What do you think?
Rate this book
Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World
"a provocative new book" -- The New York Times AI-centric organizations exhibit a new operating architecture, redefining how they create, capture, share, and deliver value. 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
288 pages, Hardcover
First published January 7, 2020
Ratings & Reviews
Friends & Following
Create a free account to discover what your friends think of this book!
Community Reviews
Displaying 1 - 30 of 64 reviews
January 27, 2021
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.
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.
January 4, 2020
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?
Some nice ideas:
- Digital amplification
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?
Some nice ideas:
- Digital amplification
September 2, 2021
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.
This review was first posted at Eustea Reads
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.
This review was first posted at Eustea Reads
January 26, 2021
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.
February 6, 2020
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.
Find more Business Books of the week on my Goodreads Listopia page at https://www.goodreads.com/list/show/9..., and find many more recommended books on my Amazon Influencer page at https://www.amazon.com/shop/stevo4747.
This book was also selected as a Best of the Best book for February, 2020: https://forums.delphiforums.com/stevo....
Find more Business Books of the week on my Goodreads Listopia page at https://www.goodreads.com/list/show/9..., and find many more recommended books on my Amazon Influencer page at https://www.amazon.com/shop/stevo4747.
This book was also selected as a Best of the Best book for February, 2020: https://forums.delphiforums.com/stevo....
November 24, 2019
Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the WorldAuthor’s Bio
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.
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.
October 5, 2019
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.
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.
February 14, 2023
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.
January 31, 2021
Excellent insights, pertinent to employees, managers, leaders who want to be ready for today's and tomorrow's challenges.
December 12, 2020
Amazing read on how businesses are using artificial intelligence to drive value.
July 28, 2022
I enjoyed this professional development book as it combined the success of modern tech giants and their use of AI.
At times the chapters were a bit wordy. However, the diagrams and formatting allowed those sections not speaking to you to be skipped.
At times the chapters were a bit wordy. However, the diagrams and formatting allowed those sections not speaking to you to be skipped.
March 2, 2020
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.
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.
April 29, 2021
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
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
March 21, 2020
What I learned reading the book:
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 .
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 .
October 14, 2019
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.
January 23, 2020
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”
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”
December 30, 2021
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 - профессора 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?»
ЕЩЕ НА ЭТУ ТЕМУ:
Кевин Келли «Неизбежно»
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 - профессора 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?»
ЕЩЕ НА ЭТУ ТЕМУ:
Кевин Келли «Неизбежно»
August 21, 2020
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)
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)
April 27, 2020
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.
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.
November 24, 2019
COMPETING IN THE AGE OF AI
Author’s Bio
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.
Author’s Bio
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.
February 4, 2022
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.
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.
October 6, 2020
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.
July 4, 2022
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.
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.
August 19, 2022
You know when you have OCD because of which you can't leave reading a book in the middle and then you come across one so bad that you wonder who you hate more - the writers for writing it or yourself for sticking to it. This was one such torture!
This is a classical case of why academics should work with actual authors if at all they ever decide to write a book. The book could have been cut by 80% if you take out the gazillion repetition of ideas. Big paragraphs could be summarised in one liners. Barring a few places, everything is so surface level that you start wondering if you learnt anything new & interesting at all.
There are some good case studies but skimming through the whole book to access them doesn't justify the time trade off. I suggest you rather watch couple of YouTube videos on the same topic and you'll know more than you'd ever learn from this wordy, bland, shallow, torturously-long book!
This is a classical case of why academics should work with actual authors if at all they ever decide to write a book. The book could have been cut by 80% if you take out the gazillion repetition of ideas. Big paragraphs could be summarised in one liners. Barring a few places, everything is so surface level that you start wondering if you learnt anything new & interesting at all.
There are some good case studies but skimming through the whole book to access them doesn't justify the time trade off. I suggest you rather watch couple of YouTube videos on the same topic and you'll know more than you'd ever learn from this wordy, bland, shallow, torturously-long book!
November 2, 2020
A fantastic introduction to Artificial Intelligence and a forecast of what is to come. From understanding operating models versus business models and more. To understanding how it is currently used in many of the services and products we use on an every day basis. The reality is AI is here to stay and if we as business people don't believe and implement it we will be at a disadvantage. There are numerous stories and examples in the book. If this is your first book on AI and are not familiar with the topic you may be left with confusion and a hunger for more knowledge. That is fine. It is worth reading the book again and digging more into the subject to master this next big thing.
February 17, 2021
An important book
While I generally find books of this kind are often re-hashes of the obvious, in fairness I have to say that this book covers important ground and will be viewed as a seminal work marking its period in time in technology just as “Crossing the Chasm” and “Innovator’s Dilemma” did. Work past rehashes of the obvious companies leveraging network effects and digitization of processes to gain scale and profitability and there is still much to gain by pausing and digesting the implications of what’s to come with digital transformation.
While I generally find books of this kind are often re-hashes of the obvious, in fairness I have to say that this book covers important ground and will be viewed as a seminal work marking its period in time in technology just as “Crossing the Chasm” and “Innovator’s Dilemma” did. Work past rehashes of the obvious companies leveraging network effects and digitization of processes to gain scale and profitability and there is still much to gain by pausing and digesting the implications of what’s to come with digital transformation.
July 14, 2021
Marco Iansiti and Karim R. Lakhani deliver the reader an in-depth analysis of the current state of AI in the workforce in addition to plenty of case studies of successes and failures across industries. The authors prove themselves as experts in their field and explain clearly the different forms, uses, and impacts of modern artificial intelligence. I believe this is an incredible read for any leader in modern business and any employee looking to enhance their skills or adapt to a rapidly evolving professional world.
Read
January 1, 2022I feel rather ambivalent about this book. On the one hand, the book exposes relevant concepts in today’s business and tech world. It also reads like a Harvard case study on many companies (Ant Financial, Uber, Google, etc) which I personally enjoy.
Conversely, this book didn’t feel to be about AI in business, but rather an effort to brand AI as the driving force behind most business strategy decisions because “digitalisation” sounds so 2000ish. Most of the concepts in the book, such as economies of scale and learning, network effects, multi-homing are pretty basic and I would be surprised one wouldn’t know about this unless you’ve been in a coma for the last 20 years.
Conversely, this book didn’t feel to be about AI in business, but rather an effort to brand AI as the driving force behind most business strategy decisions because “digitalisation” sounds so 2000ish. Most of the concepts in the book, such as economies of scale and learning, network effects, multi-homing are pretty basic and I would be surprised one wouldn’t know about this unless you’ve been in a coma for the last 20 years.
May 16, 2020
The book convinced me that, like it or not, the "platform revolution" is the real deal because of its capacity to minimize information barriers and management inefficiency. The upcoming economic transformation is going to be as ruthless as the Industrial Revolution. Traditional businesses need to revamp its organization structure and strategy to fully master the power of AI and big data, or become squished like a bug. Governments need to rethink its legal system and public policy (regarding anti-trust laws, privacy, and data security) to adapt to the new economic engine. Neoliberal Free market ideology thrived in mass-production industrial economy, but it will lead to unprecedented inequality in the new economy. A stronger welfare state is essential to regulate big private platforms and redistribute profit.
June 3, 2020
Fantastic all around business book that gave me several epiphanies in reading. It's unclear how transformational the book will be, but I could see this becoming a highly influential read in the management community over the next few years. Although there are no strictly new ideas here, the ideas are put together in a way that gives them coherence and binds them together into a strategy that can be understood and followed.
Displaying 1 - 30 of 64 reviews