什么是人工智能 人工智能可谓是计算机科学的一个大范畴，旨在创造能够自主发挥智能功能的系统，与人类进行语言交流。咱们来看看其中的一些分支。语音识别领域首先，有语音识别领域，就是让机器能听懂人说话，也能够回应。想象一下你和手机的语音助手交流，就是这方面的应用。想象一下，你正准备出门，你对着手机说：“嘿，智能助手，明天的天气怎么样？” 这时，语音识别技术开始发挥作用。你的声音被传输到系统中，经过复杂的算法和模型分析，智能助手成功将你的口头语言转化为文字，并理解了你的请求。接下来，它通过与天气数据库的连接，找到了明天的天气预报，并以口头语言回应你：“明天将会是晴天，最高气温25摄氏度。” 这一切都是语音识别领域的体现。自然语言处理（NLP）领域然后是自然语言处理（NLP）领域，让机器能够理解和使用语言，就像我们平时书写和阅读文本一样。这使得聊天机器人能够理解你的问题并做出合理回答。当你正坐在电脑前，对着机器人输入文字：“今天有什么新鲜事？” 这一切都牵扯到了NLP的技术。你的文字输入被计算机理解，系统能够识别你的询问并作出恰当的回应，仿佛是与一个了解你言辞、语气的朋友对话一般。在社交媒体上，NLP技术也发挥着关键作用。当你在评论朋友的照片时，系统可以分析你的语言，了解你的情感，从而更好地理解你对照片的反馈。这使得社交媒体平台能够根据用户的兴趣和喜好为其个性化推荐内容。计算机视觉领域计算机视觉则关注机器如何通过处理图像信息来理解和模拟人类视觉。当你的手机相机能够自动识别人脸或者车辆时，就是计算机视觉在发挥作用。想象一下，你正在使用智能手机的相机拍摄一张照片。计算机视觉技术立即介入，分析这张照片中的各种元素。它可以识别图像中的人物、物体和背景，甚至可以自动调整相机设置以获得更佳的照片效果。这样的技术使得拍照变得更加智能和便捷。在安防领域，计算机视觉也起到了关键作用。想象一下，在一个商业区域的监控摄像头中，计算机视觉系统能够检测异常行为，如盗窃或破坏，然后及时发出警报。这种技术的应用使得安全监控更加智能化，提高了反应速度和准确性。图像处理领域图像处理领域涉及如何处理和编辑图像，虽然它与人工智能直接挂钩较小，但对于计算机视觉的完整性是必要的。当你拍摄了一张照片，但由于光线不足，整个画面显得较暗。这时，图像处理技术就能派上用场。通过一系列的算法，计算机可以智能地调整图像的亮度和对比度，使得照片更加清晰、生动，仿佛重新捕捉到了光影的美妙。在医学影像方面，图像处理也发挥着巨大的作用。想象一下，医生需要对一张CT扫描图进行分析，但由于图像质量问题，一些细节可能被掩盖。图像处理技术可以帮助突显重要的结构，使医生更容易检测到潜在的病变或异常。机器人行为学习机器人行为学习使机器能够理解周围环境，并以流畅的方式移动，有点像我们人类能够在复杂环境中自如行走的感觉。想象一下，你的家里有一台智能扫地机器人。刚开始，它可能并不知道房间的布局，但通过机器人行为学习的技术，它能够逐渐学习并记住家里的每一个角落。当机器人开始扫地时，它能够智能地避开家具和其他障碍物，优化清扫路径，从而提高清扫的效率。在仓储和物流领域，机器人行为学习也带来了革命性的变化。机器人可以通过学习仓库中物品的摆放和货物的移动规律，智能地规划路径，实现高效的仓储管理和货物运输。在服务业中，像餐厅和酒店等场景中的服务机器人也是机器人行为学习的典型应用。它们能够通过与顾客的互动学习，适应不同环境下的需求，提供更个性化和友好的服务体验。模式识别（特征识别）模式识别则涉及到机器如何辨别并理解数据中的模式，就像我们能够把相似的事物放在一起。在金融领域，模式学习技术被广泛用于检测欺诈行为。银行可以通过学习正常的交易模式，发现异常模式，从而迅速识别可能的欺诈活动。这种技术的应用使得金融机构能够更加迅速和准确地应对潜在的风险。而在市场营销领域，模式学习技术可以用于分析消费者行为。通过学习消费者的购物模式和偏好，商家能够更好地理解市场需求，提供个性化的推荐和服务，从而提升客户满意度和销售效益。神经网络领域神经网络则是一种模拟人脑结构和功能的方式，通过学习和适应，机器可以具备某种认知能力。而深度学习则是在神经网络的基础上更深入、更复杂的学习过程，这使得机器能够处理更加复杂的任务和数据。这一段的案例没有办法那么通俗易懂，想象一下，你正在训练一台神经网络来辨识猫和狗的照片。一开始，这个神经网络就像是一张白纸，一无所知。你向它展示了一堆猫和狗的图片，并告诉它哪些是猫，哪些是狗。通过这个过程，神经网络开始学习图片中的各种特征，比如猫耳朵的形状、狗的尾巴的长度等。机器学习领域在机器学习的范畴下，有监督学习，就是通过大量标记好的数据来教机器学习，比如训练机器识别朋友的名字。还有强化学习，这就像是给机器设定一个目标，然后机器通过试错来不断优化，就好比让机器人尝试翻墙直到成功的例子。说一个贴近生活的例子，假如你有一堆关于房价的真实数据，包括房屋的大小、位置、周围环境等信息，以及相应的房价。通过机器学习，你可以训练一台模型，使其能够根据这些数据来预测未来房价。这个模型会通过分析数据中的各种关系和趋势，逐渐学到房价与各个因素之间的复杂关系。还有强化学习，就像教狗狗做技能一样。你告诉模型在某种情况下应该采取什么行动，而模型通过反馈机制不断调整策略，逐渐学到在不同环境中如何做出最优的决策。最后，我们越深入了解，我们越能理解到人工智能不仅仅是一种技术，更是一系列使机器具备智能的方法和技巧的集合。

数字化转型新话题，如何全面拥抱AI ✦数字化转型需要重新构建组织，并且不能外包。00:04 这很困难，需要最高管理层来推动变革。 哈佛商学院的 Karim Lakhani 博士讨论了技术、创新和商业的交叉点，以及数字化、数字数据科学以及新商业和运营模式的设计的指数效应。✦技术作为一种赋能工具贯穿于整个组织。02:58 现代公司及其基本模型的历史视角。 从部门和职能结构过渡到基于生态系统的组织。✦数字化转型需要运营和组织的根本变革08:58 云服务的集成可实现可变成本和规模经济 人工智能和算法正在推动以数据为中心的组织模式的变化✦人工智能优先组织从人类瓶颈转向算法瓶颈11:40 人工智能优先的组织面临着算法和分析能力的瓶颈，从而实现了巨大的扩展机会。 对企业的影响包括改变商业模式、通过人工智能和数字化旅程增强价值创造和价值获取。✦数据科学和人工智能对商业领袖至关重要17:18 了解数据科学和人工智能对于商业领袖来说至关重要，而不仅仅是成为数据科学家。 拥抱更广泛的技术堆栈并将其视为一项持续的任务也至关重要。公司需要持续投资技术而不是将其外包。✦组织转型是数字化转型战略的重要组成部分。19:45 仅仅专注于购买数字化转型技术往往会导致失败，因为它忽视了内部组织变革。 与不同人工智能采用成熟度水平的供应商、合作伙伴和内部部门达成共识是一项挑战，需要强大的领导力和支持需求。✦通过人工智能功能进行个性化内容创建和营销。24:53 人工智能能够以零边际成本创建按需、个性化的内容。 人工智能驱动的营销可以大规模创建个性化广告和内容，从而改变营销供应链。✦人工智能驱动的实时销售辅导正在改变销售流程。27:34 由于大流行，销售转移到 Zoom，从而提高了虚拟销售对话的舒适度。 与 Zoom 集成的人工智能工具为销售人员提供实时指导，彻底改变销售流程。✦数字化转型战略需要自上而下的协调和项目排序。32:32 人工智能项目的优先顺序应与公司的战略保持一致，无论是关注成本领先还是客户满意度和新业务模式。 成功的数字化转型通常始于高层的支持，并涉及按顺序扩展项目以满足总体战略。✦公司需要将流程嵌入软件和技术中以实现数字化转型。35:09 人工智能和技术将增强人类流程，而不是取代它们。资源分配决策需要反映这一点。 解决人工智能中的偏见至关重要，因为有偏见的数据和算法可能会大规模放大伤害。数据科学知识和法律问题是需要关注的关键领域。✦人工智能增强而不是摧毁人类能力40:38 人工智能带来的是能力和容量的提高，而不是适当的规模和成本效率 企业领导者需要投资于自己和员工的学习，以适应人工智能✦系统地投资于学习和为您的团队构建框架。42:58 随着组织变得更加数字化，数据安全变得至关重要。 数据标签和管道的安全问题对于算法的准确性也很重要。It's a total rewiring of your organization. That's what's ahead for most people. Guess what. It's damn difficult. [Laughter] [Laughter] And it's something that the C-suite – this is CXOTalk – cannot outsource to other people. They have to become experts in what's going on and drive the change. That's Karim Lakhani from the Harvard Business School. My work is at the intersection of technology, innovation, and business, how digital technologies are transforming businesses and changing business models and operating models. On the research side of things, I run a lab called The Laboratory for Innovation Science where I'm the founder and co-director. We've done a lot of work on crowdsourcing: crowdsourcing for innovation, crowdsourcing for algorithms. That's what got me into this AI space more than a decade ago. We had partners like NASA, Harvard Medical School, The Broad Institute, and so forth. In light of that, I have (over the last year) launched a new institute at Harvard called The Digital, Data, and Design Institute (D^3) because we think that these three technologies (digitization and digital data science and also the design of new business and operating models) are having an exponential effect. We have launched with more than 30 faculty members at Harvard Business School (and some colleagues at the engineering school) with 12 different labs. We're trying to work closely with companies to solve problems and do great research as well. Your focus is on how companies can compete, so what is going on? What's unique about our present time with AI that caused you to need to look at this problem? The book title is Competing in the Age of AI. We're not even saying competing with AI but in the age of AI. In many ways, the consumer economy (with our mobile phones) has already put the vast majority of humanity in the age of AI. If you think about how you navigate your email, how you navigate your music selection, your viewing habits, your reading habits, your directions, all of that is already immersed through AI. Increasingly now, the tech giants have sort of brought that to us. But that whole world is now shifting into the rest of the economy as well. The book is really about we're not turning back with less data, less digital, less algorithms. We're going to be doing more and more of it. How does that shape what companies do, how companies compete above and beyond you being an AI native firm? The book really sort of starts with the fact that the technology is going to be an enabling tool and it's no longer a thing which sits on its own but is woven throughout the fabric of the organization.That means that your operating model and your business model are going to change. That's what the book really tries to go after. How is this different from business as we've known it historically? A modern corporation really is maybe about 120 years old. If you think about the history of humanity, most of the time we've been sort of agrarian, small, little shops, and so forth. The modern corporation basically got set up 120, 130 years ago. If there were sort of seminal views of what happened in America, you look at Alfred Sloan setting up General Motors as a multidivisional company, Thomas Edison setting up General Electric as a multidivisional company, as models by which we have always run our organizations. The idea here was that you focused. You went after one thing after the other. You had visions set up. You had functional silos set up, and you were able to go and serve your customer needs. That model has done tremendous things. Our built environment, our built organization has been set up this way. Starting with the advent of computation and computers (with IBM and Microsoft), that edifice started to change where we thought that basically now what matters is not just the ways in which we organized from the top-down but the ways in which information flows across an organization. This information flow view of the world really first started with the tech industry and the software industry and the emergence of, let's say, Windows as a computational tool that allowed lots of people that power to analyze data and do things, and the spreadsheet as the way in which you would get work done. But everything was still very much in the model of divisional structures and functional structures set up. Every time you had to share data, you would be sharing large files, and there would not be things coming together. But what we saw emerge (even in the Microsoft era) was a new type of company was emerging. This company was set up as an ecosystem. Microsoft won the PC battle because they figured out how to build an ecosystem where they had lots of complementors and lots of consumers, and they were in the middle of it. This emergence of this ecosystem in the software industry then basically spread the tech industry, and more and more companies in the tech industry got organized this way. But an interesting thing happened along the way. As these ecosystems got built – and you can think about the mobile ecosystem with iOS and Google, and then, of course, Facebook dominating from that as well, and then, of course, Slack and Salesforce, and so forth, coming on its heels – what people saw was that the ways in which you would run an ecosystem platform-based company was very different than the way in which General Motors ran or General Electric ran. This meant, oh, all of a sudden we need data to cut across our entire enterprise. This meant that we had a better view of customer journeys and could personalize and create better offerings for our clients regardless of if it was a B2B setting or B2C setting or B2B2C setting, for example. The typical silos that we had in our enterprise were no longer the ways for us to organize. I think this is what's new is that what we're seeing is this pressure to de-silofy our traditional ways of organizing and to take advantage of the fact that we now have digital footprints and data across both our company operations and our interactions with customers and our suppliers. How can we put it to use more effectively and more efficiently? What we see really is, in many ways, sort of two models emerge. There is the traditional model in which all organizations, including Harvard Business School, has been in for about 100 years, which basically scales very fast and then reaches a plateau in terms of our ability to serve more and more customers and drive more and more value. You could imagine basically a concave curve of the number of users, your scale, and the value you're creating.Then we have these digital, AI-first native companies emerging which are growing, in many ways, in exponential rates. It takes a while for them to achieve scale. But once they achieve scale, they can keep growing exponentially. And so, there's a convex curve that shows up instead. These convex organizations, these exponential organizations, at their core are set up with data cutting across the entire enterprise. At the core, drive automation in their processes. At the core, are set up to basically use algorithms to make decisions and make predictions and drive pattern recognition. That shift, we think, is fundamental. We worked our way into it through these tech giants but, increasingly, more and more industries are facing that as well. In essence, what you're saying is the rise of ecosystems and then the rise of data becomes the underlying driver that forces organizations to change in some pretty fundamental ways. One hundred percent. Of course, we'd add in cloud computing and then the advances in algorithms in the last 20 years. Cloud, in many ways, made technology a variable cost instead of a fixed cost. You could then drive massive economies of scale, the cloud company provider, to then be able to take advantage of what you needed. All that have been these trends going in lockstep. But that has meant that the way in which you run a company and the way in which you organize production, operations, are fundamentally different in the ways you might have done things before. Subscribe to our YouTube channel and hit the subscribe button at the top of our website so we can send you our newsletter and you can stay up to date on these amazing live shows. Now overlay algorithms and AI on top of this ecosystem and data-centric model for how we must exist as organizations. Overlay the AI aspect on top. It's no surprise that the leaders in AI actually— Of course, universities were at the source of some of the breakthroughs in neural nets and so forth. But the adopters and the drivers of the changes in AI have come from industry. Why is that? Well, if you think about a company like Alphabet or Google, they face significant challenges in their infrastructure. I remember, in 2005 or '04, talking to people at Google. They said, "Oh, yeah. We built our own server farm with, like, 40,000 servers, and we have 5 people managing it." I was just like, "What?!" [Laughter] At that time. [Laughter] And they said, "Well, we had to make advances in algorithms to be able to make this all self-managed. We didn't want to hire thousands of people running our server farms." Remember, in 2004 or '05, this is novel, right? And so, what happened is that as these ecosystems grew, as they got embedded within the lifestyles of us – we search all the time for information – they were generating a ton of data. This data was laying fallow, and they were like, "Oh, okay. Well, we need to analyze this data, so let's use artificial intelligence and drive the advancement of these algorithms so we can better understand the data." But then they were set up in a very different way. If you think about it, there's no auctioneer at the backend of Google running auctions. It's all machine-driven. The human element is acquire the customer. Acquire the customer, then the algorithms take over. They work with you on your keywords. They work with you on your SEO optimization and the auctions. Then every step in your Google journey is mediated through algorithms. They felt the need to advance the algorithms themselves as a way to drive their own usage and their own growth. Then those spilled over into the rest of the economy. To your question, what's interesting is that the bottleneck in most traditional organizations are humans. Like, "Mike, answer my damn email, please. I sent you the spreadsheet. Can you analyze this for me," or "Can you please FedEx me the hard drive so I can go look at this data?" which is what happens in most organizations or stuck in some Slack conversation and so forth. In many of these AI-first organizations, the bottlenecks are not humans but algorithms and our capacity to actually analyze that. That then opens up scaling opportunities that are quite significant. What should people in business then be doing? Okay, you're running an organization and you're surrounded by this change. What are the implications of this for you? Let's be systematic about our analysis because I really think that the advantage is really technology folks now becoming business folks and also, by the way, HR folks becoming technology folks and technology folks becoming HR folks in thinking about what this change is. The first is, in the book with Marco Iansiti and I, we talk about business models are changing. When we talk about business models, we say you need to be clear about what a business model is. It's both the ways in which you create value, why do customers want to interact with you, and the ways in which you capture value, the ways in which your company makes money. Those need to be separate sets of analyses that you need to do. Now, you can create more value with algorithms and with AI and with digital. You can be more personalized. You can scale better. You can offer your customers much more variety and scope and so forth. Think about how your customer journeys and the value creation journeys that your company does can be enhanced through AI and digital, digital journeys. That's the first bit. You lay that all out. Then separately say, "Now that I'm creating all of this value, how might I capture all of this value as well?" The typical model, I would say, if I create value from you, I charge value, some portion of that value from you. When people come to Harvard Business School, we create a ton of learning value for them. Then we charge them tuition for our value capture. What's happened is that, now with AI, you could automate value capture. You can scale value capture. You can actually even be more creative in value capture, like for example, again, the tech industry has been based on the fact that they create value for us as users and they capture value from advertisers. There are just many more ways to capture value. Thinking systematically about how algorithms, AI, and digital can help you capture value is a separate conversation that opens up. That's just on the business model side. Then we can go, "Okay, now let's bring it to the operating model," which is what actually delivers the value, what happens inside the company. There, we think about three things to scale. How do you serve more and more customers through digital operations and so forth? Here again, what you can imagine is that you want to reduce the marginal cost of acquiring more and more customers through digital. You can impact scale this way. Scope, which you offer them. If you think about your experience now with tech industries, you do more and more things with these tech businesses. How can you improve the scope of things that you do? Then learning, how do you learn better, how do you innovate better as well through machines and the data being infused throughout your organization? We see the transformation tasks for business leaders is to systematically think about applying this technology to your business model and your operating model. Which then completely begs the question how to do it because it's very easy to describe this but the execution and practice is massively difficult because the implications, the tentacles extend through every part of the company. It's a total rewiring of your organization, and that's what's ahead for most people. Guess what. It's damn difficult. [Laughter] [Laughter] And it's something that the C-suite – this is CXOTalk – cannot outsource to other people. They have to become experts in what's going on and drive the change. I would say there are three things. One is the burden on our current leaders of organizations is to learn this new stuff and not be afraid of it. This is a new body of knowledge that you need to acquire not so that you're going to become a data scientist or machine learning engineer or a cloud specialist. The joke I make at HPS is people come to HPS and we have a required curriculum for the MBA program, for sure, and we teach them accounting. If we made accounting an optional course, nobody would take it – or very few people would take it. My dean was the chair of the accounting unit. It was like, no offense to my dean, but accounting, we make it required because we feel like this is important. We feel – we know that in order for you to run a modern business, you need to understand accounting. Our sense is now today, in November of 2022, that data science and algorithms is as essential as accounting for people in business to know. Here's why. We don't want you to become an accountant when you come to HBS, but we want you to be a good business leader. Similarly, when we teach you data science and we teach you algorithms, it's not so that you're going to become a data scientist. We want you to become a good business leader. That becomes the essential bit because if data science and AI is going to be infused throughout your organization, you better understand the ways this works and, in many ways, the downfall of not doing this properly as well. That's the first thing. The second thing is this embrace of the broader technology stack. What I mean by this is that too often technology has been viewed as edifice-building, like we'll go do this technology project like we're building a factory, and then we'll forget about it. I'm sure – in all of your more than 700 programs you have run – you know that this is an ongoing task. Nothing happens in companies without software, without technology, today. We might have it done really poorly but, in fact, that's what we need to do. We know, company after company, the tech companies, Amazon has written the systems 3 times in their last 20 years of existence. Many companies have to keep rewriting their systems over and over again. Leaders need to say that the technology build is an ever-going thing and we can't sort of have that be outsourced and put away. We need to own it and think about it and be thinking about this as an ongoing set of investments we'd be making. The last bit, which I think is the most critical bit. If you think about the first two bits, the data science and the technology stack, I will say that's like 30%. The 70% is the change management you need to do and the change in the organization you need to do. That is the hardest, hardest part. What I tell technology executives that I encounter here at Harvard Business School is that I'm like, "Guess what. You better become an HR specialist as well. You better become a change management leader as well. You can't outsource this to anybody else. This is a change process that you need to embody and lead as much as other business leaders need to as well." That for me is the 70% part of what lies ahead. I think too many people, too many boards, too many CXOs index on the 30% and not the 70%. I am convinced the 70% is necessary but not sufficient. You have to do that stuff, and you have to become good at it. But you as a leader now have to drive the organizational transformation as well with this. Well, of course, it's much easier to focus on buying technology. Let's buy a digital transformation. There's a great vendor. We pay our money, and they just do it, and it's done. But it fails because it never took inside the company. [Laughter] [Laughter] Right. Exactly. So, as I have interviewed so many business leaders, without a doubt the common theme is just as you've said that the hard part about any kind of transformation (whether it's digital transformation or the kind of next evolution of digital transformation that you're describing), it's always the people. But we have a really interesting question from Twitter. This is from Arsalan Khan. He says, "AI needs business process optimization along with integration of data (inside and outside the organization)." Here's his question. It's a great question. He says, "How do you reach consensus with vendors, partners, even internal departments who are not at the same maturity when it comes to AI adoption? How do you make this happen?" An interesting case study that we should think about doing later is the transformation at Disney. If you think about Disney and Disney+ and how they are actually now beating Netflix at their game is an amazing technology and business model transformation story as well. I had a chance to interview Bob Iger. Last year, I led an effort here at HBS to drive our own digital transformation, and I had a chance to interview Bob Iger, the former CEO and chairman of Disney. He said you don't just ask for buy-in. You demand buy-in. [Laughter] This is Bob Iger, the icon of the entertainment industry and so forth. But he said, "Look. Leaders have to demand buy-in. You can't just say, 'Oh, I need your buy-in.' No, no. 'Hey, you're in or out.'" There's a hard answer, for sure, which is like, you've got to drive buy-in. The second thing I would say is, look, I think, in many ways, we as the people driving the transformation have to become good teachers. We have to make sure that people come along with us. And the way to do that is to take on a teaching role, to take on a learning role for them. That's our job. They won't be able to do it themselves. You have to be taking on the responsibility to say, "How do I show you that A) this is approachable and B) that this is doable?" My great colleague Tsedal Neeley, she's at Harvard Business School too and a professor, she has this great thing called the hearts and minds matrix. You've got to change the hearts, but you've got to change the minds. The minds are changed by training, by learning, by making people see that, yes, I am doable. The minds you do through motivation and by showing the relevance that this has. You have to attack both sides simultaneously. Change the hearts and change the minds, and invest in both of them. Again, that's part of the transformation journey that many companies get stuck at because they don't think about the hearts and minds collectively together. Karim, if we think about the kind of changes that AI and algorithms drive across a company, can you maybe give us some examples? For example, you mentioned the business model. You mentioned relationships with customers. There's talent. There's sales. AI changes relationships across all these different processes. There's been a massive explosion in these diffusion models and large language models. Some analysis shows that the rate of improvement is 10x Moore's Law, 10x Moore's Law in these large language models and in these image-generating diffusion models, and so forth. Somebody showed me this Twitter thing which sort of blew my mind, which was like you can now autogenerate videos saying— Mike, let me ask you. Are you a dog person or a cat person? My wife loves cats, and so the right answer is I'm a cat person – and that's for sure. And is there a particular breed of cat that your wife likes that you have? Oh, we love all cats – and I hope you're listening. We love all cats. All right. Great. Now that we know this about you, we can custom create on-the-fly content for you and your wife that always has cats in our promotional videos at zero marginal cost. Now I'll say, okay, we're going to sell Mike some microphones, but we should have little cats floating by because his wife will see it and say, "Oh, definitely those microphones are more fine than the other ones without the cats." Right? That level of personalization is kind of incredible. The fact that I can now generate, on demand, at zero marginal cost, these videos and fine-tune it to you is kind of mind-blowing. But that capability is here today. That capability is here today. What OpenAI is doing, what Google is doing, what Facebook is doing with these kinds of technologies is mind-blowing. Just think that I can now generate personalized ads for each person, tweaking based on their preferences, changes marketing. How would I run a marketing department now when I can create personalized content at scale for each individual? Think about the marketing supply chain from how ideas get generated, how campaigns get created, to how they get launched, to how they get observed and they get monetized. That whole function with these large language models, both in terms of text creation and in terms of content creation, blown away, blown away and rethought through. One example. I spent a bunch of time with Flagship Pioneering to think about how AI and biology are merging together. The same diffusion models that we see for ad creation can also be applied to creating proteins. The same capability for proteins. Now just think how the R&D process changes because now I can generate any protein I want. In fact, one of the companies that we have in our portfolio that I've been advising is Generate Biomedicines, and their view is that they're creating a platform that can generate any protein, proteins that have actually not even existed in the world before, based on these types of technologies. Just think about the R&D function changing. Now I've looked at two very distinctive settings: the R&D function, which we've always thought requires this creativity and geniuses, massively augmented by AI. But then the marketing supply chain being completely turned upside down and fully automated this way. Now companies that will have access to data about you and your wife and can have permission from you and your wife to use that data to do that kind of marketing will be very differently organized. Companies that have an ad agency, creative department, they take six months to create a new ad. That ad is put on TV or even runs on YouTube but is nondifferentiated and so forth – examples. A cool thing I recently saw on this was in sales. Apparently, now lots of sales, because of the pandemic, a lot of sales moved to Zoom and people are now comfortable with having initial sales conversations on Zoom and so forth. Well, there are toolings that you can add onto Zoom that becomes, like an earpiece here, an earpiece for the salesperson to say, "You're talking too much. Slow down." Live, while you're in the conversation. "Pause for more questions. Ask a question this way. Your tonality seems to be more aggressive. Be softer." Realtime coaching for salespeople as to how to respond to a customer, and that's all AI-driven. Imagine how your sales, your face-to-face sales, process is now changing because you have this technology available. It's really augmenting capabilities that we just have not thought through properly before. That I think is the amazing thing that's ahead of us. For CXOs, then the question becomes, well, where do you begin? Do I start in marketing? Do I start in R&D? Do I start in sales? Do I start in operations? Where do I begin? That's why these guys that you bring on your show get paid the big bucks. That's part of the judgment that they need to have to say, "What are the high-value opportunities for me to start to do this? Then as I begin the transformation, how do I bring everybody else along in this way?" Of course, there are innumerable software companies now who are selling products and each one promises that it will be easier than the next. Yes. "And we all have incredible capabilities because of the data," and blah-blah-blah. We've all heard these sales pitches endlessly. Lisbeth Shaw asks a question on Twitter that is directly related to this. She asks, "How can established companies become AI companies while they run their existing business, because you don't want to go out of business while you're transforming your company?" A thousand percent. Lisbeth has it right, which is that's the biggest challenge. We don't have the luxury to be greenfield. We actually have to transform ourselves. What we've seen is there is a joint top-down and bottom-up approach. Declarations by the C-suite to say, "This is the journey we see ahead for us, and this is the way we need to go towards." You need the C-suite, the CXO buy-in, and belief, and a painting of a vision of what that means. Then what I would say is – that's the first thing – in that vision is, how will my customer value get enhanced; how will my clients be better off if I imagine this world to be? This is part of the top-down strategy around this. Then it's a question of saying, "Okay. Which are the problems that we should go after?" What I would say is it's easy for you to say, "I've got to rebuild everything," and it's like you're never going to rebuild everything. You don't want to be in this world of, like, I'm going to pause for five years and rebuild everything. You want to say, "All right. There are two things I need to do. I need to deliver value but also build capability so that I can do this more and more often and do it along the way." You then look around, either on your business model side or on your operating model side. Again, on value creation and value capture or on scale, scope, and learning. Say, "Where are some high-value problems that if I solve and I demonstrate that these get solved that I can then take that and then scale it across my enterprise?" I start with a prototype. I start with a POC. But the POC doesn't sit by itself. The POC is designed to scale. You say to the folks the green light to the POC that if this works, what is our plan to scale, and you have the plan to scale agreed upon before even the POC starts. What we've seen over and over again is that the POCs actually work. I've seen amazing hit rates for POCs working. But then they all are dead zombie projects in many organizations because there's been no commitment to scale. The commitment to scale then means, "Oh, I've got to change my operating mode, the ways in which I do that," but you need buy-in. It's the bottom-up identification of use cases, bottom-up identification of POCs, top-down agreement that we're going to do this and that, as these POCs start to scale, you prioritize. We will then make them go across the enterprise. The thing I learned from some colleagues – you know I was just spending a bunch of time at Boston Consulting Group (before I became academic), and I've reacquainted with them since I wrote the book – they had some very interesting perspective that oftentimes people get into this prioritization game, like, "Oh, which projects am I going to prioritize?" The reality is, in a top-down transformation, you'll need to do everything. And so, the question is one of sequencing. The sequencing of the projects and the scaling actually has to be based very cleverly on your strategy. Is the strategy to blow away your competition and be the low-cost provider? Then the projects you would do for AI are very different than saying, "I'm going to be number one in customer satisfaction and new business model creation." That's a very different set of perspectives. The use cases get identified at the bottoms-up level. You need top-level agreement that this is the journey they want to go on. But then top-level agreement to say that as these POCs get developed, we're going to sequence them and scale them to meet our strategy. That's the way that these transformations will work. If you think about Disney as an example, where did they start? They first started by buying Pixar. Then they boat anchored Disney Animation Studios to create digital animation. That was 15 years ago, 17 years ago. Then in that journey, they've gone step-by-step to build their own digital capabilities and start to build a platform where then Disney+ launches just before the pandemic and can take advantage of people's home viewing, but then keep going that way by being able to actually beat out Netflix at their own game. It's interesting. Just as you were describing Disney on Twitter, Michelle Batt came in to point out that Disney's success is also related to leadership pushing from the top down. This is Bob Iger saying, you know, demanding buy-in. Lag [Laughter] It reminds me of the great leader of our time Elon Musk going to Twitter and saying, "You will now work 24/7. And, by the way, we're firing half of you today." You may disagree with his personality and his politics and his incessant use of Twitter, but his ability to change the space industry, the auto industry, the electric industry, you know, electrification with SolarCity, you can't— He's done things that we'd be lucky to do in one lifetime. He's done three of them already, and we'll see what he does with Twitter. I'm not a big fan of his management style but, guess what. One of the most important questions he asked at Twitter was, "How many people are writing code that ships versus managing?" It's like, "Oh." I think the ratio was 5:1. He goes, "Okay, that has to change," because, in the end, most of our companies are going to be embedding our processes in software and technology. That's the key thing that I think CXOs have to get their head around that everything we do is going to be embedded through software, through technology, through AI. That's where you have to then make resource allocation decisions and so forth. The technology and the AI is going to augment our humans. It's not going to replace them. It's going to augment them, but the processes you have would have to be very different. We have another really important point from Arsalan Khan. He comes back, and he asks about the bias question. He said, "With data and algorithms—" I'm paraphrasing his question but, essentially, he wants to know. He phrased it really well. He says, "How do we reduce bias in AI when the ultimate goal is increasing profit and not necessarily AI's impact? For example, changes on the workforce or in society." How do we balance these? It's a really important issue. Let's unpack this. One is, why is there a bias problem with AI? Well, because bias can exist because our data that we are using to train the algorithms is not representative. We just have one class of citizens generating the data instead of another class. Our labeling operations may not be representative as well. For example, lots of tech companies have problems identifying blacks in their image processing systems because the labelers weren't able to identify them properly or distinguish the features that way as well. One is a story of data and data operations. This is why data science is a critical skill for all executives because you have to understand the data generation processes and all of the faults that would happen. I think that's the first thing. To take it to the limit, just as I can scale the benefits of AI exponentially, I can also scale the harms of AI exponentially. Bias is one of those things. The second thing is that there is a real legal issue, which has been the thing that has been so interesting for me. Statistically, computer scientists and statisticians, when they look at the algorithms, say, "Is this algorithm fair?" Oftentimes, when we think about fairness in statistics and in computer science, we think about on average. Is this algorithm treating people fairly, on average. But the law doesn't say average. The law says each and every individual has to be treated fairly. There's a lot of risk that companies are facing today because their algorithms are, on average, fair but to the individual they're not fair. They're open to a lot of liability questions. How do we make sure that those things are addressed upfront instead of addressed after the fact? This is where I think is the new frontier for many organizations, which is the conversation about bias and fairness and transparency in expanding the algorithms should not be a computer science or an AI task. This is a cross-functional task that resides with business, with technology, and with legal and policy. This has to be done collectively. Importantly, we can't do this ex-post, after the algorithms have launched. We have to do them pre- in the design phase. I think Satya Nadella has done the most thinking about this because, remember, Microsoft had a crazy amount of cybersecurity issues in the 2000s. What they had to do was retrain their software developers to build quality software and security into their processes instead of doing it ex-post. I think the same thing is going to happen with algorithms and bias and AI is that we have to build in the awareness about bias in our processes upfront instead of ex-post when the algorithms are released into the wild. The example I use is that when you go to Toyota (as a manufacturing company), there's no quality department at Toyota. Why? Because they feel like if your processes aren't creating quality then a quality department is never going to fix it. They make quality the responsibility of all employees, and they've built processes to ensure quality is built into the systems instead of doing it at the end. I think the same thing is going to happen around AI and bias as well. This is from LinkedIn. Cezar Babes comes back, and he responds to you the following way. I'll ask you to just keep your answer pretty brief. He says the following: "Should AI use be more tightly regulated? Every now and then there's a new technology that becomes the catalyst for—" and I'm readying his quote "—profit-driven goose chases. This results in loss of jobs and resentment towards that technological advancement." He says, "It would be great if AI would be a driver for human growth and result in increased capability and capacity rather than right-sizing and cost-efficiencies." Is there going to be displacement because of AI? Absolutely. Do we need to retrain people? 100%. But my belief is that, in the end, AI augments human capability instead of destroying human capability. Just as prior technologies have been enhancing us, the same thing is going to happen with AI as well. Is there a displacement period and are certain occupations going to be displaced? 1000%. That's where governments and so forth have to come together. But regulation, who is going to regulate AI and in what way? It just doesn't seem tenable to us because it is so widespread. What advice do you have for business leaders who are listening to this and saying, "All of this is fine, Professor Lakhani, but my business is successful. We don't have to deal with this stuff. We're pretty much happy as clams, so this doesn't affect us"? Go talk to your customers, and not about your products but other things that they're doing. I tell you; you will be shocked with how they're thinking about the world and how much technology is driving their decisions. When you ask them about your own products or your competitors' products, you will never hear the right answer. Ask them about other things that they're doing in their businesses, and you'll be shocked. What advice do you have for business leaders who are listening to this, nodding their heads, and saying, "I know this is true, everything you're saying. We feel the pain. I feel the pain, and I don't know what to do. It's too big and complex and hard." There is a learning mandate for this for all organizations, which is, we have a generation of leaders that came in the old model. They don't understand the technology, don't understand data science, don't understand statistics, don't understand algorithms, don't understand cloud, and feel like that's for the IT guys. I think there's a learning mandate for these leaders not just for them to become better at this but then to also get their whole organization to change as well. And so, I would start with learning. Invest in the learning for yourselves and your folks. There's so much stuff available. What you have done through this amazing series, what we offer for paid, there's lots of stuff. There's no excuse for not learning. There are lots of books. Invest systematically in learning, yourself, and building a framework for your whole team. Then cascading that down so that everybody has the same reference point. That's the first step. I see too many people shirk on learning and say, "This doesn't apply to me," when they don't know what's going to hit them over the head with this stuff. Jose Kurian just wants to point out that security is the most important of AI and machine learning services, and so can you just say something about the security dimension of all of this? Security, overall, as we get into more digitally intensive organizations, which all of us are becoming, data security, information security is going to be key-key-key for all of us. Secondly, there are actually a bunch of very important issues about data security and our data pipelines being secure and not being tampered with. Just think about labeling operations that many companies have. Many times, those labeling operations are outsourced. They could be subject to attack where even just the slight bit of mislabeling could give you flawed algorithms. As we start thinking about this stuff becoming infused throughout our enterprises, the security side around data itself is going to be massively important. I 100% agree, 1000% agree. With that, we are out of time and over time. I want to say thank you so much to Professor Karim Lakhani from the Harvard Business School. Thank you so much. I'm so grateful for your taking the time to be here with us today. It was so much fun, Mike, and it was great for me to be on this side instead of just listening. Thank you for the invitation again. Well, I hope you'll come back again. Absolutely. A huge thank you to everybody in the audience who watched and especially to the folks who ask such amazing questions. You guys are such a great audience. I have undying respect for you. Everybody, thank you so much. Check out CXOTalk.com. We have amazing shows coming up. Before you go, subscribe to our YouTube channel and hit the subscribe button at the top of our website so we can send you our newsletter and you can stay up to date on these amazing live shows. Thanks so much, everybody. I hope you have a great day. See you soon.

【综合稿件-参考】数字化转型的未来，将是一个指数级的变化 https://www.youtube.com/watch?v=ystdF6jN7hc本篇大纲：1. 创新是唯一的出路 一切照旧已经不够了 在一切都已经很优秀的世界里，创新是进步的唯一途径 2. 技术正在迅速改变我们的工作和生活方式 自动驾驶汽车和计算机学习。 需要新的技能来跟上变化。 3. 在转型时期发现新机遇 颠覆已成为新常态 变化正在呈指数级发生 4. 我们的世界通过先进技术变得更加互联 量子计算、大数据、物联网和人工智能等新兴技术正在推动这种互联性。 这些技术相互放大，创造了一场完美的变革风暴。 5. 不可数字化的技术在未来将狠有价值 创造力、想象力和情感会更重要 机器人和软件只会做可以自动化的工作，人类可以专注于不可自动化的任务 6. 通过拥抱未来和超越技术来拥抱变革 为了取得成功，想象一下通过参与可能性而不仅仅是现在来实现变革。 让自己沉浸在不久的将来，了解其影响。 成功的关键在于人类的洞察力和智慧，而不仅仅是技术和数据。 未来需要一个专注于“为什么”的整体商业模式。 7. 学习必须及时进行，生态系统才是前进的方向 需要全面转型，而不仅仅是改进单个系统 人类通过变革性的经历创造真正的价值 8. 拥抱技术，超越技术 未来是科技 更大的未来在于超越 ——核心词：not good enough is dead从前，一切照旧往往就足够了 现在，我们的未来海生一切照旧的话，就死定了 在一个万物相连的世界里 一切都同样出色 性能达到完美 只有一个空间可以创新 你 现在，你就是 汹涌的变革龙卷风的中心点 由数字化推动 移动化 强化 非中介化 自动化 科幻小说正在变成科学事实 想想看 自动驾驶汽车或能够学习和思考的计算机 和思考的计算机 我们的工作方式将一去不复返 我们需要的技能将大不相同 现在，胜负的发生速度比以往任何时候都快 你将如何应对？ 您将如何发现新 机遇？ 变革时代 人类历史上 你是在推动变革，还是被变革所推动 颠覆已成为变革的新常态 它总是逐渐地突然发生 事情真的不再是逐渐发生的了 这种变化是指数级的 一切 过去笨拙和断线的东西现在都连上了线 和智能化 汽车 城市 港口 农场 甚至人体也将装上传感器 并相互对话 这些游戏变化也是组合性的 它们相互放大 形成一场完美的变革风暴 量子计算助推大数据 物联网 促进人工智能和深度学习 机器人技术 然而，任何无法数字化或自动化的东西 都将变得极其宝贵 人类独有的特质，如创造力 想象力、直觉 情感和道德 在未来将变得更加重要 因为机器擅长模拟 但不擅长成为 是的，机器人和软件将完成我们的部分工作 但这将使我们能够专注于 无法自动化的事情 想象变革 你必须开始更多地参与可能发生的事情 而不仅仅是现在 沉浸于眼前的未来 从今天开始的 5 到 7 年后 我们需要超越技术和数据 达到人类的洞察力 和智慧 技术代表着变革的方式 但人类代表的是为什么 未来关乎整体业务模式 机会在于及时学习的流动性 而不是以防万一 不是单一的改进，而是全面的变革 不是单个系统，而是新的生态系统 人性是创造真正和持久价值的源泉 我们将以事物为载体 因为它们提供的体验 因为它们具有变革的力量 未来不只是发生 未来已经发生 新的工作方式是拥抱技术 但不是成为技术 未来就在技术之中 但更大的未来在于超越科技 让我们从这里开始生活和领导英文原文 once upon a time business as usual was often good enough no more where we are going good enough is dead in a world where everything is connected where everything is equally excellent where performance is reaching perfection there's only one space left to innovate in you right now you are a central point of the raging tornado of change fueled by digitization mobilization augmentation disintermediation automation well the list goes on science fiction is becoming science fact think about self driving cars or computers that can learn and think the way we work will never be the same the skills we need will be dramatically different winning or losing are now happening faster than ever before so what's your response how will you discover new opportunities in one of the most transformational times in human history are you driving change or are you being driven by it disruption has become the new normal with change it's always gradually done suddenly well things really have stopped happening gradually this change is exponential everything that used to be dumb and disconnected is now wired and intelligent cars cities ports farms even how bodies will be wired with sensors and will talk to each other these game changes are also combinatorial they amplify each other creating a perfect storm of change quantum computing fuels big data the internet of things fuels artificial intelligence and deep learning which fields robotics however anything that cannot be digitized or automated will become extremely valuable human only traits such as creativity imagination intuition emotion and ethics will be even more important in the future because machines are very good at simulating but not at being yes robots and software will do some of our work but this will allow us to focus on things that cannot be automated to imagine change you've got to start engaging more with what might be not just with what is immerse yourself in the immediate future 5 to 7 years out from today we need to go beyond technology and data to reach human insights and wisdom technology represents the how of change but humans represent the why the future is about holistic business mode the opportunity is to be liquid to learn just in time not just in case not single improvements but complete transformations not individual systems but new eco systems humanity is where true and lasting value is created we will engage laden by things because of the experiences they provide because of their transformative power the future doesn't just happen the future get happened the new way to work is to embrace technology but not to become it the future is in technology yet the bigger future lies in transcending it let's live and lead from here——GPT整理文字—— Once upon a time, business as usual was often good enough. No more. Where we are going, good enough is dead. In a world where everything is connected, where everything is equally excellent, where performance is reaching perfection, there's only one space left to innovate in – you. Right now, you are a central point of the raging tornado of change, fueled by digitization, mobilization, augmentation, disintermediation, automation – well, the list goes on. Science fiction is becoming science fact. Think about self-driving cars or computers that can learn and think. The way we work will never be the same. The skills we need will be dramatically different. Winning or losing are now happening faster than ever before. So, what's your response? How will you discover new opportunities in one of the most transformational times in human history? Are you driving change, or are you being driven by it?Disruption has become the new normal. With change, it's always gradually done suddenly. Well, things really have stopped happening gradually; this change is exponential. Everything that used to be dumb and disconnected is now wired and intelligent – cars, cities, ports, farms, even how bodies will be wired with sensors and will talk to each other. These game changes are also combinatorial; they amplify each other, creating a perfect storm of change. Quantum computing fuels big data; the Internet of Things fuels artificial intelligence and deep learning, which fields robotics. However, anything that cannot be digitized or automated will become extremely valuable. Human-only traits such as creativity, imagination, intuition, emotion, and ethics will be even more important in the future because machines are very good at simulating but not at being.Yes, robots and software will do some of our work, but this will allow us to focus on things that cannot be automated. To imagine change, you've got to start engaging more with what might be, not just with what is. Immerse yourself in the immediate future, 5 to 7 years out from today. We need to go beyond technology and data to reach human insights and wisdom. Technology represents the how of change, but humans represent the why. The future is about a holistic business mode; the opportunity is to be liquid, to learn just in time, not just in case. Not single improvements but complete transformations, not individual systems but new ecosystems. Humanity is where true and lasting value is created. We will engage laden by things because of the experiences they provide, because of their transformative power.The future doesn't just happen; the future gets happened. The new way to work is to embrace technology but not to become it. The future is in technology, yet the bigger future lies in transcending it. Let's live and lead from here.—————不错的评论————— Indeed time to focus on Creativity, Imagination, Intuition, Emotion, Ethics 是时候专注于创造力、想象力、直觉、情感、道德

如何提升运气01：是不是真的可以锻炼好运气 说起运气啊这本身呢就是一件非常不可思议的事情，因为即便是这个科技再先进 也探测不到运气的存在，但是数千年来呢我们每一个人都会讨论他。比方说你最近的运气不错哦，或者最近很霉，感觉做啥都不顺 总之啊，他出现在我们日常生活当中的频率很高。被我们挂在嘴边，但是他看不见摸不着，但是就有这么一本书，却叫做《好运气是锻炼出来的》所以先说结论，运气是存在的，而且是可以被锻炼出来的。我们回到这本书，他的作者是田坂广志，日本人，30岁进入了三菱集团，38岁成为了日本总和研究所共同的创办人之一，这个机构专门解决一些社会问题，后来还担任了日本原子能委员会的委员，所以这个人是一个科学素养绝对强的人。而且2011年311地震后，他被日本的首相钦点为国策顾问。好，就是这么一个人来说运气，他说运气啊，从科学上来看，确实是无法验证的，他呢并非宗教人士，也不算什么神秘主义者。但是呢，他宠她过往即使难的经历来看，运气的确存在，他说在他三十八九岁的时候，发生过一件事情，当事他还是一个在银行上班的小伙，他的上司就是一个强运之人。为什么这么说了，他说在二战期间，他的舰船被击沉，他在船上漂浮呢几天几夜，最后奇迹般地获救了。在一次闲下来闲聊的时候，田坂广志呢就问这个会长，您会派一些干部去帮助一些客户重建他们的企业，您是怎么挑人的哈。会长轻描淡写地说了一句。就挑那种强运的人啊，广志就更纳闷了，您怎么分辨谁是强运之人哈。会长呢笑而不语。后来广志跟会长接触多了，他发现一个真正经历过强运加身的人，就能够敏锐地发现别人身上的运气。 而且他在日常中还发现，不同的人的身上的这个运气确实存在强弱之分，那些影响力非常大的政治人物或者是企业家，甚至是运动员，强运都是非常重要的因素 ，甚至是最关键最极致的力量。以运动员们为例，麦迪的35秒奇迹，足球欧冠的伊斯坦布尔奇迹，技巧跟实力当然非常重要，但是在顶尖运动员之间，运气可能就是那决定胜败的点。可能再引申的话，例如修仙小说中的器运，道门仙家的契机等等，就不引申了。那如何获得好运呢，其实总原则只有一条，那就是内心的状态，会招来与之共鸣的事物，也就是要保持正面想法。 佛家也有类似的说法：三界为心，万法为实。 佛家认为，眼前的一切皆出于自己的内心所见，因此要招来好运，说到底就这么一个方法，既然好运决定于你的这个内心状态。那就要谈到深层次的意识 他说啊人其实共有五种意识。今天的内容，我们先告一段落，明天我们会接着讲《如何获得好运》，感谢大家的观看。

【讲稿】a framework for digital transformation 数字化转型框架 麦肯锡最近发布了一篇题为《speed and scale》的文章。速度与规模释放客户旅程中的数字价值。我的目的是加强这篇文章的内容，并提供一页蓝图，展现有效的和可扩展的数字化转型。如果我们说一家以信息为中心的传统公司，如旅行社、金融或保险公司为例。这样的公司有许多内部要素，包括人员、流程、数据、应用程序、组织和文化，以及供应商。这些要素相互影响，从而为客户提供价值。以往，外部变革的步伐一直很缓慢，因此构成公司的要素不必变化那么快。现在，环境已大为不同。今天，客户可能通过多种渠道与公司互动，如在线和通过代理商。他们正在寻找简单无缝的方式与公司互动，例如，当客户使用音乐下载服务时，他希望搜索购买、下载，并在几秒钟内收听自己选择的歌曲。如果这种互动变得痛苦，他就会迅速转向其他公司提供更好体验的公司。客户旅程是企业与客户互动的机制。为了实现有效的业务转型，我们需要关注这种旅程，同时考虑到整个生态系统。在设计数字功能时，要考虑到所有旅程是非常困难的。最好的办法是首先选择一部分旅程作为起点，选择哪些旅程取决于旅程对客户体验的重要性，并可能基于高流量或高影响等标准。更重要的是，它们必须符合公司的数字化愿景。一旦选定了一小部分旅程，必须对它们进行建模，以便对它们进行重新设计和数字化。我们不应试图简单地将现有旅程数字化，而是要研究如何改进旅程。例如，您可能希望减少步骤的数量，旅程中的每一步可能都侧重于某些方面。在一个步骤中，品牌可能很重要，而速度可能在另一个步骤中很重要。旅程中的每个步骤都应当模块化为业务服务，可在多个地方重复使用。以客户购买保险为例，其中一个步骤要求客户以数字方式签署接受书，这样的业务服务可在多个地方重复使用，跨越多个旅程。这意味着基础架构必须以模块化方式构建，以便重复使用。当我们设计和部署越来越多的旅程时，这种可重复使用的业务服务库将继续增长。随着环境的变化，业务服务将不断更新，以满足生态系统的新要求。随着时间的推移，电子签名可能会被视网膜扫描所取代。更新单一业务服务可确保自动传播到所有使用电子签名的地方。这种模块化的灵活性和可重新配置性使组织能够继续适应不断变化的业务环境。这些业务服务反过来又得到应用程序和技术基础设施的支持，必须加以整合。然而，通过将技术与其提供的业务服务隔离开来，就有可能逐步更换某些技术模块，而不是彻底改造整个基础设施。所有这些都是现实，我们需要架构模型来代表现实，这样我们就更容易理解和处理现实。架构代表不同类型的实体，如人员、流程、技术、应用程序、数据等，以及这些实体之间的关系。现实的变化可以通过架构的变化来传达，架构反过来也必须受到管理，对变化进行预算和规划。数字化转型的总体规划还包括其他方面，如变革管理、人才管理以及持续适应机制。变革管理贯穿整个组织，实现企业文化的重大转变。必须对员工现有的专业知识和技能进行升级，以满足组织的数字化需求。帮助员工提高数字化知识的最有效方法之一是将他们组成工作小组，工作小组由来自不同领域的人员组成，在新技术和新方法出现时将其输入团队，使其不断成熟。转型之旅永无止境，不断发展，适应不断变化的业务和客户需求。随着转型的展开，衡量标准和关键指标不一定适用了，必须制定和监控新的衡量标准，为领导者决策提供实时仪表板，并对组织进行微调。生态系统对业务的所有影响周而复始。本蓝图并不全面，旨在提供一个概念，一些在数字化转型过程中必须考虑的事项。每个步骤都可以进一步扩展到多个细节层面，以便高效执行。还可以在此蓝图中添加更多元素，这取决于需求。