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The AI-First Company: How to Compete and Win with Artificial Intelligence
Artificial Intelligence is transforming every industry, but if you want to win with AI, you have to put it first on your priority list.
AI-First companies are the only trillion-dollar companies, and soon they will dominate even more industries, more definitively than ever before. These companies succeed by design--they collect valuable data from day one and use it to train predictive models that automate core functions. As a result, they learn faster and outpace the competition in the process. Thankfully, you don't need a Ph.D. to learn how to win with AI.
In The AI-First Company , internationally-renowned startup investor Ash Fontana offers an executable guide for applying AI to business problems. It's a playbook made for real companies, with real budgets, that need strategies and tactics to effectively implement AI. Whether you're a new online retailer or a Fortune 500 company, Fontana will teach you how
• Identify the most valuable data;
• Build the teams that build AI;
• Integrate AI with existing processes and keep it in check;
• Measure and communicate its effectiveness;
• Reinvest the profits from automation to compound competitive advantage.
If the last fifty years were about getting AI to work in the lab, the next fifty years will be about getting AI to work for people, businesses, and society. It's not about building the right software -- it's about building the right AI. The AI-First Company is your guide to winning with artificial intelligence.
AI-First companies are the only trillion-dollar companies, and soon they will dominate even more industries, more definitively than ever before. These companies succeed by design--they collect valuable data from day one and use it to train predictive models that automate core functions. As a result, they learn faster and outpace the competition in the process. Thankfully, you don't need a Ph.D. to learn how to win with AI.
In The AI-First Company , internationally-renowned startup investor Ash Fontana offers an executable guide for applying AI to business problems. It's a playbook made for real companies, with real budgets, that need strategies and tactics to effectively implement AI. Whether you're a new online retailer or a Fortune 500 company, Fontana will teach you how
• Identify the most valuable data;
• Build the teams that build AI;
• Integrate AI with existing processes and keep it in check;
• Measure and communicate its effectiveness;
• Reinvest the profits from automation to compound competitive advantage.
If the last fifty years were about getting AI to work in the lab, the next fifty years will be about getting AI to work for people, businesses, and society. It's not about building the right software -- it's about building the right AI. The AI-First Company is your guide to winning with artificial intelligence.
304 pages, Hardcover
Published May 4, 2021
89 people are currently reading
285 people want to read
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Displaying 1 - 18 of 18 reviews
June 6, 2021
Este no es un libro fácil de caracterizar. Es demasiado complejo y técnico para personas que quieran adquirir conocimientos básicos sobre el uso de la Inteligencia Artificial en los negocios. Por otro lado, en ocasiones es difícil saber si el autor escribió el libro pensando en cualquier compañía que quiera usar la Inteligencia Artificial o en empresas cuyo producto o servicio consista en proveer soluciones de Inteligencia Artificial a otras empresas.
De cualquier manera, si entiendes, por lo menos de manera conceptual, como funciona esta tecnología y como puede ayudar a tomar mejores decisiones, y tu empresa se ha embarcado ya en el viaje de usarla de manera seria y sistemática para hacer más eficientes sus operaciones o para crear mayor valor para sus clientes, este libro puede ser para ti.
La Inteligencia Artificial, siendo lo que en Teoría Económica se conoce como una tecnología de uso general, tiene el potencial de revolucionar prácticamente todas las actividades de la empresa pero, al mismo tiempo, requiere el repensar y reorganizar procesos, tareas y recursos para su pleno aprovechamiento. Esta es razón por la cual, muchas empresas hacen grandes inversiones para utilizarla pero muchas veces no son capaces de generar la rentabilidad que justifique dicha inversión. En este libro, el autor presenta varias ideas valiosas para superar este problema y ayudar a tu empresa a construir las bases de una ventaja competitiva a través del uso de la Inteligencia Artificial, desde la generación de una estrategia de datos que sirvan como insumo hasta la formación de equipos de trabajo y la medición y comunicación de su efectividad.
De cualquier manera, si entiendes, por lo menos de manera conceptual, como funciona esta tecnología y como puede ayudar a tomar mejores decisiones, y tu empresa se ha embarcado ya en el viaje de usarla de manera seria y sistemática para hacer más eficientes sus operaciones o para crear mayor valor para sus clientes, este libro puede ser para ti.
La Inteligencia Artificial, siendo lo que en Teoría Económica se conoce como una tecnología de uso general, tiene el potencial de revolucionar prácticamente todas las actividades de la empresa pero, al mismo tiempo, requiere el repensar y reorganizar procesos, tareas y recursos para su pleno aprovechamiento. Esta es razón por la cual, muchas empresas hacen grandes inversiones para utilizarla pero muchas veces no son capaces de generar la rentabilidad que justifique dicha inversión. En este libro, el autor presenta varias ideas valiosas para superar este problema y ayudar a tu empresa a construir las bases de una ventaja competitiva a través del uso de la Inteligencia Artificial, desde la generación de una estrategia de datos que sirvan como insumo hasta la formación de equipos de trabajo y la medición y comunicación de su efectividad.
April 27, 2022
As a Co-Founder, I found Ash's book to be an excellent introduction to employing AI in my company.
Not only was it helpful in framing how I think about the role AI can play in building a sustainable competitive advantage, but it also introduced me to an important new idea; "data learning effects". Essentially, a single thread of data generates its own meta data, which in turn generates its own data and then its turtles all the way down. The benefits of harnessing this data and teaching computers to turn it into information are compounding in nature. It's through this continuous learning loop that breakout companies will emerge, and incumbents will learn to better serve their existing customers.
A big idea indeed.
As we stand on the precipice of the artificial general intelligence age, I recommend Ash's book to anyone looking for a more practical and applied understanding of AI. 5 stars.
Not only was it helpful in framing how I think about the role AI can play in building a sustainable competitive advantage, but it also introduced me to an important new idea; "data learning effects". Essentially, a single thread of data generates its own meta data, which in turn generates its own data and then its turtles all the way down. The benefits of harnessing this data and teaching computers to turn it into information are compounding in nature. It's through this continuous learning loop that breakout companies will emerge, and incumbents will learn to better serve their existing customers.
A big idea indeed.
As we stand on the precipice of the artificial general intelligence age, I recommend Ash's book to anyone looking for a more practical and applied understanding of AI. 5 stars.
May 7, 2021
Exceptional book on AI by Ash Fontana. Highly recommend! Also, check out a thought-provoking interview with Ash in the upcoming Strategy Skills podcast episode. This book and learning from Ash is worth your time.
June 15, 2021
Great book with practical advice on building AI-based startups. The concept of "Lean AI" and ideas around building successful teams in this space are very helpful. Highly recommended.
December 16, 2021
Love the premise of having "AI-first" companies.
Some of the stand-out excerpts from the book:
"AI-First companies put AI to work, prioritizing it within real budgets and time constraints. AI-First companies make short-term trade-offs to build intelligence in order to gain a long-term advantage over their competitors"
"Intelligence is determined by how fast you learn, and you’ll learn faster using machines. The automatic compounding of information is a data learning effect. Data learning effects = economies of scale to data + data processing capabilities + data network effects. Data learning effects compound faster than any other form of competitive advantage. Data network effects are where each incremental data point adds more information to a user of the network than the last data point."
"Defining data learnng effects. People tried to define AI in terms of scale, referring to data as the “new oil”—maybe because oil is an input, and the more of it you have, the better. People also tried to define AI in terms of networks, talking about data network effects —maybe because social networking was a major trend at the start of the big-data era. But neither scale nor network effects capture the power of AI. They don’t get to the definition of intelligence: learning fast."
"The difference between a network effect and a data network effect is what’s added to the network. With a network effect, something becomes more useful through the addition of communicative nodes to the network, whereas with a data network effect, something’s usefulness is enhanced by the addition of data to the network. What’s more, a network effect’s edges—the lines between nodes— are functional and communicate, as opposed to a data network effect’s edges, which are informational and calculate."
"DLEs are possible today because of the recent change in the three substrates. Economies of scale to data: there is lots of data going across the Internet, captured from the sensors on personal and industrial devices; Data processing capabilities: very powerful computers that can run calculations over this data, at a reasonable cost, and capable people who can make connections between disparate datasets; and Data network effects: researchers found ways to organize data into networks that run calculations on one part of the network, send the result to another part of the network for more calculations, and come up with new information. This is the field of neural networks or, more broadly, intelligent systems."
Some of the stand-out excerpts from the book:
"AI-First companies put AI to work, prioritizing it within real budgets and time constraints. AI-First companies make short-term trade-offs to build intelligence in order to gain a long-term advantage over their competitors"
"Intelligence is determined by how fast you learn, and you’ll learn faster using machines. The automatic compounding of information is a data learning effect. Data learning effects = economies of scale to data + data processing capabilities + data network effects. Data learning effects compound faster than any other form of competitive advantage. Data network effects are where each incremental data point adds more information to a user of the network than the last data point."
"Defining data learnng effects. People tried to define AI in terms of scale, referring to data as the “new oil”—maybe because oil is an input, and the more of it you have, the better. People also tried to define AI in terms of networks, talking about data network effects —maybe because social networking was a major trend at the start of the big-data era. But neither scale nor network effects capture the power of AI. They don’t get to the definition of intelligence: learning fast."
"The difference between a network effect and a data network effect is what’s added to the network. With a network effect, something becomes more useful through the addition of communicative nodes to the network, whereas with a data network effect, something’s usefulness is enhanced by the addition of data to the network. What’s more, a network effect’s edges—the lines between nodes— are functional and communicate, as opposed to a data network effect’s edges, which are informational and calculate."
"DLEs are possible today because of the recent change in the three substrates. Economies of scale to data: there is lots of data going across the Internet, captured from the sensors on personal and industrial devices; Data processing capabilities: very powerful computers that can run calculations over this data, at a reasonable cost, and capable people who can make connections between disparate datasets; and Data network effects: researchers found ways to organize data into networks that run calculations on one part of the network, send the result to another part of the network for more calculations, and come up with new information. This is the field of neural networks or, more broadly, intelligent systems."
September 6, 2024
This is about building Lean AI and Lean Startups doing AI. I liked the strongly managerial view. There's some insight on how VCs think about AI and the business of AI. There's a strong mathematical or at least formal indication of how to approach the topic. The AI technology is not described at a super deep level but there are succinct and clear explanations of a lot of the tech. What I found was a little taxing is that it's mainly, in my view, about data and data economies and points regarding this are often repeated. However, on the other hand, it's a really valuable book for AI startups and AI managers. There are some redundancies which one could argue are in the interest of clarity but at the rate at which information and even exact formulations are repeated it really does get a bit repetitive and stale in some sections.
October 11, 2021
A practical how-to guide for building AI-first companies based on data learning effects (a new type of competitive advantage that combines learning/network/scale effects) and strategies like Lean AI (the process of building a minimum viable AI to solve a specific problem). There are some interesting bits here and there, but for the most part, it’s rather dry reading. More useful as a reference than as a full-length book.
June 25, 2021
Really good read. No small feat to make such a complex topic accessible to the layperson but he pulls it off well here.
Lots to take away here whether you’re working in a startup or a large corporate. Highly recommend.
Lots to take away here whether you’re working in a startup or a large corporate. Highly recommend.
August 26, 2021
Very tactical - loved the specifics on "lean ai".
June 25, 2022
Not easy to read buy a lot of good ideas to think about when making new projects
August 9, 2023
A good read for business direction around AI. It helps understand what are the things to consider for a company to be a ai driven
June 23, 2024
interesting book showing how to build a company leveraging data science, analytics and AI approaches to gain a competitive advantage. the book is technical indeed, as a practitioner in data science i find it barely scratches the surface of what is feasible.
the main issue with this book is how relevant it was three years ago and how irrelevant most of the content is today (2024). most of the low level techniques are now hidden behind a human interface in AI (prompt engineering). no need to know any of those techniques anymore as the current AI does that and much more. scary to think what kind of books and advices will still be relevant in one year time. this one definitely not.
the main issue with this book is how relevant it was three years ago and how irrelevant most of the content is today (2024). most of the low level techniques are now hidden behind a human interface in AI (prompt engineering). no need to know any of those techniques anymore as the current AI does that and much more. scary to think what kind of books and advices will still be relevant in one year time. this one definitely not.
October 30, 2025
It was an insightful look at how businesses can harness AI to drive real impact. I appreciated how it goes beyond buzzwords and frameworks, showing practical strategies for integrating AI into decision-making and operations. It made me rethink how companies approach technology and innovation, emphasizing the mindset shift needed to truly become “AI-first.” The book is both inspiring and grounded, a solid read for anyone curious about the future of business in an AI-driven world.
March 2, 2025
Very little actual value. Author bundled a lot of famous keywords but associations and ideas are average at best. Nothing new really. If people would actually know how they would do rather than teach.
October 11, 2021
Ash has put together a first of its kind vision for an AI-first company. While some topics are still evolving, it is a good starting point.
March 13, 2022
worth reading as an introduction to the topic.
This entire review has been hidden because of spoilers.
November 12, 2024
Good book to read from the perspective of manage AI/ data product/company.
Displaying 1 - 18 of 18 reviews