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Generative AI: The Insights You Need from Harvard Business Review
The future of AI is here.
The world is transfixed by the marvel (and possible menace) of ChatGPT and other generative AI tools. It's clear Gen AI will transform the business landscape, but when and how much remain to be seen. Meanwhile, your smartest competitors are already navigating the risks and reaping the rewards of these new technologies. They're experimenting with new business models around generating text, images, and code at astonishing speed. They're automating customer interactions in ways never before possible. And they're augmenting human creativity in order to innovate faster. How can you take advantage of generative AI and avoid having your business disrupted?
Generative The Insights You Need from Harvard Business Review will help you understand the potential of these new technologies, pick the right Gen AI projects, and reinvent your business for the new age of AI.
Business is changing. Will you adapt or be left behind?
Get up to speed and deepen your understanding of the topics that are shaping your company's future with the Insights You Need from Harvard Business Review series. Featuring HBR's smartest thinking on fast-moving issues—blockchain, cybersecurity, AI, and more—each book provides the foundational introduction and practical case studies your organization needs to compete today and collects the best research, interviews, and analysis to get it ready for tomorrow.
You can't afford to ignore how these issues will transform the landscape of business and society. The Insights You Need series will help you grasp these critical ideas—and prepare you and your company for the future.
The world is transfixed by the marvel (and possible menace) of ChatGPT and other generative AI tools. It's clear Gen AI will transform the business landscape, but when and how much remain to be seen. Meanwhile, your smartest competitors are already navigating the risks and reaping the rewards of these new technologies. They're experimenting with new business models around generating text, images, and code at astonishing speed. They're automating customer interactions in ways never before possible. And they're augmenting human creativity in order to innovate faster. How can you take advantage of generative AI and avoid having your business disrupted?
Generative The Insights You Need from Harvard Business Review will help you understand the potential of these new technologies, pick the right Gen AI projects, and reinvent your business for the new age of AI.
Business is changing. Will you adapt or be left behind?
Get up to speed and deepen your understanding of the topics that are shaping your company's future with the Insights You Need from Harvard Business Review series. Featuring HBR's smartest thinking on fast-moving issues—blockchain, cybersecurity, AI, and more—each book provides the foundational introduction and practical case studies your organization needs to compete today and collects the best research, interviews, and analysis to get it ready for tomorrow.
You can't afford to ignore how these issues will transform the landscape of business and society. The Insights You Need series will help you grasp these critical ideas—and prepare you and your company for the future.
170 pages, Kindle Edition
Published January 30, 2024
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Displaying 1 - 17 of 17 reviews
May 13, 2024
Like other Harvard Business Review series, this is a compilation of articles related to the topic. Generative AI is quite new so I was interested in knowing what these specialists had to say. I found the content pretty standard, not much deeper than a blog or white paper you can find online. If you have been in the marketing/data scene for the last decade there are very few new things, just a rebranding of old concepts like personalization, tailoring the experience and improving customer service. Nothing different from what IBM Watson promised years ago with its chatbots. The takeaways section at the end of each article is usually a poor summary because they oversimplify in two or three bullets some of the concepts, losing many times the most relevant ideas.
A quick description of the eleven articles:
- Generative AI will change your business. Here’s how to adapt presents as a novelty 1-1 marketing and using transactional and behavioral data to tailor experiences. We have been doing that for years using SAS, SPSS, R or Python to mine data, understand customers and offer them the right product. The challenge continues to be gathering data, improving its quality and convincing sales people to really use this analytics model instead of contacting every client.
- How network effects make AI smarter”: talks about the data network effects (data-driven learning) and how it’s important the feedback loop to keep improving the solution.
- A framework for picking the right generative AI project: focus on the potential for learning using this technology. The framework is just a basic matrix of low and high demand or risk to group your ideas on initiatives with GenAI.
- How generative AI could disrupt creative work is the one that was the most interesting for me because it presents the different scenarios for creative professionals: AI -assisted innovation, machine monopolize creativity or ‘human-made’ commands a premium (another way to view it is to think if we are becoming centaurs -delegating task to the AI- or cyborgs -integrating AI into our task-). It also recommends investing in our own ontology.
- How generative AI can augment human creativity: find the pros and cons of ideas and compare their novelty, feasibility, specificity, impact and workability using GenAI. Combine ideas, facilitate collaboration.
- How generative AI will change sales: they describe a good CRM, a tool that companies have been using for long now. The challenge again is not AI but how to avoid silos, data gatekeepers and deploy it in all the channels including tricky offline ones (branches, external call-centers, etc.). It is also naive enough to think that salesmen will help the algorithm providing feedback, a clear proof that the authors never worked in sales.
- Generative AI has an intellectual property problem: the importance of taking care of provenance of AI-generated content to avoid risks.
- AI prompt engineering isn’t the future: another good article. Again, it has little to do with GenAI but nonetheless is very useful. It says that the key skill is problem formulation: “the ability to identify, analyze, and delineate problems (...) defining the problem by delineating its focus, scope, and boundaries.” It has four key components: problem diagnosis, decomposition, reframing and constraint design.
- Eight questions about using AI responsibly, answered: invisibility and inscrutability as core characteristics of AI, data curation and documentation, sanitizing datasets and privacy by design (PbD)
- Managing the risk of generative AI talks about the importance of accuracy, safety, honesty, empowerment, sustainability. I think it is very generic advice. But I learnt about zero-party data -data that customers share proactively- a concept that I didn’t know about (despite working a lot on the first, second and third-party data). Use zero-party or first party data, keep data fresh and well labeled, ensure there’s a human in the loop, get feedback are some of the tips.
- The AI hype cycle is distracting companies he presents a truth: most ML projects fail to deliver value. We should focus on using them to optimize existing processes.
In summary, if you are new to this subject, you can read this book in an hour or two and get a glimpse of generative AI. Otherwise, you will not find anything ground taking but an educated introduction.
Some of the key fragments:
“(...) focusing on the broadest possible view of your pool of data, of the journeys you can enable.”
“Rules layer, where the experienced designers, marketers, and business decision-makers set the target parameters for the AI to optimize.”
“Deliver the end-to-end journey, and the specific use cases involved.”
“How trustworthy, how well permissioned, how timely, how comprehensive, how biased is their data?
“The way you manage data becomes part of your brand.”
“Everyone should consider the data they share, intentionally or not.”
“Generative AI’s capabilities could also allow learning materials to be delivered differently (...) replacing clunky FAQs, bulging knowledge centers, and ticketing systems.”
“The ability to quickly and easily retrieve, contextualize, and interpret knowledge may be the most powerful business application of large language models.”
“Curation will become more valuable relative to creation as search costs rise.”
“Invest in your ontology: codifying, digitizing, and structuring the knowledge you create will be a critical value driver.”
“Domain experts who are best at generating and identifying feasible ideas often struggle with generating or even accepting novel ideas.”
“Trisociation -connecting three distinct entities.”
“Instead of thinking of AI as the tools we use, we should think of it as a set of systems with which we can collaborate.”
“AI is whatever machines haven’t done yet.” (Larry Tesler)
A quick description of the eleven articles:
- Generative AI will change your business. Here’s how to adapt presents as a novelty 1-1 marketing and using transactional and behavioral data to tailor experiences. We have been doing that for years using SAS, SPSS, R or Python to mine data, understand customers and offer them the right product. The challenge continues to be gathering data, improving its quality and convincing sales people to really use this analytics model instead of contacting every client.
- How network effects make AI smarter”: talks about the data network effects (data-driven learning) and how it’s important the feedback loop to keep improving the solution.
- A framework for picking the right generative AI project: focus on the potential for learning using this technology. The framework is just a basic matrix of low and high demand or risk to group your ideas on initiatives with GenAI.
- How generative AI could disrupt creative work is the one that was the most interesting for me because it presents the different scenarios for creative professionals: AI -assisted innovation, machine monopolize creativity or ‘human-made’ commands a premium (another way to view it is to think if we are becoming centaurs -delegating task to the AI- or cyborgs -integrating AI into our task-). It also recommends investing in our own ontology.
- How generative AI can augment human creativity: find the pros and cons of ideas and compare their novelty, feasibility, specificity, impact and workability using GenAI. Combine ideas, facilitate collaboration.
- How generative AI will change sales: they describe a good CRM, a tool that companies have been using for long now. The challenge again is not AI but how to avoid silos, data gatekeepers and deploy it in all the channels including tricky offline ones (branches, external call-centers, etc.). It is also naive enough to think that salesmen will help the algorithm providing feedback, a clear proof that the authors never worked in sales.
- Generative AI has an intellectual property problem: the importance of taking care of provenance of AI-generated content to avoid risks.
- AI prompt engineering isn’t the future: another good article. Again, it has little to do with GenAI but nonetheless is very useful. It says that the key skill is problem formulation: “the ability to identify, analyze, and delineate problems (...) defining the problem by delineating its focus, scope, and boundaries.” It has four key components: problem diagnosis, decomposition, reframing and constraint design.
- Eight questions about using AI responsibly, answered: invisibility and inscrutability as core characteristics of AI, data curation and documentation, sanitizing datasets and privacy by design (PbD)
- Managing the risk of generative AI talks about the importance of accuracy, safety, honesty, empowerment, sustainability. I think it is very generic advice. But I learnt about zero-party data -data that customers share proactively- a concept that I didn’t know about (despite working a lot on the first, second and third-party data). Use zero-party or first party data, keep data fresh and well labeled, ensure there’s a human in the loop, get feedback are some of the tips.
- The AI hype cycle is distracting companies he presents a truth: most ML projects fail to deliver value. We should focus on using them to optimize existing processes.
In summary, if you are new to this subject, you can read this book in an hour or two and get a glimpse of generative AI. Otherwise, you will not find anything ground taking but an educated introduction.
Some of the key fragments:
“(...) focusing on the broadest possible view of your pool of data, of the journeys you can enable.”
“Rules layer, where the experienced designers, marketers, and business decision-makers set the target parameters for the AI to optimize.”
“Deliver the end-to-end journey, and the specific use cases involved.”
“How trustworthy, how well permissioned, how timely, how comprehensive, how biased is their data?
“The way you manage data becomes part of your brand.”
“Everyone should consider the data they share, intentionally or not.”
“Generative AI’s capabilities could also allow learning materials to be delivered differently (...) replacing clunky FAQs, bulging knowledge centers, and ticketing systems.”
“The ability to quickly and easily retrieve, contextualize, and interpret knowledge may be the most powerful business application of large language models.”
“Curation will become more valuable relative to creation as search costs rise.”
“Invest in your ontology: codifying, digitizing, and structuring the knowledge you create will be a critical value driver.”
“Domain experts who are best at generating and identifying feasible ideas often struggle with generating or even accepting novel ideas.”
“Trisociation -connecting three distinct entities.”
“Instead of thinking of AI as the tools we use, we should think of it as a set of systems with which we can collaborate.”
“AI is whatever machines haven’t done yet.” (Larry Tesler)
October 18, 2024
Generative AI is one of HBR's Insights Series that summarized the foundational topic of Gen-AI into a handy book that promises to make us "up to speed" and "deepen our understanding" with the latest findings in Gen-AI.
What I learned from this book can be categorized as an 'overview' of major things that are happening in Gen-AI. Since I dabbled a bit in Gen-AI because of my work environment, I did have prior knowledges on some of the topics in this book. That is not to say that this book has no impact on my learnings of Gen-AI, since I gained a lot of understandings compiled from the latest journals by amazing researchers that is on the bleeding edge of this technology.
I like how this book is more of a business insights, rather than a technical one. The structure of the book makes it easy to digest the topic and I like how there are summary tabs for each chapter / topic, which made it more convenient.
If you are looking for a technical deep dive of Gen-AI and the nitty-gritty details of how it works, this is not the book for you. But I would recommend this to fellow businessman and entrepreneurs who would be keen on finding out which Gen-AI technologies that could benefit them and their businesses as the book also presents us with business cases so one can picture the potential impact with more clarity.
What I learned from this book can be categorized as an 'overview' of major things that are happening in Gen-AI. Since I dabbled a bit in Gen-AI because of my work environment, I did have prior knowledges on some of the topics in this book. That is not to say that this book has no impact on my learnings of Gen-AI, since I gained a lot of understandings compiled from the latest journals by amazing researchers that is on the bleeding edge of this technology.
I like how this book is more of a business insights, rather than a technical one. The structure of the book makes it easy to digest the topic and I like how there are summary tabs for each chapter / topic, which made it more convenient.
If you are looking for a technical deep dive of Gen-AI and the nitty-gritty details of how it works, this is not the book for you. But I would recommend this to fellow businessman and entrepreneurs who would be keen on finding out which Gen-AI technologies that could benefit them and their businesses as the book also presents us with business cases so one can picture the potential impact with more clarity.
July 7, 2025
A collection of HBR articles on generative AI. Generative AI promises both revolutionary breakthroughs in business and at the same time poses a new threat to how we do things.
Generative AI, as we’ve seen in many of its applications in today’s world, simplifies work and put troughs of information at our fingertips. With the rapid advancement of computer processing power, the future seems limitless.
However, on the other side of the coin, there are risks associated with expanding usage of AI. It can be from dataset selections that entrench biases, inaccuracy gone undetected impacting lives, intellectual property rights violation or outright taking someone’s job. Road to the AI utopia is certainly not straight path.
Businesses will need to learn to embrace this change to stay relevant. Synergizing AI with existing processes while managing the risks starts with understanding of what AI is, and what it’s capable/incapable of. Building some of the compliance, bias awareness and constraints into the design will help limit the potential downside risks.
One skillset that companies have been focusing on is on prompt engineering. In one of the articles, the author advised to redirect focus toward problem formulation instead. A well defined problem is half the solution.
Overall pretty informative book on generative AI and how businesses can prepare for their wider applications.
Generative AI, as we’ve seen in many of its applications in today’s world, simplifies work and put troughs of information at our fingertips. With the rapid advancement of computer processing power, the future seems limitless.
However, on the other side of the coin, there are risks associated with expanding usage of AI. It can be from dataset selections that entrench biases, inaccuracy gone undetected impacting lives, intellectual property rights violation or outright taking someone’s job. Road to the AI utopia is certainly not straight path.
Businesses will need to learn to embrace this change to stay relevant. Synergizing AI with existing processes while managing the risks starts with understanding of what AI is, and what it’s capable/incapable of. Building some of the compliance, bias awareness and constraints into the design will help limit the potential downside risks.
One skillset that companies have been focusing on is on prompt engineering. In one of the articles, the author advised to redirect focus toward problem formulation instead. A well defined problem is half the solution.
Overall pretty informative book on generative AI and how businesses can prepare for their wider applications.
March 13, 2025
I picked up this book in the Detroit airport about a month ago. It is a collection of essays on AI from the Harvard Business Review.
Like any collection, all essays are not equal, but some are outstanding. These essays give a lot of attention to ethics in the use of AI, especially in a business context. These are mostly very helpful.
The overall reading helped me come to a better understanding of AI and its potential, but the last essay was perhaps the best in the book, bursting the bubble of AI hype. AI is not the same as human intelligence. It is, at best, highly developed machine learning, and as such is a very useful tool in appropriate contexts and with human supervision.
Despite the three stars, I think the book is worth reading if you are interested in learning more about the topic. There is some overuse of business/management buzzwords, but you can wade through that.
Like any collection, all essays are not equal, but some are outstanding. These essays give a lot of attention to ethics in the use of AI, especially in a business context. These are mostly very helpful.
The overall reading helped me come to a better understanding of AI and its potential, but the last essay was perhaps the best in the book, bursting the bubble of AI hype. AI is not the same as human intelligence. It is, at best, highly developed machine learning, and as such is a very useful tool in appropriate contexts and with human supervision.
Despite the three stars, I think the book is worth reading if you are interested in learning more about the topic. There is some overuse of business/management buzzwords, but you can wade through that.
December 14, 2024
Useful tutorial for C-Suite to learn Gen AI potential
Essentially a warning to Corporate Execs to not ignore the impact Gen AI will have on their operations. I chose it to better understand exactly how Gen AI and Machine Learning work, what techniques they use to perform their Magic, The latter half of this paper and the FAQ’s address these issues to some extent and warn of the AI Hype infecting the Corporate World. Four Stars. ****
Essentially a warning to Corporate Execs to not ignore the impact Gen AI will have on their operations. I chose it to better understand exactly how Gen AI and Machine Learning work, what techniques they use to perform their Magic, The latter half of this paper and the FAQ’s address these issues to some extent and warn of the AI Hype infecting the Corporate World. Four Stars. ****
April 3, 2025
Apparently all it takes to get published in HBR these days is to copy and paste whatever comes out of an LLM after prompting it with "write some banal, professional-sounding prose about bias and risk in generative AI." Truly, several of these articles were hackneyed to the point of my irritation.
Still, there were a couple articles that made me look at generative AI through a fresh lens, and that will certainly be useful.
Still, there were a couple articles that made me look at generative AI through a fresh lens, and that will certainly be useful.
August 29, 2024
5/10.
There are some interesting bits but overall very okayish read. Many parts are shallow that one may already know if one has read some general GenAI related articles or blogs..
It was audiobook which certainly is not the best medium. So discount that.
There are some interesting bits but overall very okayish read. Many parts are shallow that one may already know if one has read some general GenAI related articles or blogs..
It was audiobook which certainly is not the best medium. So discount that.
April 11, 2025
مجموعة من المقالات حول الذكاء الصناعي التوليدي ، تنوعت ما بين الجيد ومتوسط الجود والممتاز ، تقدم تصورا عاما لغير المتخصصين وتركز على الجوانب النفعية والتنظيمية للذكاء الصناعي التوليدي .. قراءة سريعة جيدة لغير المتخصصين
August 21, 2024
A good collection of some key issues around GenAI.
December 7, 2024
A decent primer on AI. Nothing spectacular.
January 16, 2025
Some good thoughts, but high level and repetitive because it was written by several different authors who all wanted to share the X points needed to summarize AI.
January 16, 2025
Some important sites, AI is coming, whether we want it or not!
March 25, 2025
General stuff about gen AI. Something new, e.g. Tesler defined AI as "whatever machines haven't done yet"
June 29, 2025
Typical airport read. Learned a few good things but about half of the articles are so so.
April 1, 2024
I have obtained no insights.
August 14, 2024
The book is basically a collection of short articles written by different contributors with varying background and expertise ... including business, technology, consulting, political, academic etc. My takeaway from the book is it serves well as a general awareness to a particular kind of AI ... "Generative AI" from different perspectives when businesses are considering using it. These include basic understanding of its capabilities, limitations and risks.
August 6, 2025
This 192-page volume, published in January 2024, is part of HBR’s “Insights You Need” series, which distills fast-moving topics into digestible, strategic guidance. With contributions from Ethan Mollick, David De Cremer, Tsedal Neeley, and Prabhakant Sinha, the book offers a multifaceted look at how generative AI is reshaping business
Displaying 1 - 17 of 17 reviews