。
Introduction
The adoption of artificial intelligence (AI) has received enormous attention across virtually every industrial setting, from healthcare delivery to automobile manufacturing. In combination with the ubiquity of digital sensors, networks, and software-based automation, AI is transforming our economy and defining a new industrialization age. From Alibaba to Airbnb, this “Age of AI” is defined by the emergence of a new kind of firm, based on a digital operating model, creating unprecedented opportunities and challenges (Iansiti and Lakhani, 2020a, 2020b, 2020c).
As firms evolve to embrace an increasingly AI-centric operating model, they are digitizing a growing number of important business processes, removing human labor and management from the execution of many critical operating activities. For example, unlike processes in traditional firms, no worker sets the price on an Amazon product or qualifies a business for a loan at Ant Financial. While humans develop the algorithms and write the software code, the actual real-time creation of the solution is automated and enabled entirely by digital technology.
随着企业不断发展，拥抱越来越以人工智能为中心的运营模式，它们正在数字化越来越多重要的业务流程，从执行许多关键运营活动中剔除人力劳动和管理。例如，与传统企业不同，没有工人设定亚马逊产品的价格，也没有工人在蚂蚁金服为企业贷款资格进行评估。虽然人类开发算法并编写软件代码，但解决方案的实际实时创建是通过数字技术完全自动化和实现的。
As the economy continues to transform, innovation processes are also changing rapidly, making use of sensors, digital networks, and algorithms. Whether the product consists entirely of software, as with an iPhone app, or whether it is a more traditional hardware-centric artifact, as in a Tesla automobile, modern products are increasingly connected to the organization that created them, providing a continuous flow of data that details many aspects of the user experience. In addition, the software embedded in the products themselves enables information flowing the other way, from the firm to the user, enabling a specific solution for a specific person, constantly improving the experience in real time. These instant two-way interactions characterize an increasing range of goods and services, from Netflix video streaming to a Tesla Model 3. Effectively, these innovative solutions evolve in real time as the user experiences them.
随着经济持续转型，创新过程也在迅速变化，利用传感器、数字网络和算法。无论产品是否完全由软件组成，如 iPhone 应用程序，还是更传统的以硬件为中心的制品，如特斯拉汽车，现代产品越来越与创造它们的组织相连接，提供了大量用户体验的详细数据流。此外，嵌入在产品中的软件使信息能够双向流动，从公司到用户，为特定人提供特定解决方案，不断实时改进体验。这些即时的双向互动特征化了越来越多的商品和服务，从 Netflix 视频流到特斯拉 Model 3。实际上，这些创新解决方案随着用户体验而实时演变。
It is important to note that to bring about the kinds of dramatic changes we are describing, we do not need a particularly advanced notion of AI. AI need not be indistinguishable from the human behavior, or capable of simulating human reasoning—what is sometimes defined as “strong AI” in the field of computer science. We do not need a perfect human replica to prioritize content on a social network, optimize the recipe for a perfect cappuccino, analyze customer behavior patterns, understand the implications of design trade-offs, or personalize a product. We need only a computer system to perform simple tasks that were traditionally performed by human beings, such as recognizing images, or processing natural language. This is what traditionally defines “weak AI” (Iansiti and Lakhani, 2020a). Imperfect, weak AI, typically powered by the exploding field of machine learning, is already enough to create significant change when replicated at scale.
需要注意的是，要实现我们描述的这种戏剧性变革，并不需要特别先进的人工智能概念。人工智能不必与人类行为无法区分，或者能够模拟人类推理——这在计算机科学领域有时被定义为“强人工智能”。我们不需要一个完美的人类复制品来优先考虑社交网络上的内容，优化完美卡布奇诺的配方，分析客户行为模式，理解设计权衡的影响，或者个性化产品。我们只需要一个计算机系统来执行传统由人类执行的简单任务，比如识别图像或处理自然语言。这通常被定义为“弱人工智能”（Iansiti 和 Lakhani，2020a）。不完美的、弱人工智能，通常由不断发展的机器学习领域支持，已经足以在规模上复制时产生重大变革。
AI, as defined above, profoundly transforms the context where innovation takes place. Why? AI is inherently a decision-making technology: it offers opportunities to automate many tasks relating to learning and devising solutions. When AI is applied to the context of innovation, it may therefore transform how decisions in innovation are made, especially in relation to how novel solutions (whether a good, a service, or a process) are created and tested. This decision-making practice at the heart of innovation is what scholars refer to as design (Liedtka, 2015). Indeed, ultimately, to design is to “devise courses of action aimed at changing existing situations into preferred ones” (Simon, 1982, p. 129). Investigating how AI affects the innovation processes therefore requires an exploration of how it affects the design.
人工智能，如上所定义，深刻地改变了创新发生的背景。为什么？人工智能本质上是一种决策技术：它提供了自动化许多与学习和制定解决方案相关的任务的机会。当人工智能应用于创新的背景时，它可能改变创新决策的方式，特别是与如何创造和测试新颖解决方案（无论是产品、服务还是流程）有关的方式。创新核心的这种决策实践是学者们所称的设计（Liedtka，2015）。事实上，最终，设计就是“制定旨在将现有情况改变为期望情况的行动方案”（Simon，1982，第 129 页）。因此，调查人工智能如何影响创新过程需要探讨它如何影响设计。
在本文中，我们通过探讨先锋组织（如 Netflix 和 Airbnb）的战略，探讨人工智能对设计和创新管理的影响。我们的分析涉及三组问题：
- Questions about AI and the practice of design: To what extent is AI likely to change the way design is practiced, that is, which decisions are made and how? Is the transformation of the context induced by AI changing the design process and the objects of the design actions? For example, which decisions can be automated and which ones cannot?
关于人工智能和设计实践的问题：人工智能在多大程度上可能改变设计实践的方式，即哪些决策被做出以及如何做出？人工智能引发的背景转变是否改变了设计过程和设计行为的对象？例如，哪些决策可以自动化，哪些不能？ - Questions about AI and the principles of design: If AI induces significant changes in design practice, are these changes putting the fundamentals of design in question? Is, for example, user centeredness, questioned? Is design practice, in the age of AI, informed by significantly different principles?
关于人工智能和设计原则的问题：如果人工智能在设计实践中引发了重大变化，这些变化是否会对设计的基本原则提出质疑？例如，用户中心性是否受到质疑？在人工智能时代，设计实践是否受到明显不同的原则的指导？ - Finally, questions about AI and the theory of design and innovation: What are the implications for the theoretical frameworks that we use to interpret design and innovation? Does the widespread adoption of AI call for new research questions and for a new understanding of how design drives innovation in organizations?
最后，关于人工智能、设计和创新理论的问题：我们用来解释设计和创新的理论框架会有什么影响？人工智能的广泛应用是否需要提出新的研究问题，并对组织中设计如何推动创新的理解提出新的要求？
The article is structured as follows. We start by introducing the principles of design, with a special focus on recent developments in Design Thinking. Then, we introduce a framework that enables to compare the traditional human-intensive design practice with design practice in the age of AI. The framework is then illustrated with the cases of Netflix and Airbnb. Next, we discuss the cases (with support of additional information from the experiences in Tesla and Microsoft) to analyze the extent to which the design principles and practice are affected by AI. We then conclude with an analysis of implications for design and innovation theories and scholars.
本文结构如下。我们首先介绍设计原则，特别关注最近设计思维的发展。然后，我们介绍一个框架，可以比较传统的人力密集型设计实践与人工智能时代的设计实践。接着，我们以 Netflix 和 Airbnb 的案例来阐述这一框架。接下来，我们讨论这些案例（并结合来自特斯拉和微软的额外信息）来分析设计原则和实践受人工智能影响的程度。最后，我们总结对设计和创新理论以及学者的影响分析。
Design and its Operating Context
设计及其运作背景
To investigate whether and to what extent AI transforms our understanding of design, we frame our discussion according to two levels of analysis (Orlikowski, 2010): practice and principles. Design practice refers to the phenomenology of design in a specific context: its process (“how” design decisions are made; through which phases, methods, tools, or collaborative practices) and the object of design (which design decisions are made; which novel solution it creates, whether a good, service, or process). Design principles, instead, refer to the perspective and philosophy that inform the act of designing, and that constitute an ontology of what design is. The distinction between these two levels of analysis enables us to better discern how AI might affect the design. Is AI changing the way we design, or is it acting at a deeper level by reframing the basic principles that inspire the act of designing? To answer this question, we start by introducing the principles of design, as they emerge from the current discussion on design and innovation theory. We then illustrate how these principles have been instantiated into design practice before the advent of AI. Finally, we introduce a framework to analyze how these principles are enacted in the context of AI.
为了调查人工智能是否以及在多大程度上改变了我们对设计的理解，我们根据两个分析层次（Orlikowski，2010）来构建我们的讨论：实践和原则。设计实践指的是特定背景下设计的现象学：其过程（“如何”做设计决策；通过哪些阶段、方法、工具或协作实践）和设计对象（做出哪些设计决策；它创造了哪种新颖解决方案，无论是产品、服务还是流程）。相反，设计原则指的是指导设计行为的观点和哲学，构成了设计本质的本体论。这两个分析层次之间的区别使我们能更好地辨别人工智能可能如何影响设计。人工智能改变了我们的设计方式，还是在更深层次上通过重新构建激发设计行为的基本原则？为了回答这个问题，我们首先介绍设计原则，这些原则是从当前关于设计和创新理论的讨论中产生的。然后我们说明在人工智能出现之前这些原则是如何体现在设计实践中的。 最后，我们介绍了一个框架，用于分析这些原则在人工智能背景下是如何实施的。
The Principles of Design
设计原则
设计实践的原则是什么？关于设计本体论的科学辩论已经在设计理论领域得到发展，有许多贡献（例如参见 Galle，2002 年；Love，2000 年；Margolin，1989 年；Margolin 和 Buchanan，1996 年）。鉴于我们关注组织背景下的设计实践，我们采取了更具体的视角：设计思维。这一视角利用设计理论文献的内容，并将其调整以解释设计驱动创新如何在商业环境中发生。尽管“设计思维”这个术语存在一些模糊性，但管理学者努力梳理其原则的努力趋于三个基本因素（参见 Calabretta 和 Kleinsmann，2017 年；Dell’Era，Cautela，Magistretti，Verganti 和 Zurlo，2020 年；Micheli，Wilner，Bhatti，Mura 和 Beverland，2019 年；Seidel 和 Fixson，2013 年；尤其是 Liedtka，2015 年，重新组合设计思维原则与设计理论原则）。
- People-centered: innovation, when driven by design, is inspired by empathy with users. Rather than being driven by the advancements of technology and by what is possible, design-driven innovation stems from understanding a problem from the user perspective, and from making predictions about what could be meaningful to her. For example, we can recognize this principle in the practice of ethnographic research.
以人为本：创新在设计驱动下，受用户共情启发。设计驱动的创新并非受技术进步和可能性驱动，而是源自对问题从用户角度的理解，以及对她可能感到有意义的事物进行预测。例如，我们可以在民族志研究实践中看到这一原则。 - Abductive: design adopts a creative approach to solve problems, which sets it apart from other problem-solving practices in management, as clarified by Boland and Collopy (2004): “We portray the manager as facing a set of alternatives from which a choice must be made. This decision attitude assumes it is easy to come up with alternatives to consider, but difficult to choose among them. The design attitude toward problem-solving, in contrast, assumes that it is difficult to design a good alternative, but once you have developed a truly good one, the decision about which alternative to select is trivial” (p. ix). Design therefore implies to imagine the new rather than finding a solution within a set; as Simon (1982) states, design is “concerned not with the necessary but with the contingent—not with how things are but with how they might be” (p. xii). From the perspective of logical inference, this implies that design solves problems through abductions: rather than leveraging solely deductive reasoning (how things are) and inductive reasoning (how things likely are), design creates through abductive reasoning (by making hypotheses about how things might be). This is why design is often associated with creativity and ideation rather than analysis. For example, we can recognize this principle in the practice of brainstorming.
Abductive：设计采用创造性方法解决问题，这使其与管理中的其他问题解决实践有所区别，正如 Boland 和 Collopy（2004）所澄清的：“我们描绘经理面临一系列选择，必须做出选择。这种决策态度假定很容易提出要考虑的选择，但很难在它们之间做出选择。相比之下，设计态度对问题解决持一种不同的看法，它认为设计一个好的选择很困难，但一旦你开发出一个真正好的选择，那么选择哪个选择就是微不足道的”（第 ix 页）。因此，设计意味着想象新事物而不是在一组事物中找到解决方案；正如 Simon（1982）所述，设计“关注的不是必然的，而是偶然的——不是事物的现状，而是事物可能的状态”（第 xii 页）。从逻辑推理的角度来看，这意味着设计通过假设解决问题：设计不仅仅依靠演绎推理（事物的现状）和归纳推理（事物可能的状态），而是通过假设推理（通过假设事物可能的状态）来创造。 这就是为什么设计通常与创造力和构思联系在一起，而不是分析。例如，我们可以在集体讨论的实践中看到这一原则。 - Iterative: abductions are continuously adapted and improved through fast testing cycles. The prototypes that are built in these cycles act as a “playground” for conversation and learning (Schrage, 1999). They engage the team and users in iterations in which solutions are tested and refined, until a satisfactory result is achieved. For example, we can recognize this principle in the practice of building rudimentary mockups early in the design process.
迭代：通过快速测试周期，绑架不断地被调整和改进。在这些周期中构建的原型充当了对话和学习的“游乐场”（Schrage，1999）。它们让团队和用户参与迭代过程，测试和完善解决方案，直到达到满意的结果。例如，在设计过程的早期阶段建立基本的模型就体现了这一原则。
Design in the Context of Traditional Operating Models
传统运营模式背景下的设计
The way design principles are enacted into practice depends of course on the operating context in which design takes place. Most design practices we know today rely on human decision-making. Because of this labor-intensive design context, it is not practically possible nor economically convenient to design a different solution for each individual user. Products (goods and services) are therefore designed for segments of users (see the phase “design” in Figure 1). Then, products are manufactured at scale, through complex production architectures which include possibilities for customization (“make”). Finally, they are delivered for “use” (see also Clark and Fujimoto, 1991).
设计原则如何实施取决于设计发生的运作背景。我们今天所了解的大多数设计实践依赖于人类决策。由于这种劳动密集型的设计背景，为每个个体用户设计不同的解决方案在实践上既不现实也不经济上划算。因此，产品（商品和服务）是为用户群设计的（见图 1 中的“设计”阶段）。然后，产品通过复杂的生产架构进行规模化生产，其中包括定制的可能性（“制造”）。最后，它们被交付供“使用”（另见 Clark 和 Fujimoto，1991 年）。
传统人力密集型运营模式背景下的设计实践
After a product is released, the context evolves. For example, the market changes, or new technological opportunities emerge. In addition, organizations can learn new insights from how customers actually use the existing product. Yet, as the operating model entails significant effort and investment to redesign a product, innovation is postponed until the marginal value of a new product supersedes the cost of its design. At this point, a new design cycle starts.
产品发布后，环境会发生变化。例如，市场会发生变化，或者会出现新的技术机遇。此外，组织可以从客户实际使用现有产品的方式中获得新的见解。然而，由于重新设计产品需要大量的努力和投资，创新往往会推迟，直到新产品的边际价值超过设计成本为止。在这一点上，一个新的设计周期开始。
A structure of this kind therefore implies a significant separation in time between two consequent design initiatives. During product use, learning cycles are frozen and, consequently, solutions become rapidly “old.” New learning and ideas may only be incorporated in future solutions released in lumps, episodically and for customer segments.
因此，这种结构意味着两个相继设计倡议之间存在重大的时间分隔。在产品使用过程中，学习周期被冻结，因此解决方案迅速变得“陈旧”。新的学习和想法只能在未来以一揽子、间歇性地发布给客户群体的解决方案中得以融入。
Design in the Context of AI Factories
AI 工厂背景下的设计
As discussed above, traditional design activities are human intensive. AI offers the opportunity to revolutionize this scenario. To understand how, we have explored cases of organizations that are pioneering the use of AI in design, namely Netflix and Airbnb. The observation of these organizations allowed us to develop an original framework (Figure 2) that describes how design practice can be articulated in the age of AI. Let’s briefly describe the main elements of the framework, before delving into the description of these illustrative cases in the next section.
正如上文所讨论的，传统的设计活动需要大量人力投入。人工智能为改变这种情况提供了机会。为了了解这一点，我们已经探讨了一些在设计中开创人工智能应用的组织案例，即 Netflix 和 Airbnb。观察这些组织使我们能够制定一个描述设计实践如何在人工智能时代得以实现的原创框架（图 2）。在深入探讨下一节中这些案例的描述之前，让我们简要描述一下框架的主要要素。
人工智能工厂背景下的设计实践
To design implies making a number of decisions. A few of them are highly sophisticated and conceptual. But most decisions, especially during development, are narrow and ask for specific problem-solving skills. Examples of these detailed decisions are the choice of the functional shape of an object, the details of a product interface, or which information to display on a screen. There are plenty of detailed problems to be addressed during design. AI offers the intelligence to solve them.
设计意味着做出许多决策。其中一些是非常复杂和概念性的。但大多数决策，特别是在开发过程中，是狭窄的，需要具体的解决问题的技能。这些详细决策的例子包括选择物体的功能形状、产品界面的细节，或者在屏幕上显示哪些信息。在设计过程中有许多详细的问题需要解决。人工智能提供了解决这些问题的智能。
In the context of AI factories (i.e., organizations that make intensive use of AI in their operating models—see Iansiti and Lakhani, 2020a, 2020b) a specific solution, that is, what an individual user actually interacts with, is designed by an AI engine in what we call “problem-solving loops.” Loops collect real time data (insights) from customer interactions or from the ecosystem in which the firm lies. These data can immediately inform the AI embedded in the product, which has problem-solving capabilities (from recognizing objects to processing natural language, from making predictions to drawing conclusions). If properly conceived, an algorithm can autonomously generate a new specific solution for that precise user, with no human effort involved. Even more, as new data are continuously collected, and the AI engine embeds learning capabilities, the problem-solving loops improve their predictions about user needs and behaviors and therefore design better solutions over time.
在 AI 工厂的背景下（即，在其运营模式中大量使用人工智能的组织——参见 Iansiti 和 Lakhani，2020a，2020b），一个特定的解决方案，也就是一个个体用户实际互动的内容，是由一个 AI 引擎设计的，我们称之为“问题解决循环”。这些循环从客户互动或公司所处生态系统中收集实时数据（见解）。这些数据可以立即通知嵌入产品中的 AI，该 AI 具有解决问题的能力（从识别对象到处理自然语言，从做出预测到得出结论）。如果算法设计得当，它可以自主为该特定用户生成一个新的具体解决方案，无需人力参与。更重要的是，随着不断收集新数据和 AI 引擎嵌入学习能力，问题解决循环改进了对用户需求和行为的预测，因此随着时间的推移设计出更好的解决方案。
In an AI-powered system, many development decisions are therefore made through problem-solving loops that are autonomous and human capital-free. The work of humans is to conceive the foundations for a new offering and design these problem-solving loops (see the phase “design” in Figure 2). The loops will then replace people with technology in the development of a specific solution: they are easy to scale without redesign, and can provide a variety of solutions without large additional investments in R&D.
在一个由人工智能驱动的系统中，许多开发决策都是通过自主的、无需人力资本的问题解决循环来进行的。人类的工作是构思新产品的基础，并设计这些问题解决循环（请参见图 2 中的“设计”阶段）。这些循环将用技术取代人类，在特定解决方案的开发中：它们易于扩展而无需重新设计，并且可以在不需要大量额外的研发投资的情况下提供各种解决方案。
AI-Empowered Design in Practice
AI-强化设计实践
To see how the framework of Figure 2 works in practice, we examined the cases of Netflix and Airbnb. We selected these two cases, as, being at the frontier of AI applications, they offer a glimpse into the future of design in the context of an AI-centric firm.
为了看到图 2 的框架在实践中是如何运作的，我们研究了 Netflix 和 Airbnb 的案例。我们选择了这两个案例，因为它们作为人工智能应用的前沿，为我们提供了一个窥视在以人工智能为中心的公司背景下设计未来的机会。
Netflix and the Data-Driven, Design Thinking Machine
Netflix 和数据驱动的设计思维机器
Netflix has completely transformed the media landscape by harnessing the power of big data and AI. The core of Netflix is its data and AI-centric operating model. It is powered by software infrastructure that gathers data and trains and executes algorithms that drive virtually every aspect of the business, from personalizing the user experience to picking winning movie concepts for its next productions. In this section, we detail the Netflix approach to design, by digging into some of the machine learning techniques that Netflix has deployed into its problem-solving loops.
Netflix 通过利用大数据和人工智能彻底改变了媒体格局。Netflix 的核心是其以数据和人工智能为中心的运营模式。它由软件基础设施驱动，收集数据并训练和执行算法，推动业务的几乎每个方面，从个性化用户体验到为其下一部作品选择成功的电影概念。在本节中，我们详细介绍了 Netflix 的设计方法，深入探讨了 Netflix 已经部署到其问题解决循环中的一些机器学习技术。
Netflix started to leverage AI at least as early as 2010, to fuel its recommendation engine. In 2014, Netflix expanded its approach to invest extensively in understanding user behavior and develop a personalized streaming experience for each user. The application screens that a user sees today are “designed in real time” by a machine. Many boundaries and parameters are specified by human designers at the outset of the process. But the decisions about which movies to show, how to display them, which pictures to represent them with, and many other design decisions are done by algorithms embedded in the AI problem-solving loops. Let’s dig into these algorithms, which effectively resemble different aspects of a process of design.
Netflix 至少从 2010 年开始利用人工智能来推动其推荐引擎。2014 年，Netflix 扩大了其方法，大力投资于理解用户行为，并为每个用户开发个性化的流媒体体验。用户今天看到的应用程序屏幕是由机器“实时设计”的。在流程开始阶段，许多边界和参数由人类设计师指定。但关于展示哪些电影、如何展示它们、用哪些图片来代表它们以及许多其他设计决策都是由嵌入在人工智能问题解决循环中的算法完成的。让我们深入研究这些算法，它们有效地类似于设计过程的不同方面。
The basic problems most AI systems try to solve to shape a design experience relate to predicting an outcome. The tool for making that prediction is an algorithm—the set of rules a machine follows to solve a particular problem. AI can incorporate many types of algorithms (Domingos, 2012). Some of them have a built-in process for updating and improving, most often based on “Markov decision processes,” which seek to model a sequence of actions, each shaped by a policy, and followed by a reward. One example would be the Netflix algorithms that dynamically update its user interface, based on the actual behavior of the user, as indicated by her clicks (while the policy decides what is displayed, the click is the “reward”).
大多数人工智能系统试图解决的基本问题是塑造设计体验，与预测结果相关。用于进行预测的工具是算法——机器用来解决特定问题的一组规则。人工智能可以整合许多类型的算法（Domingos，2012）。其中一些算法具有内置的更新和改进过程，通常基于“马尔可夫决策过程”，旨在建模一系列由策略塑造的动作，然后是奖励。一个例子是 Netflix 算法，根据用户的实际行为（由其点击指示），动态更新其用户界面，而策略决定显示什么，点击则是“奖励”。
While applications have exploded over the last decade, the foundations of algorithm design have been around for a while. The conceptual and mathematical development of classical statistical models like linear regression, clustering, or Markov chains dates back more than a hundred years. Today’s neural networks were initially developed in the 1960s and are only now being put to use at a scale with production-ready outputs. The vast majority of production-ready and operational AI systems at Netflix use one of three general approaches to developing accurate predictions using statistical models, also known as machine learning. These are supervised learning, unsupervised learning, and reinforcement learning.
在过去的十年里，应用程序数量激增，但算法设计的基础已存在一段时间。经典统计模型的概念和数学发展，如线性回归、聚类或马尔可夫链，可以追溯到一百多年前。今天的神经网络最初是在 1960 年代开发的，现在才开始以生产就绪的输出规模投入使用。Netflix 的绝大多数生产就绪和运营 AI 系统使用三种通用方法之一来开发准确的预测，这些方法也被称为机器学习。它们是监督学习、无监督学习和强化学习。
Supervised learning 监督学习
The basic goal of supervised machine-learning algorithms is to come as close as possible to an expert (or an accepted source of truth) in predicting an outcome. The classic case is analyzing a picture and predicting whether the subject is a cat or a dog. In this case, the expert would be any human being with good eyesight who could label photos as cat or dog. The first step in supervised learning is to create (or acquire) a labeled data set. The data are then split between training and validation. As we compare the algorithmic model’s prediction of the outcome to the validated labeled outcomes, we can determine if we are satisfied with the error between prediction and expert. If we are not satisfied, we can go back and choose a different statistical approach, get more data, or work on identifying other features that may be helpful in making a more accurate prediction. Netflix uses supervised learning in a variety of scenarios. For recommendations, Netflix has used labeled data sets made up of actions and results (e.g., movies chosen and liked) by people who are deemed by the algorithm to be similar to a given user. A large data set of user choices calibrated by characteristics of the user and of the decision context can lead to effective recommendations.
监督式机器学习算法的基本目标是尽可能接近专家（或被接受的真实来源）来预测结果。经典案例是分析一张图片并预测主题是猫还是狗。在这种情况下，专家可以是任何具有良好视力的人，可以将照片标记为猫或狗。监督学习的第一步是创建（或获取）一个带标签的数据集。然后将数据分为训练和验证集。当我们将算法模型对结果的预测与验证标记的结果进行比较时，我们可以确定我们对预测和专家之间的误差是否满意。如果我们不满意，我们可以返回并选择不同的统计方法，获取更多数据，或者努力识别可能有助于进行更准确预测的其他特征。Netflix 在各种情景中使用监督学习。对于推荐，Netflix 使用由被算法认为与给定用户相似的人的行为和结果（例如，选择并喜欢的电影）组成的带标签数据集。 用户选择的大数据集，根据用户和决策背景的特征进行校准，可以产生有效的推荐。
Note that supervised learning resembles elements of human design, as instantiated earlier in the first principle of design (people centered). Just as human designers immerse themselves in the context of use and observe all possible aspects of the user experience, the algorithms are trained by a relevant stream of user data, with significant information on the context of use (e.g., the type of device, time and place of action, and so on). The richer the stream of data, the more the problem-solving loops are user centered.
请注意，监督学习类似于人类设计的元素，正如设计的第一原则（以人为中心）中所体现的那样。就像人类设计师沉浸在使用环境中并观察用户体验的所有可能方面一样，算法通过相关的用户数据进行训练，其中包含有关使用环境的重要信息（例如设备类型、行动的时间和地点等）。数据流越丰富，问题解决循环就越以用户为中心。
Unsupervised learning 无监督学习
Unlike supervised learning models, which train a system to recognize known outcomes, the primary application of unsupervised learning algorithms is to discover insights in data with few preconceptions or assumptions. Whereas in supervised learning the data inputs are labeled with a given outcome, unsupervised learning algorithms aim to find “natural” groupings in the data, without labels, and uncover structure that may not be obvious to the observer. In our example of photos of cats and dogs, an unsupervised learning algorithm might find several types of groupings. Depending on how the clusters are structured, these could end up separating cats and dogs, or indoor and outdoor photographs, or pictures taken during day or night, or virtually anything else. In these cases, one does not know exactly what to look for, but is searching for related groups. Netflix uses unsupervised learning to discover related groups of customers or to create different versions of the user interface that match different usage patterns. Even more advanced, Netflix uses data and AI algorithms to predict which content to create in the first place. The first application of predictive analytics was back in 2013, to evaluate the potential of House of Cards, in collaboration with Media Rights Capital. The new series was a hit and Netflix continued to develop content in response to detailed predictive analytics on market and user behavior. Cindy Holland, vice president of original content, noted in an interview: “We have projection models that help us understand, for a given idea or area, how large an audience size might be, given certain attributes about it. We have a construct for genres that basically gives us areas where we have a bunch of programs and others that are areas of opportunity” (Spangler, 2018).
与监督学习模型不同，监督学习模型训练系统识别已知结果，无监督学习算法的主要应用是在数据中发现洞见，而不带有太多先入之见或假设。在监督学习中，数据输入带有给定结果标签，而无监督学习算法旨在在数据中找到“自然”的分组，不带标签，并揭示可能对观察者不明显的结构。在我们猫和狗照片的例子中，无监督学习算法可能会找到几种类型的分组。根据簇的结构如何构建，这些簇可能最终分离猫和狗，室内和室外照片，白天或夜晚拍摄的照片，或几乎任何其他内容。在这些情况下，人们不知道确切要寻找什么，但是在寻找相关的群组。Netflix 使用无监督学习来发现相关的客户群组或创建与不同使用模式相匹配的用户界面的不同版本。更进一步，Netflix 使用数据和人工智能算法来预测首先创建哪些内容。 预测分析的首次应用可以追溯到 2013 年，当时与 Media Rights Capital 合作评估《纸牌屋》的潜力。这部新系列大获成功，Netflix 继续根据市场和用户行为的详细预测分析开发内容。原创内容副总裁辛迪·荷兰在一次采访中指出：“我们有投影模型帮助我们了解，对于某个想法或领域，考虑到某些属性，可能会有多大的观众规模。我们有一个关于流派的构想，基本上为我们提供了一些我们拥有大量节目的领域，以及其他一些是机会领域”（Spangler, 2018）。
Note that unsupervised learning is a relatively unstructured design process, where the patterns that emerge at the end do so based strictly on the observations (the data) and are not set up at the outset of the process. Although at its core, the algorithm simulates induction, when perpetuated on an extremely large quantity of data unsupervised learning provides insights and hypotheses that mirror the abductions of humans, or the dynamics of ideation and brainstorming. Hence, unsupervised learning also embeds the basic perspectives of design thinking into the problem-solving loops of the AI factory.
请注意，无监督学习是一个相对无结构的设计过程，最终出现的模式严格基于观察（数据），并不是在过程开始时设定的。尽管在其核心，该算法模拟归纳，但在大量数据的持续作用下，无监督学习提供了与人类的推理或构思和头脑风暴动态相类似的见解和假设。因此，无监督学习还将设计思维的基本观点嵌入到人工智能工厂的问题解决循环中。
Reinforcement learning 强化学习
Reinforcement learning makes up the third machine learning paradigm and is the closest in structure to a traditional design process. The applications of reinforcement learning may be even more impactful than those of supervised and unsupervised learning. Rather than starting with data on an expert’s view of the outcome, as in supervised learning, or with a pattern and anomaly recognition system, as in unsupervised learning, reinforcement learning just requires a starting point and a performance function. The system starts somewhere and probes the space around the starting point, using as a guide whether it has improved or worsened the performance of the algorithm. The key trade-off is whether to spend more time exploring the contextual complexity beyond the current understanding or exploiting the model built so far to drive decisions and actions.
强化学习构成第三种机器学习范式，其结构与传统设计过程最为接近。强化学习的应用可能比监督学习和无监督学习更具影响力。与监督学习中从专家对结果的观点出发，或者无监督学习中的模式和异常识别系统不同，强化学习只需要一个起点和一个性能函数。系统从某处开始，探索起点周围的空间，根据算法的性能是否改善或恶化来指导。关键的权衡是是否花更多时间探索当前理解之外的情境复杂性，还是利用迄今为止构建的模型来驱动决策和行动。
Let’s say we take a cable car up a tall mountain and we want to walk our way down. It is a really foggy day and the mountain does not have any clearly marked paths. Since we cannot just see the best way down, we have to walk around and explore different options. There is a natural trade-off between the time we spend walking around and getting a feel for the mountain, and the time we spend actually walking down once we believe we have found the best path. This is the trade-off between exploration and exploitation. The more time we spend exploring, the more we will be convinced we have the best way down, but if we spend too long exploring, we will have less time to exploit the information and actually walk down.
假设我们乘坐缆车上了一座高山，想要步行下山。今天雾气很大，山上没有明确标记的路径。由于我们无法看到最佳下山路线，我们不得不四处走动，探索不同的选择。在我们认为找到最佳路径后，我们实际下山所花费的时间与四处走动、熟悉山势所花费的时间之间存在着自然的权衡。这就是探索和利用之间的权衡。我们花在探索上的时间越多，我们就越会相信找到了最佳下山路线，但如果探索时间过长，我们将有更少的时间来利用信息并实际下山。
This is pretty close to the way the Netflix algorithm actually personalizes movie recommendations and the visuals they are associated with. Through the analysis of user data, Netflix recognized that viewers have enormous diversity in taste and preferences. So, the Netflix team decided that each user should be shown a cover artwork specifically designed for her, drawn from the frames of a movie. The artwork would highlight the aspects of the title that are specifically relevant to that specific user (Chandrashekar, Amat, Basilico, and Jebara, 2017). The problem was complicated, as the Netflix team needed to figure out which movie selection to present, and then, which artwork to combine with that movie to maximize the match between user and recommendation. A single season of an average TV show (about 10 episodes) contains nearly 9 million total frames. Asking creative editors to efficiently sift through that many frames of videos to design an artwork that would capture the audience’s attention would be tedious and ineffective. Designing an artwork for each specific user according to his or her preferences would simply be impossible. But an AI factory, and in particular reinforcement learning loops, can address this design problem effectively. In a way similar to our previous example (finding our way down the mountain), Netflix uses reinforcement learning (and in particular multi-arm bandit algorithms) to spend some time exploring options, and some time exploiting the solution offered by its models. To explore visual options and refine the prediction model, Netflix systematically randomizes the visuals shown to a user. Netflix then exploits the improved model to show a specific user a slew of recommendations with improved visuals. The Netflix service continues to improve dynamically, by automatically cycling between periods of exploration and exploitation, designed to learn the most about the preferences of a complex human being, and maximize the engagement of this specific user over the long haul.
这与 Netflix 算法实际个性化电影推荐和相关视觉的方式非常接近。通过分析用户数据，Netflix 发现观众在品味和偏好上有着巨大的多样性。因此，Netflix 团队决定为每个用户展示专门为她设计的封面艺术品，从电影的帧中绘制而来。这幅艺术品将突出显示与该特定用户相关的标题方面（Chandrashekar, Amat, Basilico 和 Jebara，2017）。问题很复杂，因为 Netflix 团队需要找出要呈现的电影选择，然后确定与该电影结合以最大程度匹配用户和推荐的艺术品。一部普通电视剧的一个季度（约 10 集）包含近 900 万个总帧。要求创意编辑高效地筛选这么多视频帧来设计一个能吸引观众注意力的艺术品将是乏味且无效的。根据每个特定用户的偏好为其设计艺术品简直是不可能的。 但是，人工智能工厂，特别是强化学习循环，可以有效解决这个设计问题。类似于我们之前的例子（在山下找路），Netflix 使用强化学习（特别是多臂赌博算法）来花费一些时间探索选项，一些时间利用其模型提供的解决方案。为了探索视觉选项并完善预测模型，Netflix 系统地对用户显示的视觉进行随机化处理。然后，Netflix 利用改进的模型向特定用户展示一系列带有改进视觉效果的推荐内容。Netflix 服务继续动态改进，通过自动在探索和利用之间循环，旨在更多地了解复杂人类的偏好，并最大程度地提高特定用户的参与度。
Note that with its emphasis on balancing exploitation and exploration, reinforcement learning resembles the process of human design in many facets, and in particular the principle of iterations enunciated earlier. Just as we see with traditional design approaches, opening the funnel with broad exploration can lead to more interesting and innovative decisions, but must be balanced with the increased challenge in making sure the exploitation phase converges on a usable solution.
请注意，强调平衡开发和探索的强化学习在许多方面类似于人类设计过程，特别是之前阐述的迭代原则。正如我们在传统设计方法中看到的那样，通过广泛探索打开漏斗可以导致更有趣和创新的决策，但必须平衡确保开发阶段收敛于可用解决方案的挑战增加。
In its earliest days two decades ago, the Netflix operating model consisted of shipping DVDs. With this mail delivery service, Netflix could only track which titles users viewed, how long they kept a DVD, and how they rated each title, but they could not monitor actual viewing behavior. Although Netflix already recognized the importance of using data to improve customer experience, the heaviness of its assets and operations gave limitations to its capability to design. But when in 2007 Netflix launched its streaming service, the company seized the opportunity to transform its operating model into an AI enabled one. With streaming, Netflix could track the full user experience—when viewers pause, rewind, or skip during a show, for example, or what device they watch it on. This enabled to design several problem-solving loops that bring design principles to its extreme level: a different solution for every single user, designed and delivered on the fly. As Joris Evers, Netflix’s then-chief of communications, says “there are 33 million different versions of Netflix” (Carr, 2013).
在二十年前的早期，Netflix 的运营模式是通过邮寄 DVD。通过这种邮寄服务，Netflix 只能追踪用户观看的影片、他们保留 DVD 的时间以及他们对每部影片的评分，但无法监控实际的观看行为。尽管 Netflix 已经意识到利用数据来改善客户体验的重要性，但其资产和运营的繁重性限制了其设计能力。但是，当 Netflix 在 2007 年推出其流媒体服务时，公司抓住机会将其运营模式转变为一个 AI 启用的模式。通过流媒体，Netflix 可以追踪完整的用户体验——例如，观众在节目中暂停、倒带或跳过的时间，或者他们使用的设备。这使得设计了几个解决问题的循环，将设计原则发挥到极致：为每个用户提供不同的解决方案，即时设计和交付。正如 Netflix 当时的传播主管 Joris Evers 所说：“Netflix 有 3300 万个不同版本”（Carr，2013）。
How Airbnb Reframed the Design Practice in the Hospitality Industry
Airbnb 如何重新构建了酒店行业的设计实践
The case of Netflix offered an opportunity to understand the design practices of an organization, as it transitions from a traditional operating model to an AI enabled one. In particular, it illustrated how problem-solving loops work, the different configurations they can take, and how they enable to create people-centered solutions. How does this new form of design compare to other practices? To this purpose, the hospitality industry offers interesting insights, as its competitive arena contrasts players with traditional and AI-powered operating models.
Netflix 的案例为我们提供了一个了解组织设计实践的机会，因为它从传统运营模式转变为人工智能模式。特别是，它展示了问题解决循环的运作方式，它们可以采取的不同配置，以及如何使人们中心化解决方案。这种新形式的设计与其他实践相比如何？为此，酒店业提供了有趣的见解，因为其竞争领域将传统和人工智能驱动的运营模式进行了对比。
Achieving people centricity in hospitality businesses is extremely complex by nature, because the context is characterized by diversity in many things, including cultures, ages, backgrounds, and travel purposes. As an indication of cultural complexity, consider that the chatbot of Booking.com translates 43 different languages. In the face of this kind of complexity, the traditional operating model of the industry was based on heavy investments in real estate (hotels and their spaces and rooms) and labor-intensive processes, with people that need to be hired, educated, and coordinated. Rooms of asset-heavy companies therefore need to be designed in a more or less standardized fashion, and they remain static for a significant span of time. Similarly, the user experience, and the related back end of the service, is designed and formalized to ensure that quality standards are always respected. This kind of traditional operating model creates significant challenges in delivering an experience that can fit the individual users.
在酒店业实现以人为中心是非常复杂的，因为这个背景充满了多样性，包括文化、年龄、背景和旅行目的。作为文化复杂性的一个指标，可以考虑 Booking.com 的聊天机器人可以翻译 43 种不同的语言。面对这种复杂性，传统的行业运营模式是基于对房地产（酒店及其空间和客房）的大量投资和劳动密集型流程，需要雇佣、培训和协调人员。因此，资产重的公司的客房需要以更或多或少标准化的方式设计，并且在相当长的时间内保持静态。同样，用户体验和相关服务的后端被设计和规范化，以确保始终尊重质量标准。这种传统的运营模式在提供适合个体用户的体验方面存在重大挑战。
To address the challenge, the industry has witnessed in the last decades some of the most frequent and popularized initiatives of consolidated design thinking initiatives. Examples of design thinking applications in the industry included projects conducted by IDEO for Intercontinental Hotels Group. One project, for example, targeted short-stay travelers and aimed to create a convenient experience. Another targeted business travelers and resulted in the design of proper spaces for meeting and working. Yet another project focused on revamping the Holiday Inn Express brand by redesigning everything from how you check in to the look and feel of the room itself (Wilson, 2015). Each of these projects was framed according to a linear design practice, typical of traditional operating models: running ethnography to understand stakeholders’ needs, ideating to formulate effective experiences for the target segment, and relying on rough prototyping for the identified solutions. To this purpose the innovation trend within the sector has been to create innovation labs that are spacious places where design teams can prototype rooms on a 1:1 scale. Sometimes, innovation in room design was taken to the extreme, and it was conducted directly on site: Marriott and Hilton selected real hotels to run beta-tests, where customers could directly get in touch with new ideas. The projects were then frozen into a design (of rooms, processes, or IT applications) that the asset-heavy operator could deliver in a proper consistent way at scale.
为了应对这一挑战，过去几十年来，该行业见证了一些最频繁和最受推崇的设计思维倡议。行业中设计思维应用的例子包括 IDEO 为洲际酒店集团开展的项目。例如，其中一个项目针对短期旅行者，旨在创造便利的体验。另一个项目针对商务旅行者，结果是设计了适合会议和工作的合适空间。另一个项目则专注于改造假日酒店快捷品牌，从办理入住到房间本身的外观和感觉重新设计了一切（Wilson，2015）。这些项目中的每一个都按照线性设计实践框架，这是传统运营模式的典型特征：进行民族志研究以了解利益相关者的需求，构思出有效的目标群体体验，依靠粗略的原型制作确定的解决方案。为此，该行业内的创新趋势是创建宽敞的创新实验室，设计团队可以在 1:1 比例上原型化房间。 有时，客房设计的创新被推向了极致，并直接在现场进行：万豪和希尔顿选择了真实的酒店进行测试，顾客可以直接接触到新的想法。然后，这些项目被冻结为一个设计（客房、流程或 IT 应用程序），这样资产重的运营商可以按照规模以适当一致的方式交付。
In the last part of the 2000s, a significant transformation of the hospitality sector began. New companies with a lighter operating model entered the industry, colliding with traditional asset-heavy business models (Iansiti and Lakhani, 2020c). Airbnb addressed a similar need to Marriot: providing space to guests who needs it. Consigning the onus of managing the operations to the hosts, Airbnb was able to get over traditional growth bottlenecks, such as the necessity to acquire rooms in order to scale. The capability of Airbnb to offer a state-of-the-art solution for each individual user depends on two factors.
在 2000 年代的最后一部分，酒店行业发生了重大转变。新公司采用轻量化运营模式进入该行业，与传统资产密集型商业模式发生碰撞（Iansiti 和 Lakhani，2020c）。Airbnb 解决了与万豪酒店类似的需求：为需要空间的客人提供住宿。将运营管理的责任委托给房东，Airbnb 能够克服传统增长瓶颈，如需要获取房间才能扩展规模。Airbnb 能够为每个用户提供最先进的解决方案的能力取决于两个因素。
First is the breadth of design options. For example, in 2017, Airbnb (which was founded in 2007), spread over more than 190 countries and 80,000 cities, and counted more than three million hosts: three times Marriott International’s rooms, although it was founded in 1927. And, even more, these three million rooms were all different from the other three million designs. Traditional innovation practices in asset-intensive businesses could not create such a variety of physical designs.
首先是设计选择的广度。例如，2017 年，成立于 2007 年的 Airbnb 遍布 190 多个国家和 8 万多个城市，拥有超过 300 万名房东：是万豪国际成立于 1927 年的客房数量的三倍。而且，更重要的是，这 300 万个客房各不相同。资产密集型企业的传统创新实践无法创造出如此多样化的物理设计。
Second, this enormous breadth of options had to be connected to the needs of each individual user. And here is where the AI comes into place. Airbnb collects an enormous amount of data from the interaction with each user. Since 2016, the data science team has developed an extensive logging within the booking flow that allows them to collect insights on what guests see, how they react to different types of interfaces, how much time they spend on a listing page, how long they take to make a booking request, or the exact time in which they decide to go back to search (Dai, 2017). When a customer interacts with Airbnb’s search engine, a new event log (i.e., a list of user activity event data) is sent to a central repository. These logs pile up and detail the customer profile, with her preferences and behavior (Mayfield, Puttaswamy, Jagadish, and Long, 2016). Every time a customer reconnects to the service in search for a new traveling experience, Airbnb replies by instantaneously closing his problem-solving loop: data are extracted from the repository and processed by an AI-engine to create a new solution, personalized not only for the customer herself, but for the specific interaction.
其次，这种广泛的选择必须与每个用户的需求相连接。这就是人工智能发挥作用的地方。Airbnb 从与每个用户的互动中收集了大量数据。自 2016 年以来，数据科学团队在预订流程中开发了广泛的日志记录，使他们能够收集关于客人看到什么、他们如何对不同类型的界面做出反应、他们在列表页面上花费多少时间、他们花多长时间提出预订请求，或者他们决定何时返回搜索的确切时间的见解（Dai，2017）。当客户与 Airbnb 的搜索引擎互动时，一个新的事件日志（即用户活动事件数据列表）被发送到一个中央存储库。这些日志堆积起来，详细描述了客户的个人资料、偏好和行为（Mayfield，Puttaswamy，Jagadish 和 Long，2016）。每当客户重新连接到服务以寻找新的旅行体验时，Airbnb 通过立即关闭他的问题解决循环来回应：数据从存储库中提取，并由人工智能引擎处理，以创建一个新的解决方案，不仅个性化地针对客户本人，还针对具体的互动。
The system works similarly to the case of Netflix. Even more interesting, Airbnb is a “two-sided platform”; that is, it interacts in real time with two categories of users (guests on one side, and hosts on the other). Its AI factory has therefore different problem-solving loops that work in parallel for each specific user type. An example of a problem-solving loop that provides effective solutions to hosts is how Airbnb designs the price of each individual list in an instantaneous and dynamic way: by ticking a box, a host accepts Airbnb’s AI-engine to leverage data streams to automatically refine the price of their accommodation, within a price range. The AI-engine processes a vast amount of information collected from the ecosystem (Chang, 2017), such as the check-in lead time variation as the check-in date approaches, the listing popularity (i.e., how many people search around the host’s area and how many of them click into the host’s page), and the booking history, to understand how customers are reacting to price variations. The outcome is that the price is designed in the moment every time a guest asks for that specific property (Srinivasan, 2018).
该系统的运作方式类似于 Netflix 的情况。更有趣的是，Airbnb 是一个“双边平台”；也就是说，它实时与两类用户（一边是客人，另一边是房东）进行互动。因此，其人工智能工厂具有不同的问题解决循环，针对每种特定用户类型同时运作。一个为房东提供有效解决方案的问题解决循环的例子是，Airbnb 如何以即时动态的方式设计每个单独列表的价格：通过勾选一个框，房东接受 Airbnb 的人工智能引擎利用数据流自动调整他们住宿的价格，范围内。人工智能引擎处理了从生态系统中收集的大量信息（Chang，2017），例如随着入住日期的临近而发生的入住提前时间变化，列表的受欢迎程度（即，有多少人在房东所在地区搜索，有多少人点击进入房东的页面），以及预订历史，以了解客户对价格变化的反应。结果是，每当客人要求特定物业时，价格都是在当时设计的（Srinivasan，2018）。
AI offers the capability to perform different problem-solving loops independently on both sides of the platform, overcoming one of the limitations of traditional operational models: the need to balance the requirements of different stakeholders. This design strategy enabled Airbnb to quickly become a central node of its network. The operating model was immensely scalable and allowed Airbnb to improve the quality of its services for both sides of the platform, thus enriching the communities of users and hosts at the same time. In a way, the case showcases the ultimate people centeredness: it provides solutions targeted to each individual person, across both users and hosts, driving personalization in a dynamic way that improves through constant iteration. It would have been impossible to achieve this with traditional design practices.
人工智能提供了在平台两侧独立执行不同问题解决循环的能力，克服了传统运营模式的局限之一：需要平衡不同利益相关者的需求。这种设计策略使 Airbnb 迅速成为其网络的中心节点。这种运营模式具有极强的可扩展性，使 Airbnb 能够改善其服务质量，同时丰富用户和房东社区。从某种意义上说，这个案例展示了最终以人为中心：它提供针对每个个体的解决方案，跨越用户和房东，以动态方式推动个性化，通过不断迭代不断改进。传统设计实践无法实现这一点。
Discussion 讨论
As AI is diffusing in our society, scholars and practitioners wonder how this will impact our understanding of innovation and design. Our preceding exposition has important implications for design and innovation scholars and practitioners.
随着人工智能在我们的社会中的普及，学者和实践者们想知道这将如何影响我们对创新和设计的理解。我们先前的阐述对设计和创新的学者和实践者具有重要意义。
Artificial Intelligence and Design Practice
人工智能与设计实践
Up until today digital technologies have mainly spread into the operations of organizations, reducing the costs and time of manufacturing and delivering products and services. But the design of those products and services has largely remained a human intensive process. Referring back to Figure 1, even if the “making” was fast and cheap, the “designing” was heavy in time and resources. It was necessarily an intermittent activity, conducted in large projects and for a segment of users.
直到今天，数字技术主要已经传播到组织的运营中，降低了制造和交付产品和服务的成本和时间。但是，这些产品和服务的设计在很大程度上仍然是一个人力密集型的过程。回顾图 1，即使“制造”快速且廉价，但“设计”却需要大量时间和资源。这必然是一种间歇性的活动，是在大型项目中为一部分用户进行的。
AI dramatically changes this scenario: it moves digital automation upstream, from manufacturing to design. Note that automation could be simply limited to accelerate traditional design tasks. For example, Airbnb is developing an AI system that can recognize sketches of customer experience hand-drawn by a designer on a drawing board and automatically render them into specifications for software engineers (Saarinen, 2017; Schleifer, 2017). If this were the only kind of use of AI, the essence of design practice would remain untouched: innovators would do what they did in the past (i.e., to draw components of the customer experience and translate them into specifications), but faster. However, Netflix and Airbnb go well beyond. They bring automation directly into problem solving; that is, in the definition of detailed design choices: which interface to show to a specific user, which content to create, how to position a product compared to competitors. In this new context, designers and engineers do not simply make those decisions faster. They just do not make them, as they are delegated to AI. In other words, AI is the stimulus for an epiphany in the way we look at design (Magistretti, Dell'Era, and Verganti, 2020; Verganti, 2009, 2011a, 2011b). This has profound implications both in terms of the object and of the process of design.
人工智能极大地改变了这种情景：它将数字自动化向上游移动，从制造到设计。请注意，自动化可能仅限于加速传统设计任务。例如，Airbnb 正在开发一种人工智能系统，可以识别设计师在绘图板上手绘的客户体验草图，并自动将其转化为软件工程师的规格说明（Saarinen，2017 年；Schleifer，2017 年）。如果这是人工智能的唯一使用方式，设计实践的本质将保持不变：创新者将继续做他们过去所做的事情（即绘制客户体验的组成部分并将其转化为规格说明），只是更快。然而，Netflix 和 Airbnb 走得更远。他们直接将自动化引入问题解决；也就是说，在详细设计选择的定义中：向特定用户展示哪种界面，创建哪种内容，如何将产品定位与竞争对手相比。在这种新的背景下，设计师和工程师不仅仅是更快地做出这些决定。他们根本不做出这些决定，因为这些决定已被委托给人工智能。 换句话说，人工智能是我们看待设计方式上的顿悟的刺激（Magistretti, Dell'Era, and Verganti, 2020; Verganti, 2009, 2011a, 2011b）。这对设计的对象和过程都具有深远的影响。
The new object of design
设计的新对象
The first dramatic change is in the object of design practice (the “what” of design). In human-intensive design, humans develop a product down to the level of details: for example, which image to be displayed on a screen. Conversely, with AI, the specific solution experienced by an individual user (i.e., what she actually sees on the screen of her mobile phone), is not only delivered but also designed by a problem-solving loop powered by AI. What humans do, in the context of AI, is not to design solutions (these are generated by the AI engine), but to design these problem-solving loops.
设计实践的第一个显著变化在于设计对象（即设计的“内容”）。在人力密集型设计中，人类将产品开发到细节层面：例如，屏幕上显示哪幅图像。相反，在人工智能中，个体用户体验的具体解决方案（即她在手机屏幕上实际看到的内容）不仅仅是被提供，而且是由人工智能驱动的问题解决循环设计的。在人工智能的背景下，人类所做的不是设计解决方案（这些由人工智能引擎生成），而是设计这些问题解决循环。
This change of object has disruptive implications. Especially because most AI algorithms do not reason like humans, that is, they do not just replicate and automate the thinking of an engineer or a designer; they work in a different way. Most of the applications we discussed in the cases of Netflix and Airbnb are instances of weak AI: they are focused on a combination of simple tasks (such as recognizing a shape in an image or if two images have different shapes) which are not nearly as sophisticated as the human thinking process they replace. Yet, by replicating these tasks millions of times (and by nurturing them with masses of data), weak AI can provide complex predictions, which even surpasses human capabilities.
这种对象的改变具有破坏性的影响。特别是因为大多数人工智能算法不像人类那样推理，也就是说，它们不仅仅是复制和自动化工程师或设计师的思维；它们以一种不同的方式工作。我们在 Netflix 和 Airbnb 案例中讨论的大多数应用都是弱人工智能的实例：它们专注于一系列简单任务的组合（比如识别图像中的形状或两个图像是否具有不同的形状），这些任务远不及它们所取代的人类思维过程那样复杂。然而，通过数百万次地复制这些任务（并用大量数据培养它们），弱人工智能可以提供复杂的预测，甚至超越人类的能力。
The consequences are important. How do you design problem-solving loops? How do you conceive design rules that are based on extremely simple tasks, but that once replicated time and time again, can autonomously provide extremely complex solutions to users? Engineers and designers are not educated this way. Their mental frames are trained to systemically embrace complex tasks. To leverage the power of AI, they need an unprecedented capability: to imagine what a dumb system can do when operating at scale.
后果很重要。你如何设计解决问题的循环？你如何构想基于极其简单任务的设计规则，但一旦一次又一次地复制，就能自主地为用户提供极其复杂的解决方案？工程师和设计师没有接受过这种教育。他们的思维框架被训练成系统地拥抱复杂任务。为了利用人工智能的力量，他们需要一种前所未有的能力：想象一个愚蠢系统在规模运作时能做什么。
The new process of design
设计的新流程
As the object of design changes (from designing solutions to designing problem-solving loops) the process of design (the “how” of design) changes as well. This is evident if we compare Figure 1 with Figure 2 previously illustrated: in the context of AI factories the design process is split into two chunks. First, a human-intensive design phase where the solution space is conceived and the problem-solving loops are designed; and then, an AI-powered phase, where the specific solution is developed for a specific user by the algorithm. As this second chunk of the process requires virtually zero cost and time, the development of the solution can be activated for each individual user, in the precise moment in which she asks for it. This in turn enables leveraging the latest available data and learning, and therefore creating, every time, a better novel solution. There are no more product or service blueprints that act as buffers between design and use. Design, delivery, and use—they all happen, in part, simultaneously.
随着设计对象的变化（从设计解决方案到设计问题解决循环），设计过程（设计的“如何”）也会发生变化。如果我们将之前展示的图 1 与图 2 进行比较，这一点就显而易见：在人工智能工厂的背景下，设计过程被分为两个部分。首先是一个人力密集型的设计阶段，在这个阶段中，解决方案空间被构想出来，问题解决循环被设计出来；然后是一个由人工智能驱动的阶段，在这个阶段中，通过算法为特定用户开发特定解决方案。由于这个过程的第二部分几乎不需要成本和时间，解决方案的开发可以在每个个体用户请求时激活。这反过来使得能够利用最新的可用数据和学习，从而每次都创造出更好的新解决方案。再也没有产品或服务蓝图作为设计和使用之间的缓冲。设计、交付和使用——它们在某种程度上同时发生。
Although this new practice is clearly visible in the realm of digital experiences based on software (such as Netflix and Airbnb), it is also gaining traction in industries based on physical products. Take for example the case of Tesla. Its operating model reflects those of Netflix or Airbnb, as it gathers massive amounts of data to design user experiences. However, to enable problem-solving loops Tesla is confronted by a tangible “hindrance”: the actual car. Hardware cannot be designed (yet) in real time, remotely and automatically. To unleash the power of AI, Tesla had therefore to reimagine the design of the car, acting in two diverse directions. First, it got rid of all the physical interacting elements (e.g., buttons) to embed most of the controls into digital user interfaces (e.g., into the large central touchscreen; Lambert, 2018). Second, it overloaded cars with sensors to collect data. Data are drawn from external sources (typically ultrasound equipment, GPS input, cameras, radar transmitters, and LIDAR) as well as internal ones. As cars go, sensors collect data and train Tesla’s learning algorithms. Interestingly enough, some of these sensors are “silent,” meaning they are not already used to provide direct value to customers, but placed “in perspective.” They are activated remotely after product release to enable new loops and provide new services to customers. The Model 3, for example, has been armed since 2017 with a cabin-facing camera placed in the rearview mirror. This camera was initially dormant (Lambert, 2017). Only in June 2019 was the camera used, thanks to new software updates, to recognize occupants and adapt some of the hardware’s adjustable components, such as the seats, vehicle mirrors, music, or driving mode preferences, in accordance with a specific user profile (Lambert, 2019).
尽管这种新的做法在基于软件的数字体验领域（如 Netflix 和 Airbnb）中明显可见，但它也在基于实体产品的行业中获得了越来越多的支持。以特斯拉为例。其运营模式反映了 Netflix 或 Airbnb 的模式，因为它收集大量数据来设计用户体验。然而，为了启用问题解决循环，特斯拉面临着一个切实的“障碍”：实际的汽车。硬件（尚未）无法实时、远程和自动设计。为了释放人工智能的力量，特斯拉必须重新构想汽车的设计，朝两个不同的方向行动。首先，它摆脱了所有的物理交互元素（例如按钮），将大部分控制嵌入到数字用户界面中（例如大型中央触摸屏；Lambert，2018）。其次，它通过传感器超载汽车以收集数据。数据来自外部来源（通常是超声波设备、GPS 输入、摄像头、雷达发射器和激光雷达）以及内部来源。随着汽车行驶，传感器收集数据并训练特斯拉的学习算法。 有趣的是，其中一些传感器是“沉默”的，意味着它们尚未被用于为客户提供直接价值，而是被“放在透视中”。它们在产品发布后远程激活，以启用新的循环并为客户提供新的服务。例如，Model 3 自 2017 年起就装备了一个安装在后视镜上的面向车厢的摄像头。这个摄像头最初是休眠的（Lambert，2017）。直到 2019 年 6 月，该摄像头才得以使用，通过新的软件更新，识别乘客并根据特定用户配置文件调整一些硬件可调组件，如座椅、车辆镜子、音乐或驾驶模式偏好（Lambert，2019）。
Artificial Intelligence and Design Principles
人工智能与设计原则
Our cases show that in the context of an AI factory, design practice changes dramatically both in terms of the object and process of design. Does AI also undermine the core principles that underpin design? In other words, is this new design practice still people centered, abductive, and iterative? Or is it rooted in different principles? Our observations suggest that AI does not question the fundamental principles of design thinking. Rather, it further reinforces them.
我们的案例表明，在人工智能工厂的背景下，设计实践在设计的对象和过程方面发生了巨大变化。人工智能是否也会削弱支撑设计的核心原则？换句话说，这种新的设计实践是否仍然以人为中心，是归纳的，是迭代的？还是根植于不同的原则？我们的观察表明，人工智能并没有质疑设计思维的基本原则。相反，它进一步强化了这些原则。
To support this statement, we start from the findings of an extensive study of AI-powered strategies conducted by one of our coauthors (Iansiti and Lakhani, 2020a). The study shows that AI affects the operating model of an organization by eliminating three limitations: scale, scope, and learning. The cases discussed in this article show that AI removes these limitations also in innovation processes, empowering design’s principles to be people centered, to create abductions, and to innovate through iterations.
为了支持这一觏述，我们从我们的一位合著者（Iansiti 和 Lakhani，2020a）进行的一项广泛研究的发现开始。该研究表明，人工智能影响组织的运营模式，通过消除规模、范围和学习三个限制。本文讨论的案例显示，人工智能还在创新过程中消除了这些限制，赋予设计原则以以人为中心，创造绑架，并通过迭代进行创新。
Scale and people centeredness
规模和以人为中心
Traditional design practice has significant scale limitations. Being one of the most intensive human-based activity, it requires the investment of significant resources and time. These scale limitations pose substantial constraints to people centeredness, as it is unreasonable to design a solution every time a user needs it. Products are instead designed for customer segments or average user archetypes (hence the use of “personas” in classic design thinking processes).
传统设计实践存在显著的规模限制。作为最为密集的人类活动之一，它需要投入大量资源和时间。这些规模限制对以人为中心造成了重大约束，因为每当用户需要时设计一个解决方案是不合理的。产品通常是为客户群体或平均用户原型而设计的（因此在经典设计思维过程中使用“人物角色”）。
AI removes significant scale limitations in design, as the development of specific solutions is performed by machines. This enables the achievement of ultimate levels of people-centeredness. In fact, as seen in the case of Netflix, supervised learning leverages a rich stream of data on each individual user. This focus on individuals can be scaled with no limitations on the number of users and the complexity of data. As a consequence, the solution that a specific user experiences (e.g., what a user sees in the screen of the Netflix application) has been developed just for her, on the basis of her own data. Interestingly, the relationship between scale and people-centeredness is now inverted. In human-intensive design, the larger the number of users and the complexity of insights, the more difficult it is to focus on individuals. In the context of AI factories, the larger the number of users and the richer and more complex the stream of data, the better the predictions of the machine on the behaviors of individuals. An even more advanced example is provided by Airbnb. Here the organization has to deal simultaneously with different categories of individuals: hosts and guests. Not only do the learning loops not suffer by this increase of complexity, but they also benefit from the integrated elaboration of data from both sides of the market.
AI 消除了设计中的重要规模限制，因为特定解决方案的开发是由机器执行的。这使得实现以人为中心的终极水平成为可能。事实上，正如 Netflix 的案例所示，监督学习利用了每个个体用户的丰富数据流。这种对个体的关注可以在用户数量和数据复杂性上无限扩展。因此，特定用户体验的解决方案（例如，用户在 Netflix 应用程序屏幕上看到的内容）是基于她自己的数据开发的。有趣的是，规模和以人为中心的关系现在被颠倒了。在人力密集型设计中，用户数量和洞察力的复杂性越大，就越难以专注于个体。在 AI 工厂的背景下，用户数量越大，数据流越丰富复杂，机器对个体行为的预测就越准确。更先进的例子是由 Airbnb 提供的。在这里，组织必须同时处理不同类别的个体：房东和客人。 学习循环不仅不会因为复杂性的增加而受到影响，而且还会受益于来自市场双方的数据的综合阐述。
Scope and abductions 范围和绑架
Human-intensive design practices also have significant limitations in scope. Products are designed for a specific industry and with a specific target. Once they are released, they are unlikely to be applied in a different context. A car is designed to be a means of transportation. Moving from there to entertainment services is unlikely to happen. Limitations of scope are significant even within the same industry. Consider the case of Intercontinental Hotels Group, previously illustrated. The solutions developed by IDEO to address short-stay travelers and business travelers required different design initiatives, by different teams, and different brands of the same organization. The scope limitations of human-intensive design pose therefore significant constraints. Once a design brief is defined and frozen, creativity can happen only within the space of that brief.
人力密集型设计实践在范围上也存在显著的局限性。产品是为特定行业设计的，针对特定目标。一旦发布，它们不太可能在不同的背景下应用。汽车被设计成一种交通工具。从那里转向娱乐服务的可能性不大。即使在同一行业内，范围的限制也是显著的。考虑之前提到的洲际酒店集团的案例。IDEO 为解决短期旅行者和商务旅行者而开发的解决方案需要不同的设计举措，由不同团队和同一组织的不同品牌完成。因此，人力密集型设计的范围限制具有显著的约束力。一旦设计简报被定义和冻结，创造力只能在该简报的空间内发生。
AI enables the removal of many limitations in scope. In the context of AI factories, a design brief is fluid and can be reframed even after a product has been released. For example, we have seen how Netflix uses unsupervised learning to find new patterns in customer tastes that were not set up at the outset of the process. These predictions are used to support abductions in imagining new movie series. AI also makes it easier to imagine radically new services. Consider for example Airbnb, which has expanded into “travel experiences,” by offering guests the possibility to take a horse ride on a beach or hire musicians. To enter this new industry Airbnb leverages the same AI factory that powers the traditional hospitality service of AI. Similarly, Tesla leverages the learning loops embedded in its cars to complement its offering (transportation) with entertainment that passengers may enjoy during a trip.
人工智能使范围中的许多限制得以消除。在人工智能工厂的背景下，设计简报是灵活的，甚至在产品发布后也可以重新构思。例如，我们已经看到 Netflix 如何利用无监督学习来发现客户口味中的新模式，这些模式在流程开始时并未设定。这些预测用于支持想象新电影系列的推断。人工智能还使得更容易想象出根本新的服务。例如，考虑一下 Airbnb，它通过提供客人在海滩上骑马或雇佣音乐家的可能性，扩展到了“旅行体验”。为了进入这个新的行业，Airbnb 利用了支持人工智能传统款待服务的同一人工智能工厂。同样，特斯拉利用其汽车中嵌入的学习循环来补充其提供的服务（交通），让乘客在旅途中享受娱乐。
Learning and iterations 学习和迭代
Traditional design practices, finally, have relevant limitations in terms of learning. In fact, design-build-test iterations that fuel learning are confined within a project. They are discontinued once a product is released. New learning that comes from the observation of real use can only feed the development of future versions. Innovation therefore happens episodically, in lumps. And as the context evolves new solutions became rapidly “old.”
传统的设计实践在学习方面存在相关的局限性。事实上，推动学习的设计-构建-测试迭代仅限于一个项目内。一旦产品发布，这些迭代就会终止。从对实际使用的观察中获得的新知识只能促进未来版本的开发。因此，创新是间歇性的，成块出现。随着背景的演变，新的解决方案迅速变得“陈旧”。
AI drastically removes limits in learning. Note that AI factories are intrinsically iterative. They deliver through loops. As the case of Netflix illustrates, each time a customer accesses the service, the firm activates a problem-solving loop. This loop not only leverages the most recent data and algorithms. It also offers a new opportunity to further learn. The algorithm, in particular, can direct the learning strategy toward improvements, that is, toward refining its parameters to solve a problem better (e.g., showing a more appropriate movie cover to a specific user), or toward exploring new opportunities (e.g., proposing to the user a new movie category). This balancing act of exploitation and exploration, facilitated by reinforcement learning and double-armed bandit algorithms, occurs continuously, throughout the entire product life cycle.
人工智能极大地消除了学习的限制。请注意，人工智能工厂本质上是迭代的。它们通过循环交付。正如 Netflix 的案例所说明的，每当客户访问该服务时，公司就会启动一个问题解决循环。这个循环不仅利用最新的数据和算法，还为进一步学习提供了新的机会。特别是算法可以引导学习策略朝着改进的方向发展，即朝着调整参数以更好地解决问题（例如，向特定用户展示更合适的电影封面），或者朝着探索新的机会（例如，向用户推荐新的电影类别）。这种通过强化学习和双臂老虎算法实现的开发和探索的平衡行为持续发生，贯穿整个产品生命周期。
The implications in terms of innovation are significant. First, learning never ends. The solution experienced by a specific user in a specific moment is not the same she experienced when the product was first released. It is the most advanced design so far. In a way, the solution is always “new.” Second, learning is based on real use. Rather than coming from testing prototypes in simplified contexts, here learning comes from the actual use of the product in a real context. Third, learning is person centered. Rather than leveraging insights from other people who used previous generation products (or tested a prototype), now data come from earlier use by the same person. Fourth, every user interaction is an opportunity to conduct new experiments. Learning loops are therefore designed with a different logic than traditional products. The latter included only the features that were considered useful at the time of design. AI engines are instead overloaded by elements whose utility is not fully exploited at the time of release. In other words, they are explicitly designed with redundant affordances (Gibson, 1977), as we saw in the case of Tesla, where the internal pointing camera has not delivered any feature for two years.
创新方面的含义是重大的。首先，学习永无止境。特定用户在特定时刻经历的解决方案与产品首次发布时的体验并不相同。这是迄今为止最先进的设计。在某种程度上，解决方案总是“新的”。其次，学习基于实际使用。学习不是来自在简化环境中测试原型，而是来自产品在真实环境中的实际使用。第三，学习是以人为中心的。与利用其他使用过上一代产品（或测试原型）的人的见解不同，现在的数据来自同一人早期使用的情况。第四，每次用户互动都是进行新实验的机会。因此，学习循环的设计逻辑与传统产品不同。后者仅包括在设计时被认为有用的功能。相反，AI 引擎被那些在发布时未完全利用的元素所超载。 换句话说，它们明确设计为冗余的可支配性（吉布森，1977 年），正如我们在特斯拉的案例中所看到的，其中内部指向摄像头已经两年没有提供任何功能。
In summary, AI factories incorporate and further empower the principles of design thinking: beyond being people centered, they are single-person centered; they facilitate creativity across segments, stakeholders, and industries, enabling abductions beyond the scope which a product was initially conceived for; finally, they are intrinsically iterative, moving learning and innovation beyond development into the product life cycle.
总的来说，人工智能工厂融入并进一步强化设计思维的原则：超越以人为中心，它们是以单个人为中心的；它们促进跨领域、利益相关者和行业的创造力，使产品最初构想的范围之外的创新成为可能；最后，它们本质上是迭代的，将学习和创新从开发推进到产品生命周期中。
Design for AI AI 设计
If AI empowers a more advanced practice of design, the converse can also happen: design can empower a more effective, human-centered implementation of AI. Think of the hospitality industry. Both Booking.com and Airbnb make intense use of AI, for example for personalized listing and helping hosts make decisions regarding pricing. Yet, Booking.com’s innovation path is less driven by design, but, rather, by an intense use of A/B testing. At Booking.com features are therefore pushed “from the lab outwards” rather than “from the user-inwards.” On the other side, Airbnb has design thinking in its DNA, as two of its founders, Brian Chesky and Joe Gebbia, are alumni of Rhode Island School of Design. In 2011 the company launched the Snow White project to bring human-centered design at all levels of the organization, and redesign competitive strategy (Fields Joffrion, 2018). The project was led by Rebecca Sinclair, then head of user experience research and design, and a former designer at IDEO. “At the time, like a lot of tech startups, we called the website and the app ‘the product,’” says Sinclair. But then “by practicing design thinking […] we were looking at a journey […], imagining our customers booking, and we saw that the moments that mattered most were offline. This offline experience—this trip to Paris or stay in a treehouse—is what they were buying from us, not a website or an app. That’s when we started to say, ‘the product is the trip’ and began shifting our perspective.” The result of this design perspective in driving innovation is evident not only by comparing Airbnb’s user interface with Booking.com, but also in the capability of Airbnb to funnel AI toward the development of new business categories, such as Airbnb Experiences.
如果人工智能推动了更先进的设计实践，相反的情况也可能发生：设计可以推动更有效、以人为中心的人工智能实施。想想酒店行业。Booking.com 和 Airbnb 都大量使用人工智能，例如用于个性化列表和帮助房东做出定价决策。然而，Booking.com 的创新路径不太受设计驱动，而是更多地依赖于大量使用 A/B 测试。在 Booking.com，功能因此是“从实验室向外推进”的，而不是“从用户内部向外推进”的。另一方面，Airbnb 的 DNA 中融入了设计思维，因为其两位创始人 Brian Chesky 和 Joe Gebbia 都是罗德岛设计学院的校友。2011 年，该公司启动了“白雪公主项目”，将以人为中心的设计引入组织的各个层面，并重新设计竞争策略（Fields Joffrion，2018）。该项目由当时的用户体验研究和设计负责人 Rebecca Sinclair 领导，她曾是 IDEO 的设计师。“当时，像许多科技初创公司一样，我们称网站和应用为‘产品’，”Sinclair 说道。 然后，“通过实践设计思维[...]我们正在考虑一段旅程[...]，想象我们的客户预订，我们发现最重要的时刻是线下的。这种线下体验——去巴黎旅行或住在树屋里——是他们从我们这里购买的，而不是一个网站或一个应用程序。这时我们开始说，‘产品就是旅行’，并开始转变我们的视角。”这种设计视角在推动创新方面的结果不仅体现在比较 Airbnb 的用户界面与 Booking.com，还体现在 Airbnb 将人工智能引向发展新业务类别的能力，比如 Airbnb 体验。
Microsoft offers another insight into the key role of design for the implementation of AI. As Microsoft’s CEO, Satya Nadella, stated, AI is the new “runtime” of its firm. Its operating model is now built around AI. This required the company to radically reorganize its IT and data assets, which had been dispersed across the company’s various operations (Iansiti and Lakhani, 2020a). Interestingly, the transformation was not led by an IT manager or IT experts. Rather, the whole initiative was driven by Kurt Del Bene, an executive with product experience, as he was the former head of Microsoft’s Office business unit, and a team of leaders and engineers from product functions. Nadella indeed wanted the company operating processes and AI factory to be designed as one designs products rather than IT infrastructures. 重試 錯誤原因
Implications for Innovation and Design Theories
创新和设计理论的启示
Professor Simon ignores the possibility mentioned in my article that problem solving and problem finding might require opposite, or at least orthogonal cognitive strategies (and by ‘cognitive’ I mean not just rational, but emotional and motivational as well). (Csikszentmihalyi, 1988b, p. 184)
西蒙教授忽略了我在文章中提到的可能性，即解决问题和发现问题可能需要相反的，或至少是正交的认知策略（通过“认知”我指的不仅仅是理性，还包括情感和动机）。 （奇克森特米哈伊，1988 年，第 184 页）
In 1988, before recent advancements in computer intelligence, and of the challenges that this posed to our understanding of cognition, Mihaly Csikszentmihalyi and Herbert Simon started a dispute on the true nature of creativity. Simon and Csikszentmihalyi were addressing a question that is central for innovation and design scholars: how do we think creatively? How do we have ideas and find solutions?
1988 年，在计算机智能的最新进展之前，以及这给我们对认知的理解带来的挑战，米哈伊·奇克森米哈伊和赫伯特·西蒙开始就创造力的真正本质展开争论。西蒙和奇克森米哈伊正在探讨一个对创新和设计学者至关重要的问题：我们如何进行创造性思维？我们如何产生想法并找到解决方案？
Simon, in exploring the potential of a computer program called “BACON” that he and his colleagues had developed at Carnegie Mellon University, was supporting a rational perspective of cognitive processes (Simon, 1988), where creativity could be interpreted as a process of problem solving (and therefore, partly embedded into computers). In a following paper, Csikszentmihalyi challenged this perspective (1988a, p. 160): “Simon wishes to prove, namely, that creativity is nothing but problem solving”; Csikszentmihalyi instead proposed “problem finding as the hallmark of creativity.” Simon (1988) reacted to Csikszentmihalyi’s challenge by further reinforcing its position (p. 178): “I would claim that, just as finding laws that explain data is a problem-solving process, so finding good problems and finding relevant data for solving them are problem-solving processes of a normal kind” (our italics). The essence of the response by Csikszentmihalyi is in the opening statement of this section: problem solving and problem finding do have a different nature.
西蒙在探索他和他的同事在卡内基梅隆大学开发的一个名为“BACON”的计算机程序的潜力时，支持了一个认知过程的理性观点（西蒙，1988 年），在这个观点中，创造力可以被解释为问题解决的过程（因此，在一定程度上，嵌入到计算机中）。在随后的一篇论文中，奇克森特米哈伊（Csikszentmihalyi）挑战了这一观点（1988a，第 160 页）：“西蒙希望证明的是，创造力只不过是问题解决”；奇克森特米哈伊反而提出“问题发现是创造力的标志”。西蒙（1988 年）对奇克森特米哈伊的挑战做出了进一步的回应（第 178 页）：“我要声明，正如找到解释数据的规律是一个问题解决的过程一样，找到好问题并找到解决它们的相关数据也是一种正常的问题解决过程”（我们的斜体）。奇克森特米哈伊的回应的核心在于本节的开头声明：问题解决和问题发现确实具有不同的本质。
This dispute anticipated the evolution of innovation and design theories in the years to follow, with two rather independent streams unfolding: innovation as a process of problem solving, or innovation as a process of problem finding, or, in other words, as sensemaking. The first perspective (advocated by Simon) took the spotlight. Indeed, innovation scholars, especially those who investigated the process of product development, mainly looked at innovation as the result of creative problem solving (Clark and Fujimoto, 1991; Krishnan, Eppinger and Whitney, 1997; Ulrich and Eppinger, 1995). In this perspective innovation challenges can be described as a hierarchical tree (Clark, 1985), where solutions at a higher level become objectives for lower level problems (enacting Simon’s view that problem finding can be seen as nested problem solving). This perspective has captured the larger share of attention also in the development of theories of design driven innovation, in which the d-school at Stanford and the related frameworks of Design Thinking are rooted (Buchanan, 1992; Brown, 2008, 2009; Kelley and Kelley, 2013; Martin, 2009). Although Design Thinking also embraces the framing of a problem (as for example in the double diamond model), it is still theoretically rooted in the theories of problem solving laid down by Simon (in which problem framing is still considered a rational activity included in problem solving).
这场争议预示着创新和设计理论在接下来的几年中的演变，展现出两个相对独立的流派：创新作为问题解决的过程，或者创新作为问题发现的过程，换句话说，作为意义建构。第一个观点（由西蒙提倡）受到了关注。事实上，创新学者，特别是那些研究产品开发过程的学者，主要将创新视为创造性问题解决的结果（Clark 和 Fujimoto，1991 年；Krishnan，Eppinger 和 Whitney，1997 年；Ulrich 和 Eppinger，1995 年）。在这个观点中，创新挑战可以被描述为一个分层树（Clark，1985 年），在这个树中，更高层次的解决方案成为更低层次问题的目标（实现西蒙的观点，即问题发现可以被看作是嵌套问题解决）。这个观点在设计驱动创新理论的发展中也占据了更大的关注份额，其中斯坦福大学的 d 学院和相关的设计思维框架根植于其中（Buchanan，1992 年；Brown，2008 年，2009 年；Kelley 和 Kelley，2013 年；Martin，2009 年）。 尽管设计思维也包括问题的框架（例如双钻石模型中），但它在理论上仍根植于西蒙提出的解决问题理论（其中问题框架仍被视为包含在问题解决中的理性活动）。
This focus of theory development in the past decades was justified by the fact that problem solving was complex and therefore required the most significant chunk of effort by humans. However, the current diffusion of AI is dramatically changing this scenario. Problem solving is now increasingly embedded into the automated learning loops of AI factories. If problem solving is performed by machines, what kind of thinking is left to humans in innovation? The role of humans in AI factories (indicated in the phase “design” in Figure 2) becomes to understand what problems should be addressed and to drive the continuous evolution of algorithms toward a meaningful direction. The core of this activity is not problem solving, but problem finding.
过去几十年理论发展的重点是由于问题解决是复杂的，因此需要人类付出最大的努力。然而，人工智能的当前扩散正在显著改变这种情况。问题解决现在越来越多地嵌入到人工智能工厂的自动化学习循环中。如果问题解决由机器执行，那么人类在创新中还剩下什么样的思考？人类在人工智能工厂中的角色（在图 2 中的“设计”阶段中指示）是理解应该解决哪些问题，并推动算法朝着有意义的方向不断演进。这一活动的核心不是解决问题，而是发现问题。
The consequences for the theories of innovation are substantial. In fact, as Csikszentmihalyi (1988b) clarifies in the dispute with Simon, “problem solving and problem finding might require opposite, or at least orthogonal cognitive strategies” (p. 184). This implies that the theoretical framework of problem solving, that we extensively leveraged in the past to understand innovation, will be less effective to understand human creativity in the context of AI. We need to complement those theories with new frameworks.
创新理论的后果是重大的。事实上，正如 Csikszentmihalyi（1988b）在与 Simon 的争论中澄清的那样，“问题解决和问题发现可能需要相反的，或至少是正交的认知策略”（第 184 页）。这意味着我们过去广泛利用的解决问题的理论框架，将不太有效地用于理解人类创造力在人工智能背景下的情境。我们需要用新的框架来补充这些理论。
In his dispute, Csikszentmihalyi also suggested a possible path for these new frameworks, leveraging earlier studies he conducted on objects and products (Csikszentmihalyi and Rochberg-Halton, 1981): problem finding is an activity of meaning making, or, in other words, of sensemaking. Just to mention a simple example put forward by Csikszentmihalyi in his discussion with Simon: an algorithm that has been created to solve a problem cannot refuse to solve it; it cannot pull the plug (unless this trigger is already incorporated in its code). A human can. She can avoid to create, if it does not make sense, morally, emotionally, or by intrinsic motivation.
在他的争论中，奇克森特米哈伊还提出了这些新框架的可能路径，利用了他早期对物体和产品进行的研究（奇克森特米哈伊和罗奇伯格-哈尔顿，1981 年）：问题发现是一种赋予意义的活动，或者换句话说，是一种意义构建。只需提及奇克森特米哈伊在与西蒙讨论中提出的一个简单例子：一个被创建来解决问题的算法不能拒绝解决它；它不能中断（除非这个触发器已经内置在它的代码中）。人类可以。她可以避免创造，如果这没有道德上、情感上或内在动机上的意义。
In the past, the perspective of innovation as an activity of sensemaking (i.e., of giving meaning to things and experiences) has only timidly found space in innovation studies. A few scholars, mainly in the field of design driven innovation, have plunged deeply in problem framing (see for example Dorst, 2015; Schön, 1982, 1995) and innovation of meaning (starting from Krippendorff’s [1989] definition that “Design is making sense of things”; see also Jahnke [2013]; Krippendorff [2006]; Norman and Verganti [2014]; Stigliani and Ravasi [2012]; Verganti [2008, 2009]; Verganti and Öberg [2013]). Our understanding of innovation as sensemaking is still very limited.
过去，将创新视为一种感知活动（即，赋予事物和经验意义）的视角在创新研究中仅仅是小心翼翼地找到了一席之地。一些学者，主要是在以设计驱动的创新领域，深入探讨了问题框架（例如 Dorst，2015 年；Schön，1982 年，1995 年）和意义创新（从 Krippendorff 的定义开始[1989]，“设计是对事物赋予意义”；另请参见 Jahnke [2013]；Krippendorff [2006]；Norman 和 Verganti [2014]；Stigliani 和 Ravasi [2012]；Verganti [2008 年，2009 年]；Verganti 和Öberg [2013]）。我们对创新作为感知活动的理解仍然非常有限。
There is, however, a relevant body of theories, which has developed outside the circles of innovation scholars that we can leverage to address this new theoretical challenge. Sensemaking in organizations has indeed received significant attention in organizational psychology since the work of Weick that addresses how people give meaning to their collective experiences (Weick [1995]; Weick, Sutcliffe and Obstfeld [2005]; for an extensive review see Maitlis and Christianson [2014]). Of particular interest for investigations of innovation and design is the focus on the construction of new meaning, also indicated as sense giving (Gioia and Chittipeddi, 1991) or sense breaking (Pendleton-Jullian and Seely Brown, 2016).
然而，有一系列相关的理论是在创新学者圈之外发展起来的，我们可以利用这些理论来解决这个新的理论挑战。组织中的意义建构确实在组织心理学中受到了重视，自从 Weick 的工作以来，人们如何赋予他们的集体经验意义已经得到了广泛关注（Weick [1995]；Weick，Sutcliffe 和 Obstfeld [2005]；有关详尽审阅，请参阅 Maitlis 和 Christianson [2014]）。对于创新和设计调查而言，特别感兴趣的是对新意义的构建的关注，也被称为意义赋予（Gioia 和 Chittipeddi，1991）或意义打破（Pendleton-Jullian 和 Seely Brown，2016）。
There is therefore an enormous (and intriguing) space ahead to be explored. We predict that the most significant future theoretical developments in innovation theories will come from a deeper understanding of problem finding and will leverage theories of sensemaking. Also, we predict that design will move closer to organization theories, and especially leadership, which is an inherent act of sensemaking (Scharmer, 2007).
因此，前方有一个巨大（而且有趣）的空间等待探索。我们预测，创新理论中最重要的未来理论发展将来自对问题发现的更深入理解，并将利用意义构建理论。此外，我们预测设计将更加接近组织理论，特别是领导力，这是一种内在的意义构建行为（Scharmer，2007）。
Conclusion and Future Research Directions
结论和未来研究方向
The emergence of software, digital networks, and AI is driving widespread transformation across the economy. AI automates decision-making and learning, which is the core of innovation. The potential impact on innovation performance, as seen in the examples discussed in this article, is important. By removing the typical limitations (in scale, scope, and learning) of human-intensive design, AI can offer better performance in terms of customer centricity, creativity, and rate of innovation.
软件、数字网络和人工智能的出现正在推动经济的广泛转型。人工智能自动化决策和学习，这是创新的核心。正如本文讨论的例子所示，对创新绩效的潜在影响是重要的。通过消除人力密集型设计的典型限制（规模、范围和学习），人工智能可以在客户中心性、创造力和创新速度方面提供更好的绩效。
Yet, to capture this potential, managers need to fundamentally rethink the way their organization innovates. Design practice, in the age of AI, is completely different than the human-intensive innovation processes many organizations have in place today. For example, in AI-powered organizations, the role of humans is not to develop full solutions (which evolve in real time by AI), but to understand which innovation problems are meaningful, framing the innovation effort, and set up the software, data infrastructure, and problem-solving loops that will solve them.
然而，要抓住这一潜力，管理者需要从根本上重新思考他们的组织创新方式。在人工智能时代，设计实践与许多组织目前采用的以人为中心的创新流程完全不同。例如，在人工智能驱动的组织中，人类的角色不是开发完整解决方案（这些解决方案会通过人工智能实时演变），而是理解哪些创新问题是有意义的，构建创新努力，并设置软件、数据基础设施和解决问题的循环，以解决这些问题。
In this article we have illustrated how pioneering organizations, such as Netflix and Airbnb, have implemented this new design practice, and how they use it to create value. Still, we are at the beginning of a transformation in innovation processes, whose extent is difficult to fully capture. Many fundamental questions are still open. For example: are AI-powered innovation practices appropriate in any context, or does their potential depends on industry or on company-specific factors, including for example strategy or culture? Or, how can organizations transition from human-intensive to AI-centric innovation systems? Which changes of competences are required (for example, we showed that designing problem-solving loops requires new sets of skills), and which roles should lead this transition (the changes will reach across R&D, manufacturing, sales, IT and beyond)? As other pioneering managers and organizations will explore the adoption of AI in innovation, these questions will find new and more profound answers.
在本文中，我们阐述了先锋组织（如 Netflix 和 Airbnb）如何实施这种新的设计实践，以及他们如何利用它创造价值。然而，我们正处于创新过程转型的初期阶段，其范围难以完全捕捉。许多基本问题仍然悬而未决。例如：AI 驱动的创新实践在任何情境下是否合适，或者它们的潜力取决于行业或公司特定因素，包括战略或文化等？又或者，组织如何从以人为主的创新系统过渡到以 AI 为中心的创新系统？需要哪些能力的变化（例如，我们表明设计问题解决循环需要新的技能集），以及哪些角色应该引领这一转变（这些变化将涉及研发、制造、销售、IT 等领域）？随着其他先锋管理者和组织探索在创新中采用 AI，这些问题将找到新的更深刻的答案。
For scholars, the implications in terms of innovation and design theory are also substantial. New theoretical questions arise and new frameworks are needed. For example: how can we define and conceptualize innovation, in a context where change is never over and a solution is never “old”? We have seen, in fact, that problem-solving loops can keep learning and continue to deliver improved solutions to a user. How does one apply concepts such as incremental and radical innovation in a context in which the solution keeps evolving? Another example is the concept of accountability in innovation. We have seen that in AI factories solutions are created, improved, and personalized by machines, which operate through loops that scale up rapidly, with the potential of creating unintended outcomes, including the amplification of biases. Are existing theoretical frameworks that connect decisions to outcome in innovation still valid, when decisions are made by machines? And do current models of incentivizing and rewarding innovation still hold up?
对学者们来说，创新和设计理论方面的含义也是重大的。新的理论问题出现了，需要新的框架。例如：在一个变化永无止境、解决方案永不“陈旧”的背景下，我们如何定义和概念化创新？事实上，我们已经看到，问题解决循环可以持续学习，并继续为用户提供改进的解决方案。在解决方案不断演变的情况下，如何应用增量和根本创新等概念？另一个例子是创新中的问责概念。我们已经看到，在人工智能工厂中，解决方案由机器创建、改进和个性化，通过快速扩大的循环运作，可能导致意想不到的结果，包括偏见的放大。在决策与创新结果之间建立联系的现有理论框架在决策由机器做出时仍然有效吗？当前的激励和奖励创新的模式是否仍然适用？
One the most fascinating theoretical avenues, in our view, concerns the way scholars interpret decision-making in innovation. For one, as problem-solving is increasingly delegated to machines, humans will more deeply engage in problem finding (i.e., collectively defining which problems make sense to address). However, we still know little of how problem finding in innovation occurs. Past innovation theory has focused largely on problem-solving. A focus on problem finding would require new theoretical lenses. In this article we have suggested that future innovation and design frameworks could leverage theories of sensemaking. This would bring innovation even closer to organization theory, where sensemaking has been deeply explored, as with theories of leadership. One thing is for sure—this space promises to be one of the most fascinating journeys for innovation scholars in the years to come.
在我们看来，最迷人的理论途径之一涉及学者们如何解释创新中的决策制定。首先，随着问题解决越来越多地被委托给机器，人类将更深入地参与问题发现（即，共同定义哪些问题有意义需要解决）。然而，我们对创新中的问题发现过程知之甚少。过去的创新理论主要关注问题解决。关注问题发现将需要新的理论视角。在本文中，我们建议未来的创新和设计框架可以利用意义构建理论。这将使创新与组织理论更加接近，而在组织理论中，意义构建已经得到深入探讨，就像领导理论一样。有一点是肯定的——这个领域将成为未来几年创新学者最迷人的旅程之一。
Biographies 传记
Roberto Verganti is professor of leadership and innovation at the House of Innovation of the Stockholm School of Economics, where he co-directs The Garden—the Center for Design and Leadership. He is also the co-founder of Leadin’Lab, the laboratory on the Leadership, Design and Innovation of Politecnico di Milano. Roberto is the author of Overcrowded. Designing Meaningful Products in a World Awash with Ideas, published by MIT Press in 2017, and of Design-Driven Innovation, published by Harvard Business Press in 2009, which has been selected by the Academy of Management for the George R. Terry Book Award as one of the best six management books published in 2008 and 2009. Roberto serves on the Advisory Board of the European Innovation Council of the European Commission. (www.verganti.com)
罗贝托·维尔甘蒂（Roberto Verganti）是斯德哥尔摩经济学院创新之家（House of Innovation）的领导力和创新教授，他共同主持“The Garden”——设计与领导力中心。他还是米兰理工大学领导力、设计和创新实验室（Leadin’Lab）的联合创始人。罗贝托是《拥挤》（Overcrowded. Designing Meaningful Products in a World Awash with Ideas）一书的作者，该书于 2017 年由麻省理工学院出版社出版；他还是《设计驱动创新》（Design-Driven Innovation）一书的作者，该书于 2009 年由哈佛商业出版社出版，被管理学会选为 2008 年和 2009 年最佳六本管理书籍之一。罗贝托还担任欧洲创新委员会咨询委员会的成员。（www.verganti.com）Luca Vendraminelli is a PhD candidate at the University of Padova, Italy and a visiting fellow at LISH, the Laboratory for Innovation Science at Harvard University. His interests revolve around the mechanisms of digital transformations in traditional companies. He is investigating how to govern digital transformation by means of strategy, and the effects of Artificial Intelligence on operating models.
卢卡·文德拉米内利是意大利帕多瓦大学的博士候选人，也是哈佛大学创新科学实验室（LISH）的访问学者。他的兴趣主要集中在传统公司数字转型的机制上。他正在研究如何通过战略来管理数字转型，以及人工智能对运营模式的影响。Marco Iansiti is the David Sarnoff Professor of Business Administration at Harvard Business School, where he is also a codirector of LISH, the Laboratory for Innovation Science, and of the Digital Initiative at HBS. His work has appeared in scientific journals such as Journal of Product Innovation Management, Strategic Management Journal, Management Science, Organization Science, Research Policy, and Industrial and Corporate Change. More recently, his writings on innovation and digital transformation made HBR’s list of top 10 papers of the year for three of the last four years. Iansiti has authored or coauthored several books, including Technology Integration, and The Keystone Advantage, both published by Harvard Business School Press. His latest book, co-authored with Karim R. Lakhani, is Competing in the Age of Artificial Intelligence published by Harvard Business School Press (2020).
马可·伊安西蒂（Marco Iansiti）是哈佛商学院（Harvard Business School）戴维·萨诺夫（David Sarnoff）商业管理教授，同时也是 LISH（创新科学实验室）和 HBS 数字化倡议的联合主任。他的研究成果发表在《产品创新管理杂志》（Journal of Product Innovation Management）、《战略管理杂志》（Strategic Management Journal）、《管理科学》（Management Science）、《组织科学》（Organization Science）、《研究政策》（Research Policy）和《工业与公司变革》（Industrial and Corporate Change）等科学期刊上。最近，他关于创新和数字转型的著作连续四年中有三年入选《哈佛商业评论》（HBR）年度十大论文之列。伊安西蒂撰写或合著了多本书籍，包括由哈佛商学院出版的《技术整合》（Technology Integration）和《关键优势》（The Keystone Advantage）。他与卡里姆·R·拉哈尼（Karim R. Lakhani）合著的最新著作是《人工智能时代的竞争》（Competing in the Age of Artificial Intelligence），于 2020 年由哈佛商学院出版社出版。
