AI-centric organizations exhibit a new operating architecture, redefining how they create, capture, share, and deliver value.
Now with a new preface that explores how the coronavirus crisis compelled organizations such as Massachusetts General Hospital, Verizon, and IKEA to transform themselves with remarkable speed, Marco Iansiti and Karim R. Lakhani show how reinventing the firm around data, analytics, and AI removes traditional constraints on scale, scope, and learning that have restricted business growth for hundreds of years. From Airbnb to Ant Financial, Microsoft to Amazon, research shows how AI-driven processes are vastly more scalable than traditional processes, allow massive scope increase, enabling companies to straddle industry boundaries, and create powerful opportunities for learning—to drive ever more accurate, complex, and sophisticated predictions.
When traditional operating constraints are removed, strategy becomes a whole new game, one whose rules and likely outcomes this book will make clear. Iansiti and
Present a framework for rethinking business and operating modelsExplain how "collisions" between AI-driven/digital and traditional/analog firms are reshaping competition, altering the structure of our economy, and forcing traditional companies to rearchitect their operating modelsExplain the opportunities and risks created by digital firmsDescribe the new challenges and responsibilities for the leaders of both digital and traditional firms
Packed with examples—including many from the most powerful and innovative global, AI-driven competitors—and based on research in hundreds of firms across many sectors, this is your essential guide for rethinking how your firm competes and operates in the era of AI.
I think if you had been in a coma for the past 25 years and you just woke up today saying, “What could I read to learn the bare minimum about technology in order to fake my way through a job interview”. This book would be helpful educating you about what has been going on at the intersection of business and technology. Other than that you’re probably gonna be frustrated to hear the same tired examples and pro-tech cliches you’ve heard in other books.
I briefly forgot why I stopped reading “tech-business” books. So thanks to this book for reminding me why. As a counterbalance, everyone who reads this book should be required to read The Age of Surveillance Capitalism by Shoshana Zuboff.
Are there still people working in technology who don’t know what economies of scale are? The authors multiple times misconstrue “algorithms“ with simple data science you could perform in an Excel document. I get the impression that neither author has written a line of code in their lives. Much of this book reads like a business consultant desperately trying to remain relevant in a domain they don’t actually understand.
“Technology first,” no shit, thanks, I was going to do my accounting on clay tablets. Does anybody have any papyrus I could use instead?
“Data is the lifeblood of an organization“ if your business is not collecting information to help you optimize business processes and customer interactions, you should be fired for breach of fiduciary duty. The only businesses getting away with not using technology are roofing installers in Florida after hurricane season.
As such, I found this book to be mostly a miss. It trots out the same tired examples you’ve heard before in various case studies. The analysis is what you would expect from your average professor who doesnt have tenure and doesn’t want to take risks. And there’s no real depth to the examples provided. Saying ‘Netflix used algorithms to help make the decision to produce House of Cards’ informs no one about the thought process or systems that went into the series of decisions that resulted in that hit show.
Ultimately this results in a book that doesn’t say anything of consequence and at times obscures the truth. As when the author talks about “multi homing,” the process of when two platforms compete for users and workers, and how most platforms want to reduce this. The example given being Uber and Lyft. You should call a toad a toad. Most platforms seek to find a Monopoly in the market so that they don’t have to compete on any level. The general tech model currently is to subsidize unprofitable competition with venture capital or even IPO money at this point. Once you have the stranglehold on the market raise prices and drastically Cut extraneous costs to increase your profitability. Don’t weasel around and make up names for something that already exists. At least Peter Thiel is honest when he talks about it.
As far as the other examples, Is rehashing how unexpected the market penetration of Alipay was teaching us anything other than “Chinese Venmo go varoom?” All the examples herein talked about, are surface level and fail to really matter or teach anything. Microsoft started focusing on cloud first after they saw the profitability of AWS and they like money. They’re empty talking points. We’ve heard it all before. Technology is powerful. Those who leverage technology effectively will out compete those who don’t. There. I saved you an afternoon reading this. Time is money, you can Venmo me compensation of whatever your hourly rate is. DM me for the details.
Real interesting organizations that are actually doing some cutting edge stuff are ignored, I’m thinking of Square as an example of a company that leverages financial data well for small businesses. Plantier as a company that is so far ahead in the data science realm that much of their research is classified. As Harvard professors how hard would it be to call up Jack or Pete and spend a couple months talking to people in order to actually learn something useful?
This is the second decidedly mediocre book I’ve read in the last couple months from HBS professors. Where “Reimagining Capitalism“ made me angry, this book was mostly mildly annoying. Like your boss using too much corporate jargon during a presentation. “Great Synergy! Everyone work together! Q2 productivity is raising” Starting to get the impression they aren’t doing anything that matters over there. Go to Sloan to learn something, Stanford or Yale for the networking, and Harvard for the empty platitudes.
Frankly? This is all hype, bullshit, dystopia waiting to happen or something else. But definitely not strategy and leadership and other stuff.
Frankly, most of the stuff mentioned is about 1 or more of the following things: - creative financing (uh-huh, let's invent how to get people to take more loans, use all kinds of trackers on themselves, start 'needing' new types of loans... etc, - targeted adds, more adds, marketing, more marketing, a torrential flood of marketing, sales upping (whenever they are gonna stuff up somewhere all those products people don't really need), - delivery, - social stuff (why the heck do people need algorithms to socialize now?), - the cybersec taggle (so, you invent new tech and anti-tech and anti-anti and so on goes the unending recursion), - delegating hiring decisions to algorithms (yay, woment to secretaries - nice sexist approach, RobHR!), - other new age blah-blah. Yawnworthy. Frankly, for this hoopla they definiely shouldn't have bother with all the tech. It's a good thing that maybe something useful will
A bit too high-level. What exactly do we do about algo-bias and other trash going amok with the so-called AI, which is still comparatively harmless?
Time to review another book on my MBA summer reading list! I studied a bit about IoT during my undergraduate days, so this book on artificial intelligence (AI) sounded very interesting.
Competing in the Age of AI is based on one premise: that “AI is becoming the universal engine of execution” and as such is “becoming the new operational foundation of business – the core of a company’s operating model, defining how the company drives the execution of tasks.” Because this is a fundamental shift in business operating models, the authors say that companies must learn how to adapt.
While I thought this book was a good refresher on the potential impact of AI on businesses, the book does seem a bit confused about who its target audience is. Some basic concepts, such as disintermediation caused by technology and the impact of network effects are explained in detail, but others, like what the authors mean by AI, are barely touched upon. Despite this being a book dedicated to artificial intelligence, the authors don’t spend more than a paragraph defining what they mean when they refer to AI. At the start of the book, they reference that weak AI (by which they mean AI that can “perform tasks that were traditional performed by human beings”) is enough to make a huge impact, but the book also refers to unsupervised and reinforcement learning, as well as using AI to make predictions, which sounds more like strong AI to me.
If this book is aimed at business people who are unfamiliar with the concept of AI and are likely to think of it as just another buzzword, it may have been beneficial for the book to spend some time explaining what they mean by AI. It would also help if the writers made a clearer distinction between AI and machine learning, because the two terms seem to be used almost interchangeably.
Putting aside the lack of clear definitions, I thought the middle sections of the book, on the AI factory, rearchitecting the firm, and becoming an AI company were interesting. The book looks at Amazon and Microsoft’s transformations to show how established firms could transform their businesses and why they might want to do so and I found those sections to be interesting.
And as you might imagine from the examples above, this book focuses on giant (often tech) companies. The only example I saw of AI being used on a smaller scale was when the authors created an AI factory to help map out lung cancer tumours from CT scans. But what about the applications of AI for smaller businesses, which may not have the capital to make large investments into technology or may not know if they are collecting enough data for AI to have an impact? It does feel like most AI/ML/IoT projects are focused on making big companies bigger, rather than helping smaller firms compete (the exception I have heard of is that Industrie 4.0 was originally meant to help SMEs, but when I was studying it, it hadn’t moved past the big firms either).
Overall, this is an interesting book and people who are looking at the business impact of recent technology developments will probably want to read this. Since Competing in the Age of AI was published in early 2020, most of the case studies are still relevant – I think the only thing I noticed was the absence of Jack Ma’s disappearance (and the implications of government interference in this area) from all discussions of Ant Financials and Alibaba.
While this book is timely, important, and very relevant to a range of processes and occupations facing change as Artificial Intelligence (AI) becomes both more common and prevalent; I must caveat this is not an easy read. The language, to include technical terms, are straightforward enough. Where this book will challenge readers is with its ponderous narrative. It is definitely written in an academic style, but unfortunately, it is an academic style that conveys what needs said without any consideration of maintaining reader interest or memorability. I really believe the authors would have had a best-seller if they found someone who could have (or still could) help them to rewrite this work in more of a narrative style. Even with only a few illustrations or case studies tied together to build the narrative upon, I think the impact and memorability of this book would increase significantly. With that said, I highly recommend this book for anyone engaged in military or business pursuits since AI is currently beginning to reshape these fields the most, although eventually AI's impacts will be felt throughout society.
This book was Stevo's Business Book of the Week for the week of 11/5, as selected by Stevo's Book Reviews on the Internet and Stevo's Novel Ideas. AI removes traditional constraints on scale, scope, and #learning.
أسلوب هذا الكتاب سيئ جداً، بحيث أنه يبدو وكأن مؤلفيه هواة لا أساتذة جامعيون في هارفارد، فهم يعشقون الكلمات المُنمَّقة والبرَّاقة التي يحشون كتابهم بالآلاف منها، دون أي اعتبار لمعناها الفعلي. أدناه أمثلة من جمل شبه عشوائية كادت تدفعني إلى رمي هذا الكتاب في أقرب حاويةٍ لولا أنه كان مقرَّراً عليَّ في الجامعة:
"AI is becoming the the universal engine of execution." "AI is becoming the new operational foundation of business." "Having software shape the critical path of operational execution has substantial ramifications."
جوهر هذا الكتاب هو ما تراه أعلاه من تكرار لا نهائي للأفكار والأمثلة والمصطلحات (وخصوصاً الصّفات المبهرة مثل "عالمي" و"جديد" و"كبير") ، وأزعم أن تلخيص كل فصلٍ منه في صفحة إلى صفحتين لن يُضيِّع شيئاً من محتواه. الفكرة الجوهرية بسيطة جداً، وهي أن كل "التحول التقني" و"مواكبة الذكاء الاصطناعي" التي يهذر بها المؤلّفون مراراً وتكراراً هي عبارةٌ عن جمع للبيانات وتوظيفها في بناء نماذج للذكاء الاصطناعي. لكي تُحقِّفَ ذلك، عليك أن تجمع البيانات المبعثرة في أقسام شركتك أو مؤسّستك في مكانٍ واحد ("مستودع بيانات مركزي")، وأن تبني "شبكة" من العملاء أو المستخدمين، وذلك لكي تجمع بيانات أكثر من هؤلاء المستخدمين وبالتالي تبني نماذج ذكاء اصطناعي أذكى وبالتالي تربح مالاً أكثر وبالتالي تسيطر على العالم.
هاك، ل��َّصتُ لك 300 صفحة في ثلاثة سطور. على الرَّحب والسِّعَة.
Marco Iansiti is a Professor of Business Administration at Harvard Business School. His special expertise revolves around Technology and Operations Management. He advises Blue Chip companies globally on operational transition, and technological transformation for the 21st Century. Iansiti and Lakhani posit workable solutions and invaluable insights into the infinite utility of AI.
Iansiti is a prolific author of publications based on a particular area of expertise. Some of his works include: Digital Ubiquity, The Truth About Blockchain (Iansiti & Lakhani), The Keystone Advantage (Roy Levine), and Managing Our Hub Economy. Those looking for advice on establishing digital advantage or operational model transformation of a global organization need look no further than Competing in The Age of AI.
Karim R. Lakhani is a Business Administration Professor at Harvard Business School. Lakhani is the co-director of the Laboratory of Innovation Science at Harvard’s Institute of Quantitative Social Science, as well as the Chair of the Harvard Business School’s Analytics Program. His area of expertise is innovation and technology management. He is the author of numerous articles and case studies on technology, digital commerce, and digital innovation. He has been published in a myriad of significant publications that include: The Economist, The Wall Street Journal, and Business Week among many others.
Who is the target audience?
This is probably the most important book on business application of digital innovations this decade. This thesis is suitable for those who want or need to understand the potential for the increased span, scope, and scale afforded by the appropriate utilization of digital innovations, particularly artificial intelligence as applied to business models. Iansiti and Lakhani’s concept provides a most important tool for Captains of industry, investors, CEOs, entrepreneurs, and students of business and technology. Iansiti and Lakhani’s publication is a must read for all those who want to improve their understanding of the application of AI in organizations. Competing in the Age of AI should be compulsory reading for all those involved in leveraging competitive advantage in the new business world underpinned by artificial intelligence.
Synopsis
The discussions herein include the question of ethics in application and distribution of technology. Iansiti and Lakhani’s have provided a map for the exploitation of the technical advances provided by new technology. The authors have given practical advice on the strengths, limitations, and challenges of employing artificial intelligence to support and augment a company’s strategy.
Conclusion
Competing in The Age of AI is a seminal work, containing all the key ingredients for global companies to explore in order to improve competitive advantage. Iansiti and Lakhani have provided sensible, practical jargon-free explanations for the application of advanced technology strategies and advice on the potential effects on span, scope, and scale across the organization.
Acknowledgment
My sincere thanks go to: NetGalley, and Harvard Business Review Press for affording me the opportunity to review of Competing in the Age of AI.
Understanding the role of artificial intelligence in shaping the future of organizations is one of the hot topics nowadays. Increasingly we see the traditional industry boundaries dissolves as companies like Amazon, Alibaba, Facebook, and Airbnb venture out to activities other than what they originally set into.
For example Amazon who started as online bookseller now has Alexa who can control homes and does task traditionally done by personal assistants. It looks like information technology is becoming a central hub of every other traditional industries.
Now this book provides the concepts every executive needs to understand in order to be successful not just within their respective organizations but how they deal with other players in other industries.
The book is easy to read, well written, and provides lots of case studies readers can use to illicit ideas that will help them compete with other firms.
The world of business is always changing. Today, artificial intelligence, machine learning, blockchain technology increasingly disrupts every aspect of human endeavor. This book will help your organization to change your frameworks on both business and operational model; and stay relevant in the face of digital competition.
Fresh angle on how the competitive landscape will change when AI will transform business into platforms connecting customers with services. Nice manual, as well, to adapt internal operations.
Me siento bastante ambivalente con este libro porque siento que da algunos consejos valiosos y establece algunas cosas que creo que deben ser conocidas por la mayoría de los empresarios, gerentes o directivos, pero al mismo tiempo siento que está lleno de paja de negocios que a personas técnicas no les ayuda mucho.
Me quedo con algunas partes que me parece importante recordar de vez en cuando:
1. Las compañías modernas deben estar familiarizadas con la tecnología digital y aprovecharla al máximo porque es parte de lo que deben hacer como su centro: desarrollar tecnología que atienda a sus clientes. 2. Las cosas están cambiando y el cambio sólo se va acelerar, separando a las personas/empresas que aprovechan y desarrollan tecnología de las que no 3. Capturar y usar datos es increíblemente importante
Por otro lado, siento que el libro al ser escrito por personas de negocios y no de tecnología, tiene algunas fallas fundamentales, por ejemplo, que el costo de atender un cliente más mediante tecnología digital siempre es negligible. Esto es cierto hasta que se llega a un punto en el que sistema tiene que volver a ser desarrollado por que no soporta la operación actual, lo cuál no es nada barato y sí puede que el costo de atender a un cliente individual sea cerca de cero, pero atenderlos a todos (y a veces, como lo resalta el libro, los negocios tienen valor por el volumen de clientes que tienen) tiene un costo muy alto.
Sinceramente pasé rápido por gran parte de este libro porque detecté que no me aportaría mucho, pero me alegra por lo menos haber sacado algo.
Hype, Hype and more Hype :) The main problem that most of the stuff they describe is not related to "AI" or even "ML" but just good old software :) But "digital" is so 2000ish now. Moreover, they either don't understand or don't care that calling any DNN model "algorithm" is really misleading as it's just a lot of weights -there are no way to really describe it logically. There are some interesting ideas regarding competing (e.g. while Uber has a lot of competitors, as their business is clustered around cities, so they can't leverage their global scale), interesting description of Satya' activities @Microsoft.
In Competing in the Age of AI, Authors Marco Iansiti and Karim R. Lakhani argue that reinventing a firm around data, analytics, and AI removes traditional constraints on the scale, scope, and learning that have restricted business growth for hundreds of years. From Airbnb to Ant Financial, Microsoft to Amazon, research shows how AI-driven processes are vastly more scalable than traditional processes, allow massive scope increase, enabling companies to straddle industry boundaries, and create powerful opportunities for learning—to drive ever more accurate, complex, and sophisticated predictions.
The book describes the profound implications of artificial intelligence for business. It is transforming the very nature of companies—how they operate and how they compete. When a business is driven by AI, software instructions and algorithms make up the critical path in the way the firm delivers value. This is the “runtime”—the environment that shapes the execution of all processes.
Favourite Takeaways – Competing in the Age of AI
Transformation is about more than technology; it’s about the need to become a different kind of company. Confronting this threat does not involve spinning off an online business, putting a laboratory in Silicon Valley, or creating a digital business unit. Rather, it involves a much deeper and more general challenge: Rearchitecting how the firm works and changing the way it gathers and uses data, reacts to information, makes operating decisions, and executes operating tasks.
AI DIsruption
AI is the “runtime” that is going to shape all of what we do.—Satya Nadella, Microsoft CEO”
AI is becoming the universal engine of execution. As digital technology increasingly shapes “all of what we do” and enables a rapidly growing number of tasks and processes, AI is becoming the new operational foundation of business—the core of a company’s operating model, defining how the company drives the execution of tasks. AI is not only displacing human activity, it is changing the very concept of the firm.
As such, the first truly dramatic implications of artificial intelligence may be less a function of simulating human nature and more a function of transforming the nature of organizations and the ways they shape the world around us.
The Challenge Ahead
AI can render skills and talents obsolete, from driving a car to managing a traditional retail establishment. Digital networks can alter and transform accepted approaches to social and political interaction, from dating to voting. The broad deployment of AI could threaten millions of jobs in the United States alone. And beyond the erosion of capability, threats to traditional skills, and other direct economic and social impact, we are increasingly vulnerable as an increasing portion of our economy and our very lives become embedded in digital networks.
Rethinking the Firm
Ant Financial employs fewer than ten thousand people to serve more than 700 million customers with a broad scope of services. By comparison, Bank of America, founded in 1924, employs 209,000 people to serve 67 million customers with a more limited array of offerings. Ant Financial is just a different breed.
Business and Operating Models
The value of a firm is shaped by two concepts. The first is the firm’s business model, defined as the way the firm promises to create and capture value. The second is the firm’s operating model, defined as the way the firm delivers the value to its customers.
The AI factory
The AI factory is the scalable decision engine that powers the digital operating model of the twenty-first-century firm. Managerial decisions are increasingly embedded in software, which digitizes many processes that have traditionally been carried out by employees.
Experience from Netflix and other leading firms underlines the importance of a few essential AI factory component
Data pipeline:
This process gathers, inputs, cleans, integrates, processes, and safeguards data in a systematic, sustainable, and scalable way.
Algorithm development:
The algorithms generate predictions about future states or actions of the business. These algorithms and predictions are the beating heart of the digital firm, driving its most critical operating activities.
Experimentation platform:
This is the mechanism through which hypotheses regarding new prediction and decision algorithms are tested to ensure that changes suggested are having the intended (causal) effect.
Software infrastructure:
These systems embed the pipeline in a consistent and componentized software and computing infrastructure, and connect it as needed and appropriate to internal and external users.
Capability Foundations
The most obvious challenge in building an AI-centered firm is to grow a deep foundation of capability in software, data sciences, and advanced analytics. Naturally, building this foundation will take time, but much can be done with a small number of motivated, knowledgeable people.
Network vs Learning Effect
Network effects describe the value added by increasing the number of connections within and across networks, such as the value to a Facebook user of having connections with a large number of friends, or access to a broad variety of developer applications.
The most important value creation dynamic of a digital operating model is its network effects. The basic definition of a network effect is that the underlying value or utility of a product or service increases as the number of users utilizing the service increases.
Learning effects capture the value added by increasing the amount of data flowing through the same networks—for example, data that may be used to power AI to learn about and improve the user experience or to better target advertisers.
Multihoming
The first and most important force shaping value capture is multihoming. Multihoming refers to the viability of competitive alternatives, specifically to situations wherein users or service providers in a network can form ties with multiple platforms or hub firms (“homes”) at the same time. If a network hub faces competition from another hub connecting to a network in a similar way, the first network hub’s ability to capture value from the network will be challenged, especially if the switching costs are low enough for users to easily use either hub.
Disintermediation
Disintermediation, wherein nodes in a network can easily bypass the firm to connect directly, can also be a significant problem for capturing value. From Homejoy to TaskRabbit—that provides only a connection between network participants. After the first connection is made, most if not all of the value created is delivered, and it’s difficult to hold a user accountable to the network hub for ongoing rents.
Network Bridging
Network bridging involves making new connections across previously separate economic networks, making use of more-favorable competitive dynamics and different willingness to pay. Network participants can improve their ability to both create and capture value when they connect to multiple networks, bridging among them to build important synergies.
Strategic Collisions
A collision occurs when a firm with a digital operating model targets an application (or use case) that has traditionally been served by a more conventional firm. Because digital operating models are characterized by different scale, scope, and learning dynamics from those of traditional firms, collisions can completely transform industries and reshape the nature of competitive advantage.
Conclusion
We live in an important moment in the history of our economy and society. As digital networks and AI increasingly capture our world, we are seeing a fundamental transformation in the nature of firms. This removes historical constraints on scale, scope, and learning and creates both enormous opportunity and extraordinary turbulence. But despite all this newfound digital automation, it seems that we can’t quite do away with management just yet.
The challenges are just too great, too complex, and too amorphous to be solved by technology (or technologists) alone. But leading through these changing times will require a new kind of managerial wisdom, to steer organizations from full-scale firms to new ventures, and from regulatory institutions to communit
In his book, The Innovator's Dilemma, The concept of architectural inertia – the resistance to adaptation-, informs the late 𝐏𝐫𝐨𝐟𝐞𝐬𝐬𝐨𝐫 𝐂𝐥𝐚𝐲𝐭𝐨𝐧 𝐂𝐡𝐫𝐢𝐬𝐭𝐞𝐧𝐬𝐞𝐧's disruption theory. According to disruption theory, it is the architectural inertia established by the links with existing customers that prevents an organization from responding effectively to disruptive change.
As I picked up this book, 𝐂𝐨𝐦𝐩𝐞𝐭𝐢𝐧𝐠 𝐢𝐧 𝐭𝐡𝐞 𝐀𝐠𝐞 𝐨𝐟 𝐀𝐈: 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲 𝐚𝐧𝐝 𝐋𝐞𝐚𝐝𝐞𝐫𝐬𝐡𝐢𝐩 𝐖𝐡𝐞𝐧 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 𝐚𝐧𝐝 𝐍𝐞𝐭𝐰𝐨𝐫𝐤𝐬 𝐑𝐮𝐧 𝐭𝐡𝐞 𝐖𝐨𝐫𝐥𝐝, knowing that academicians wrote it with a title focused on one aspect of the Artificial Intelligence Revolution, I am pleased to report that this book breaks the Mold and is my best book, 2024.
After 𝐓𝐡𝐞 𝐈𝐧𝐧𝐨𝐯𝐚𝐭𝐨𝐫'𝐬 𝐃𝐢𝐥𝐞𝐦𝐦𝐚 𝐛𝐲 𝐭𝐡𝐞 𝐥𝐚𝐭𝐞 𝐏𝐫𝐨𝐟𝐞𝐬𝐬𝐨𝐫 𝐂𝐥𝐚𝐲𝐭𝐨𝐧 𝐌, 𝐂𝐡𝐫𝐢𝐬𝐭𝐞𝐧𝐬𝐞𝐧, this is the second book I have encountered on how the technology revolutions of the past several decades are disrupting businesses and what they mean for strategy development.
The authors, 𝐌𝐚𝐫𝐜𝐨 𝐈𝐚𝐧𝐬𝐢𝐭𝐢 𝐚𝐧𝐝 𝐊𝐚𝐫𝐢𝐦 𝐑. 𝐋𝐚𝐤𝐡𝐚𝐧𝐢 start by talking about Artificial Intelligence (AI). Still, they quickly acknowledge that this current transformational technology builds on a series of technology-driven transformations going back to the invention of the transistor.
They are wonderful storytellers, weaving through their narrative historical perspective and modern case studies to help us understand the lessons they are teaching.
…when an activity is digitized (like converting a paint stroke into pixels), profound changes take place. A digital representation is infinitely scalable — it is now possible to communicate the pattern easily and perfectly it represents, replicate it, and transmit it at virtually zero marginal cost to a nearly infinite number of recipients, anywhere in the world. Moreover, digitizing the activity makes it easily connectable, also at zero marginal cost, to limitless other, complementary activities, dramatically increasing its scope. Finally, the digital activity can embed processing instructions — AI algorithms that shape behavior and enable a variety of possible paths and responses. This logic can learn as it processes data, continuously training and improving the algorithms that are embedded in it. The digital representation of human activity can thus learn and improve itself in ways that analog processes cannot. These factors completely transform the ways a firm can (and should) operate.
Traditionally, the intrinsic scalability, scope amplification, and learning potential of technology were limited by the operating architecture of the organizations in which it was deployed. But over the past decade, we have seen the emergence of firms that are designed and architected to release the full potential of digital networks, data, algorithms, and AI. Indeed, the more a firm is designed to optimize the impact of digitization, the greater its potential for scale, scope, and learning embedded in its operating model — and the more value it can create and capture….
That’s it. Those two (partial) paragraphs capture what this book teaches.
𝐂𝐡𝐚𝐩𝐭𝐞𝐫 2 focuses on the firm. Going back to the definition of the firm, business models, and operating models, the authors explain how digital technology changes the nature of scale, scope, and learning.
𝐂𝐡𝐚𝐩𝐭𝐞𝐫 3 𝐩𝐫𝐨𝐯𝐢𝐝𝐞𝐬 𝐚 𝐥𝐚𝐲𝐦𝐚𝐧'𝐬 𝐢𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐭𝐨 𝐰𝐡𝐚𝐭 𝐭𝐡𝐞 𝐚𝐮𝐭𝐡𝐨𝐫𝐬 𝐜𝐚𝐥𝐥 "𝐭𝐡𝐞 𝐀𝐈 𝐟𝐚𝐜𝐭𝐨𝐫𝐲." They describe this factory as treating decision-making as an industrial process that creates a virtuous cycle between user engagement, data collection, algorithm design, prediction, and improvement, leading to more usage (and more data).
Similar to various definitions of the building blocks of the Connected Intelligence Revolution, the authors identify four components of the AI factory: the data pipeline, algorithm development, an experimentation platform, and software infrastructure. They then teach us about each of these four components, using the example of Netflix to help us understand how the pieces work together to improve the customer experience.
𝐓𝐡𝐞 𝐀𝐈 𝐟𝐚𝐜𝐭𝐨𝐫𝐲 𝐓𝐡𝐞 𝐀𝐈 𝐟𝐚𝐜𝐭𝐨𝐫𝐲 is the scalable decision engine that powers the digital operating model of the twenty-first-century firm. Managerial decisions are increasingly embedded in software, which digitizes many processes that have traditionally been carried out by employees.
𝐃𝐚𝐭𝐚 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞: This process gathers, inputs, cleans, integrates, processes, and safeguards data in a systematic, sustainable, and scalable way.
𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭: The algorithms generate predictions about future states or actions of the business. These algorithms and predictions are the beating heart of the digital firm, driving its most critical operating activities.
𝐄𝐱𝐩𝐞𝐫𝐢𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 𝐩𝐥𝐚𝐭𝐟𝐨𝐫𝐦: This is the mechanism through which hypotheses regarding new prediction and decision algorithms are tested to ensure that the changes suggested have the intended (causal) effect.
𝐒𝐨𝐟𝐭𝐰𝐚𝐫𝐞 𝐢𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞: These systems embed the pipeline in a consistent and componentized software and computing infrastructure and connect it as needed and appropriate to internal and external users.
𝐂𝐡𝐚𝐩𝐭𝐞𝐫𝐬 4 𝐚𝐧𝐝 5 return back to the firm and how it needs to be rearchitected to implement the digital operating model. Traveling back in time to the Dutch East India Company and then the Industrial Revolution, the authors examine how siloed architectures emerged to enable growth and increasing complexity, but also introduced limitations.
In 𝐂𝐡𝐚𝐩𝐭𝐞𝐫 6, 𝐭𝐡𝐞 𝐚𝐮𝐭𝐡𝐨𝐫𝐬 𝐝𝐢𝐫𝐞𝐜𝐭𝐥𝐲 𝐟𝐨𝐜𝐮𝐬 𝐨𝐧 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐲. They teach about network effects and learning effects. They use examples including Uber and Airbnb to help explain the different types of networks and the strategic implications of each.
They also introduce dynamics that can significantly impact the value of networks, including multihoming, disintermediation, and network bridging. They then move into specific steps leaders can take in developing strategy in this new era.
𝐍𝐞𝐭𝐰𝐨𝐫𝐤 𝐯𝐬 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐄𝐟𝐟𝐞𝐜𝐭. 𝐍𝐞𝐭𝐰𝐨𝐫𝐤 𝐄𝐟𝐟𝐞𝐜𝐭 describe the value added by increasing the number of connections within and across networks, such as the value to a Facebook user of having connections with a large number of friends, or access to a broad variety of developer applications.
The most important value-creation dynamic of a digital operating model is its network effects. The basic definition of a network effect is that the underlying value or utility of a product or service increases as the number of users utilizing the service increases.
𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐞𝐟𝐟𝐞𝐜𝐭𝐬 capture the value added by increasing the amount of data flowing through the same networks—for example, data that may be used to power AI to learn about and improve the user experience or to better target advertisers.
𝐌𝐮𝐥𝐭𝐢𝐡𝐨𝐦𝐢𝐧𝐠 The first and most important force shaping value capture is 𝐌𝐮𝐥𝐭𝐢𝐡𝐨𝐦𝐢𝐧𝐠.
𝐌𝐮𝐥𝐭𝐢𝐡𝐨𝐦𝐢𝐧𝐠 refers to the viability of competitive alternatives, specifically to situations wherein users or service providers in a network can form ties with multiple platforms or hub firms (“homes”) at the same time. If a network hub faces competition from another hub connecting to a network in a similar way, the first network hub’s ability to capture value from the network will be challenged, especially if the switching costs are low enough for users to easily use either hub.
𝐃𝐢𝐬𝐢𝐧𝐭𝐞𝐫𝐦𝐞𝐝𝐢𝐚𝐭𝐢𝐨𝐧 𝐃𝐢𝐬𝐢𝐧𝐭𝐞𝐫𝐦𝐞𝐝𝐢𝐚𝐭𝐢𝐨𝐧, wherein nodes in a network can easily bypass the firm to connect directly, can also be a significant problem for capturing value. From Homejoy to TaskRabbit—that provides only a connection between network participants. After the first connection is made, most if not all of the value created is delivered, and it’s difficult to hold a user accountable to the network hub for ongoing rents.
𝐍𝐞𝐭𝐰𝐨𝐫𝐤 𝐁𝐫𝐢𝐝𝐠𝐢𝐧𝐠 𝐍𝐞𝐭𝐰𝐨𝐫𝐤 𝐁𝐫𝐢𝐝𝐠𝐢𝐧𝐠 involves making new connections across previously separate economic networks, making use of more favorable competitive dynamics and different willingness to pay. Network participants can improve their ability to both create and capture value when they connect to multiple networks, bridging among them to build important synergies.
In 𝐂𝐡𝐚𝐩𝐭𝐞𝐫 7, as they have throughout the book, the authors pull back from the somewhat theoretical and academic to the real world. They discuss several industries where the digital model has collided with existing business models and how industry leaders have responded.
They start with the cell phone industry contrasting Nokia’s rapid fall in the face of the iPhone and Android digital models with Samsung’s value-retaining response. They then move on to briefly touch on the computer, retail, entertainment, and automotive industries, all at different stages in these collisions, to provide a real-world perspective on how the theory translates into meaningful industry change.
𝐂𝐡𝐚𝐩𝐭𝐞𝐫 7 effectively concludes the full development of the themes introduced in the first chapter. However, the authors still have important points to make.
𝐂𝐡𝐚𝐩𝐭𝐞𝐫 8 𝐢𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐞𝐬 𝐭𝐡𝐞 𝐞𝐭𝐡𝐢𝐜𝐚𝐥 𝐝𝐢𝐥𝐞𝐦𝐦𝐚𝐬 𝐢𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐞𝐝 𝐛𝐲 𝐭𝐡𝐞𝐬𝐞 𝐝𝐢𝐠𝐢𝐭𝐚𝐥 𝐦𝐨𝐝𝐞𝐥𝐬. They deal with topics including digital amplification (the echo chamber effect that reinforces existing biases), algorithmic biases, cybersecurity, platform control, and the broad concepts of fairness and equity.
𝐂𝐡𝐚𝐩𝐭𝐞𝐫 9 𝐢𝐬 𝐭𝐢𝐭𝐥𝐞𝐝 “𝐓𝐡𝐞 𝐍𝐞𝐰 𝐌𝐞𝐭𝐚”, which the authors explain means “a new reality that transcends the existing game rules or goes beyond traditional game limits and constraints.”
They specifically list five new rules:
• Rule 1: Change is No Longer Localized; It is Systemic
• Rule 2: Capabilities are Increasingly Horizontal and Universal
• Rule 3: Traditional Industry Boundaries are Disappearing; Recombination is Now the Rule
• Rule 4: From Constrained Operations to Frictionless Impact
• Rule 5: Concentration and Inequality Will Likely Get Worse
While the authors draw 𝐂𝐨𝐦𝐩𝐞𝐭𝐢𝐧𝐠 𝐢𝐧 𝐭𝐡𝐞 𝐀𝐠𝐞 𝐨𝐟 𝐀𝐈 to a conclusion that is just as revolutionary, they have done so in a way that reasonably builds on current realities, deals well with the real economic complexities, and provides helpful guidance for how to navigate these times.
They close the book with 𝐜𝐡𝐚𝐩𝐭𝐞𝐫 10 𝐭𝐢𝐭𝐥𝐞𝐝 “𝐀 𝐋𝐞𝐚𝐝𝐞𝐫𝐬𝐡𝐢𝐩 𝐌𝐚𝐧𝐝𝐚𝐭𝐞” which provides a roadmap for how we can individually and collectively manage this radical transformation.
Though it can unleash enormous growth, the removal of operating constraints isn't always a good thing. Frictionless systems are prone to instability and are hard to stop once they're in motion. Think of a car without brakes or a skier who can't slow down. A digital signal—a viral meme, for instance—can spread rapidly through networks and can be just about impossible to halt, even for the organization that launched it in the first place or an entity that controls the key hubs in a network.
Digital operating models can aggregate harm along with value. Even when the intent is positive, the potential downside can be significant. A mistake can expose a large digital network to a destructive cyberattack. Algorithms, if left unchecked, can exacerbate bias and misinformation on a massive scale. Risks can be greatly magnified. Consider the way that digital banks are aggregating consumer savings in an unprecedented fashion. Ant Financial, which now operates one of the largest money market funds in the world, is entrusted with the savings of hundreds of millions of Chinese consumers. The risks that present are significant, especially for a relatively unproven institution.
Digital scale, scope, and learning create a slew of new challenges—not just privacy and cybersecurity problems, but social turbulence resulting from market concentration, dislocations, and increased inequality. The institutions designed to keep an eye on business—regulatory bodies, for example—are struggling to keep up with all the rapid change.
In an AI-driven world, once an offering’s fit with a market is ensured, user numbers, engagement, and revenues can skyrocket. Yet it’s increasingly obvious that unconstrained growth is dangerous. The potential for businesses that embrace digital operating models is huge, but the capacity to inflict widespread harm needs to be explicitly considered. Navigating these opportunities and threats will be a real test of leadership for both businesses and public institutions.
Bottom line, I strongly recommend Competing in the Age of AI to anyone wanting to understand how digital revolutions are transforming how firms operate and industries compete.
𝐖𝐡𝐚𝐭 𝐈𝐭 𝐓𝐞𝐚𝐜𝐡𝐞𝐬: The authors explain how the digital technology revolutions of the past 50 years have led to a new competitive landscape requiring a new digital operating model.
𝐖𝐡𝐞𝐧 𝐓𝐨 𝐔𝐬𝐞 𝐈𝐭: This book teaches well through multiple important topics including the nature of the technology revolutions, the impact on strategy development, the impact on strategic architecture, and strategic options for firms in different industries responding to this new threat.
The book was an honour by Nuria Bookstore, Nairobi Kenya 🇰🇪 .
What can apps do? Page 34: Ant financial / Alipay, WeChat Pay, Paytm and KakaoPay are innovative apps that make it possible for people to pay and receive money to / from other people. AI taps into data to drive personalization, revenue optimization and recommendations. - Page 53: Increasingly, many processes previously done by people are digitized. Example: Dispatchers do not decide which card are chosen on DiFi, Grab, Lyft or Uber.
What can people do? - Page 97: Managers are innovators, as they envision how digital systems will evolve over time. - Page 97: Managers are integrators as they work to connect disparate digital systems and identify new connections between the firm's operating model and the customers it serves. - Page 97: Managers are guardians, as they work to preserve the quality, reliability, security and responsibility of the digital systems they control. - Page 113: Building an AI centric operating model is about taking traditional processes and embedding them in software and algorithms. - Page 126: Just as industry dominated strategic thinking in the past, network analysis, network thinking will shape strategic thinking in the future. Network analysis involves understanding open and distributed connections across firms .
The book is basically 'AI is important'. 'Managers are stupid and don't understand it' and 'You just have to do the same as google, apple, Amazon and Alibaba ' With a few interesting notes that could have been summarized in a one pager. But no real insights, or ideas on how to truly implement an AI centric organization
AI is one of the major trends in technology currently. A lot of people are speaking about it and how it can influence our lives, jobs, education. Very interesting and insightful overview of technology and application.
The first few chapters are interesting. After that, the books uses technical language which makes it hard to understand. However, the authors have done a great job in integrating and delivering this many data to addresses one of latest and most complicated forms of human evolution “AI”
Overall this book was just ok. I probably went in with huge expectations, as to why I felt it let me down. There were few chapters within the book which were exceptional but the entire mid section felt more like university text. I much prefer Kai Fu Lee’s AI Superpowers.
О ЧЕМ КНИГА: Книга дает устоявшимся компаниям и стартапам набор методик и фреймворков для понимания, работы и конкурирования в эпоху искусственного интеллекта. Переход компании в эру ИИ - это не просто трансформация технологий управления, а появление совершенно другой компании на месте существующей сейчас. Как создать такую компанию нам пошагово рассказывают авторы - профе��сора Harvard Business School. Мысли и подходы из этой работы определяют бизнес в наступившем десятилетии. Очень важная книга.
Особенно интересно читать книгу сейчас, так как она была написана до Covida и видеть, что произошло с компаниями, бизнес-модели которых разбирают авторы.
ГЛАВНАЯ МЫСЛЬ КНИГИ: Ни одна область человеческой деятельности не останется независима от искусственного интеллекта. Постепенно ИИ проникнет во все занятия и дисциплины человека и для бизнеса настанет новый век. Те компании и предприниматели, которые не перестроятся под новую реальность останутся на обочине или потеряют бизнес.
Мы говорим сейчас не об изменениях в технологиях или о специфике работы определенных компаний. Мы говорим сейчас, что вся экономика изменится под влиянием ИИ.
ЗАЧЕМ ЧИТАТЬ ЭТУ КНИГУ? Чтобы получить методы для трансформации своей компании в ИИ компанию.
МЫСЛИ И ВЫВОДЫ ИЗ КНИГИ: - ИИ уже встроен в большинство типов деятельности и процессов. Мы просто его не замечаем, но уже не можем без него обходиться.
- Цифровая копия бизнеса может учиться и улучшать саму себя, в отличии от аналогового и физического варианта.
- Компании в основе бизнес-модели которых находится ИИ создают и поддерживают ценность, а также конкурируют совершенно другими способами, чем традиционные компании.
- В традиционном бизнесе при увеличении его размера усложняются процессы и компания лимитирована в росте своей операционной моделью. Сложность ухудшает бизнес. В бизнесе, где в основе лежит ИИ и система полностью оцифрована, сложность не только не мешает, а помогает развитию компании.
- Фабрика ИИ состоит из 4 компонентов: 1. Поток данных. 2. Алгоритмы. 3. Экспериментальная платформа. 4. Инфраструктура.
ЧТО Я БУДУ ПРИМЕНЯТЬ: - Решая каждую задачу в бизнесе, буду задавать себе вопрос - «Как можно её оцифровать и использовать здесь ИИ?»
The title suggests this is a business book about AI, it is not. Instead, it is about digital business models. What little there is about AI, refers to machine learning, however the references are in passing.
The central idea, that businesses benefit greatly from effective digital operating models, is true but hardly a revelation. The authors recommend organisations to move away from silos and bespoke systems to commonality and interoperability.
As with most business books, this is an HBR article expanded with plenty of filler - including the history of the firm, Luddites, fax machines and the demise of Nokia. None of these commonplace ideas are well told.
The remainder of the book takes well-known trends as evidence of the author's hypothesis and make sweeping statements that are not supported by the evidence. A particular favourite of mine is Table 5-1 showing gross margins and profits for AI maturity "leaders" and "laggards" (GM 55%vs. 37%, Earnings 16% vs 11%) - this strongly suggests a skewed sample with high margin industries (e.g. software) represented in the Leader category and low margin industries (e.g. retail) in the Laggard category. Naturally, the sample data is unsourced.
There are breathless discussions of well-known firms such as Netflix, Peloton, Ocado, etc. that shed remarkably little light and overstate the case - three years later all these firms look less alluring - Netflix's amazing data didn't stop them developing too many mediocre shows, Peloton's customers weren't so sticky after all and Ocado's share price is a fraction of 2020 highs.
Despite having substantial access to Microsoft, the insight about the cloud transformation is scant and seems to boil down to Microsoft's CEO having successfully applied lessons from The Innovator's Dilemma by Clayton Christensen.
I'd love to find an insightful book about AI but this isn't it.
Warning: This book is meant to be read again and again... Everything is so useful!
To compete in the era of AI, business owners have to update 2 things: Their business and operational models. The thing is, wandering aimlessly without a solid Business Model would just be a way to tag on a couple of AI tools into a business process, a product, or service and really not take advantage of everything that AI can be used to reshape a company. The other thing is that an update of the operational infrastructure is also essential for everything AI requires a scalable infrastructure that would certainly collapse traditional data infrastructures.
In today's 4th industrial revolution, AI is used as a factory to mass produce insights, functionalities, data products on scale and done quickly. For this to happen, the operational infrastructure has to help this happen. An infrastructure that allows access to every data source and functionality from the company through APIs; as inspired by Jeff Bezos 2002 Mandate (ifyky, if not, the letter is shown in the book too).
This book is the result of a wide research for experiences by the authors themselves as well as their colleagues and mentors, all of them wise in their fields. I can't really summarize everything stated. There was a fun chapter about the Luddites by the end that may be a moment of history to be repeated if companies don't worry about the jobs of their employees and just fire them.
Personally, I couldn't understand the first paragraph of the Preface and had to re-read it like 8 times. But this paragraph is like a great overview for the first 4 chapters. Later chapters talk about some company strategy, but I think the most important chapters are the first 4-5.
The book overall is amazing, and should be re-read multiple times to keep learning from a myriad of experts in less than 250 pages.
I initially bought this book thinking some of the concepts could help me in one of my consulting engagements. Little did I know that I would constantly be referencing the revolutionary concepts and examples in the book.
No matter the industry, Professors Iansiti and Lakhani compellingly illustrate why artificial intelligence will fundamentally change the way in which we operate and how it will do so (if it has not already).
From household companies like Alibaba and Netflix to niched digital pioneers like ZBJ, the authors do a great job explaining complex concepts (e.g. AI company archetypes, competitive forces etc.) across the gamut of functions – strategy, operations, implementation, and organization design – which, again, is illustrated superbly through clear and concise examples.
I also particularly enjoyed and was very intrigued by their discussions on the ethical implications of leveraging AI and how leaders can respond within this new context. In my opinion, the societal implications of this technology are all too often left out in our mainstream discussions but it is precisely these discussions we need to have as a society,especially as this technology takes center stage during the pandemic.
I would undoubtedly and unequivocally recommend this book to any business owner or professional, student interested in business and economics, or anyone with a vague interest in AI. Easily the best business book I have read this year (and, frankly, for a while now)
Debo empezar por decir que éste es un muy buen libro. El título, sin embargo, es un poco engañoso. El libro se trata fundamentalmente de un buen resumen lo que se sabe hasta el momento sobre la transformación digital de las empresas, más que un libro que trate fundamentalmente sobre Inteligencia Artificial, como parece indicar el título. Es verdad que una de las tecnologías digitales que podrán tener mayor impacto en los siguientes años es la Inteligencia Artificial; sin embargo, la transformación digital de las empresas va mucho más allá y requiere implementar no sólo esta sino otras tecnologías digitales y, sobre todo, requiere cambios en la cultura y el liderazgo de las organizaciones.
Habiendo dicho lo anterior, termino como empecé esta reseña: este es un muy buen libro, especialmente para los líderes empresariales que están comenzando su camino de transformación digital (tal vez a marchas forzadas por la emergencia que atravesamos) y requieren un buen resumen de los que esto significa. Los autores hacen un muy buen trabajo en condensar lo que han investigado otros académicos sobre el tema y son generosos en reconocer a quienes han hecho las distintas contribuciones aunque es verdad que para los lectores más conocedores del tema, hay poco en el libro de novedoso y por tanto, no les será de tanto provecho.
Marco Iansiti is a Professor of Business Administration at Harvard Business School. His special expertise revolves around Technology and Operations Management. He advises Blue Chip companies globally on operational transition, and technological transformation for the 21st Century. Iansiti and Lakhani posit workable solutions and invaluable insights into the infinite utility of AI.
I studied Managerial Cybernetics, Systems Analysis, and Systems Development under the tutelage of Stafford Beer for both my B.Sc., and M.Sc. I am currently evaluating how the synthesis of a variety of recent technologies can be applied to extend the economic potential of large distributed networks and International organizations. Much of my work includes related issues raised in Competing in The Age of AI. My experience and my academic credentials, I believe afford me a unique perspective on this manuscript.
Iansiti is a prolific author of publications based on a particular area of expertise. Some of his works include: Digital Ubiquity, The Truth About Blockchain (Iansiti & Lakhani), The Keystone Advantage (Roy Levine), and Managing Our Hub Economy. Those looking for advice on establishing digital advantage or operational model transformation of a global organization need look no further than Competing in The Age of AI.
Karim R. Lakhani is a Business Administration Professor at Harvard Business School. Lakhani is the co-director of the Laboratory of Innovation Science at Harvard’s Institute of Quantitative Social Science, as well as the Chair of the Harvard Business School’s Analytics Program. His area of expertise is innovation and technology management. He is the author of numerous articles and case studies on technology, digital commerce, and digital innovation. He has been published in a myriad of significant publications that include: The Economist, The Wall Street Journal, and Business Week among many others.
Who is the target audience?
This is probably the most important book on business application of digital innovations this decade. This thesis is suitable for those who want or need to understand the potential for the increased span, scope, and scale afforded by the appropriate utilization of digital innovations, particularly artificial intelligence as applied to business models. Iansiti and Lakhani’s concept provides a most important tool for Captains of industry, investors, CEOs, entrepreneurs, and students of business and technology. Iansiti and Lakhani’s publication is a must read for all those who want to improve their understanding of the application of AI in organizations. Competing in the Age of AI should be compulsory reading for all those involved in leveraging competitive advantage in the new business world underpinned by artificial intelligence.
Synopsis
The discussions herein include the question of ethics in application and distribution of technology. Iansiti and Lakhani’s have provided a map for the exploitation of the technical advances provided by new technology. The authors have given practical advice on the strengths, limitations, and challenges of employing artificial intelligence to support and augment a company’s strategy.
Conclusion
Competing in The Age of AI is a seminal work, containing all the key ingredients for global companies to explore in order to improve competitive advantage. Iansiti and Lakhani have provided sensible, practical jargon-free explanations for the application of advanced technology strategies and advice on the potential effects on span, scope, and scale across the organization.
Acknowledgment
My sincere thanks go to: NetGalley, and Harvard Business Review Press for affording me the opportunity to review of Competing in the Age of AI.
An executive-level view on the impacts of new digital technologies on business, and an introduction to the strategies on how to compete in the new terrain. As a data scientist, it is beneficial to see what the business experts at HBS are recommending, but it is quite frustrating to see how little time was put into actually explaining any of the technology. It reminded me of some of the business classes I took during my undergrad- extremely high level, just enough to make decisions without being fully informed.
Too often, data scientists hear people asking us to "do AI" on problems, having no idea what that would entail or if it is even possible. I wish that books introducing business people to the premise of AI would take a bit more time on explaining what it actually is. They don't need to teach the specific models or math behind it, but an introduction to what is possible would make a big difference. The fact that classification and clustering are mentioned zero (0) times in the book is disheartening.
This book discusses how companies can benefit from artificial intelligence (AI) to be more competitive. It clarifies how data and analytics are more vital than ever to the success of businesses. It reminds us that today’s economy is driven by digital technologies and alongside other factors; companies’ success depends on the way they deliver value to customers. The success of today’s dynamos such as Google, Microsoft, and Facebook is a strong indication of appreciating AI. The authors of the book, Iansiti and Lakhani (2020) have proposed three elements to enrich operating models by AI: scale, scope, and learn. They suggest that it is necessary to redesign processes to benefit from AI-driven technologies. In their view, companies that welcome AI will survive much better than those that will not. Iansiti and Lakhani’s model may be considered as an update of Ries’s (2011) loop of ‘build, measure, and learn’ in his book, The Lean Startup.
Laden down with business jargon and buzzwords and focused on management theory, the book is rather sloppy with the language when describing the actual technology, freely using the terms "digital" "AI" and "Machine Learning" as interchangeable equivalents.
After an enthusiastic introduction touting the benefits of data analytics (which is hardly a new concept and has nothing to do with AI) and presenting an operating model for a "scalable decision factory", the book gets somewhat lost in the weeds, presents some case studies to illustrate how great their ideas are that read like press releases (they divulge in the afterword that they have financial ties to just about every company mentioned), then belatedly finishes up with a rather superficial discussion of security, privacy, and ethical considerations that just maybe should be part of the earlier planning phases and not an afterthought.