What To Do When Machines Do Everything: How to Get Ahead in a World of AI, Algorithms, Bots, and Big Data

by Malcolm Frank, Paul Roehrig, Ben Pring

“Refreshingly thought-provoking…” – The Financial Times

The essential playbook for the future of your business What To Do When Machines Do Everything is a guidebook to succeeding in the next generation of the digital economy. When systems running on Artificial Intelligence can drive our cars, diagnose medical patients, and manage our finances more effectively than humans it raises profound questions on the future of work and how companies compete. Illustrated with real-world cases, data, and insight, the authors provide clear strategic guidance and actionable steps to help you and your organization move ahead in a world where exponentially developing new technologies are changing how value is created.

Written by a team of business and technology expert practitioners—who also authored Code How the Digital Lives of People, Things, and Organizations are Changing the Rules of Business—this book provides a clear path to the future of your work.

The first part of the book examines the once in a generation upheaval most every organization will soon face as systems of intelligence go mainstream. The authors argue that contrary to the doom and gloom that surrounds much of IT and business at the moment, we are in fact on the cusp of the biggest wave of opportunity creation since the Industrial Revolution. Next, the authors detail a clear-cut business model to help leaders take part in this coming boom; the AHEAD model outlines five strategic initiatives—Automate, Halos, Enhance, Abundance, and Discovery—that are central to competing in the next phase of global business by driving new levels of efficiency, customer intimacy and innovation.

Business leaders today have two be swallowed up by the ongoing technological evolution, or ride the crest of the wave to new profits and better business. This book shows you how to avoid your own extinction event, and will help you;

Understand the untold full extent of technology's impact on the way we work and live. Find out where we're headed, and how soon the future will arrive Leverage the new emerging paradigm into a sustainable business advantage Adopt a strategic model for winning in the new economy The digital world is already transforming how we work, live, and shop, how we are governed and entertained, and how we manage our money, health, security, and relationships. Don't let your business—or your career—get left behind. What To Do When Machines Do Everything is your strategic roadmap to a future full of possibility and success. Or peril.

Genres: Business, Nonfiction, Technology, Artificial Intelligence, Science, Leadership, Audiobook

225 pages, Kindle Edition

First published January 1, 2017

Reviews

[Ronald J. · Rating 2 out of 5]

There are some nuggets in here, but nothing really new, except perhaps the AHEAD business model they lay out, which is all about Internet of Things, data collection, analysis, etc. I do tend to share the author's view that both the dystopian and utopian views of AI, Robots, etc., are both wrong. I, too, am an optimist when it comes to the ingenuity, creativity, and dynamism of free people working in free markets. We will always find a way to serve one another, and the idea that there will be no work is absurd. If that was the case, as economist Deirdre McCloskey points out, it would have happened a long time ago.

[Devin · Rating 2 out of 5]

Book was really high-level and while it attempted to be "strategic", the content is mostly fluffy consultant-speak and doesn't dig into the tactical "how to" of working with automation and AI technologies.

[Kıvanç Oktaş · Rating 2 out of 5]

Konuyu düzenli olarak takip edip ara sıra makaleler okuyan birisi için çok da farklı sözü olmayan bir okuma olacaktır.

[Mike Scialom · Rating 5 out of 5]

Artificial Intelligence is getting a bad press, and the authors of “What To Do When Machines Do Everything” want to call attention to the incredible opportunities this “new machine” offers.
Every such machine initiates a transformation – firstly a period of confusion (the one we’re in now), swiftly followed by a productivity boom. “The loom led to an abundance in clothing, the steam engine to an abundance in transoceanic travel, and the assembly line to an abundance of refrigerators finding their way into homes all around the world.”
In each case, “technology has given and technology has always taken away. Automation and technological substitution of human labour are facts of life. More important, these dynamics are a good thing. More tools mean more leverage which means more efficiency, which means more productivity.”
Not much element of doubt there – no prizes for guessing which side of the battle line of utopians vs dystopians the three authors are on when they say that “we are in an incredible time, when technology is significantly extending the envelope of human capability”. But you’d be wrong, for Cognizant Technology Solutions’ trio of Malcolm Frank, Paul Roehrig and Ben Pring say neither side can lay claim to the future. They see better living standards; better, more satisfying jobs and “new experiences” invented – plus pressure on wages as the “I, Daniel Blake” fate of AI “leaving behind those unable to keep up and compete” kicks in. Hard. In the US, they predict 12 per cent of the workforce will see their jobs being automated: that’s 19 million souls. But they estimate that 21 million new jobs will be created by AI.
Whatever the final tally, it’s pointless to resist. “Automation is a deep and unstoppable force.” Luddite thinking is futile. “In the context of the moment, their arguments had merit. Yet in the context of history, as we recognise how the loom clothed the world, established a foundation for global trade, initiated the growth of a large middle class, and launched various related industries, they were wrong.”
Much of the reason that AI freaks people out is down to a misunderstanding of what it is. There’a actually three types of technology in the mix. There’s ANI – Artificial Narrow Intelligence – which is task-specific, such as “driving a car, reviewing an X-ray or tracking financial trades for fraud”.
Second is AGI, or Artificial General Intelligence. “Creating AGI is a dramatically harder task than creating ANI; by most estimates we are still more than two decades away from developing such AI capabilities, if ever.” What capabilities? Well, the time it takes for the average American to make a coffee is “currently insanely difficult for a computer”. The tasks – “identifying the coffee machine, figuring out what the buttons do, finding the coffee in the cupboard etc” – are fraught for a machine. Incidentally it’s this type of AI which is fuelling the fears of the singularity crowd, which appears to include SoftBank founder Masayoshi Son, who estimates that the singularity – the cross-over point at which machine intelligence will out-think human intelligence – will be passed next year, according to his talk at this year’s Mobile World Congress in Barcelona. The crew here have a different take on the singularity. “By most estimates we are still more than two decades away from developing such AI capabilities, if ever.” Ironic that a singularity can be so divisive!
The third definition of AI is “super AI” which “in essence, is the technical genie being let out of the bottle. In such a scenario, would humans even know how to stop such a machine?… How could we then turn the machine off when it’s always 10 (or 1,000) steps ahead of us?” The authors take refuge in the knowledge that more computational power doesn’t always mean more actual IQ – “as we know,” they point out, “whenever 10 reasonably smart people are put in a room, the collective IQ is not 1,200 but actually somewhere around 95 once one accounts for the different opinions and objectives that people always bring”.
Going digital will involve everything – literally every thing – becoming a code generator. But it’s not just about harnessing the power of this new machine – you need the right business model. “The more technology enhances us, the more it creates the opportunity for a human touch.” So Pret A Manger offers a hugely efficient service, but its core branding is based on its staff, who are “quirky, fun and engaged”. Apple has eliminated the check-out desk and put its sales in the hands of its floor staff, who carry the automated tools required with them. “Our work ahead will require us to double-down on the activities have and will continue to have an advantage over silicon.” Many tasks will be done by machine, but jobs in financial services to teachers to lawyers to health care will remain, with AI making them easier by providing them with the information which would currently take considerable time to gather, learn or research. And ultimately the future is all about “building the new machines that allow us to achieve higher levels of human performance.” In our brave new world people will always be the ultimate X-factor.
This book, beautifully enthused by its subject, has just one elephant in the room. It’s titled “What To Do When Machines Do Everything” but about the lost employees the trio are curiously inconclusive. The problem is best described in the 1930s by the very wonderful John Maynard Keynes of this (Cambridge) parish, who said that it doesn’t matter what work people do – they could dig holes and fill them in again for all it matters – because the important part of the equation is that people have earned money to spend otherwise yes, production is cheaper, faster, more creative… the supply is enhanced, but if people have no cash then demand is decimated. What then? What will people do? The argument that there will be more time for artistic pursuits, voluntary work and charitable causes only goes so far.
And so, in a way, it’s inevitable that AI will be viewed with concern: until we fully feel the benefits, it’s going to be challenging times. But at the end of the day, would a car park attendant want to go back to the days when he or she spent the whole day giving change and handing out tickets? Or going ahead just slightly, will people not invest in a driverless car because they insist on doing the driving themselves? And will AI actually be able to feed the world?
- This review appears in the May edition of Cambridge Business magazine, pages 57-59. http://edition.pagesuite-professional...

[Andrei Scurei · Rating 2 out of 5]

The book has a bunch of good ideas but really that's about it. They could have been stated and explained in 5 pages. The authors rarely bother with factual data to back their claims (apart from a bunch of companies used as anecdotal evidence and reiterated obsessively throughout), which is shocking for a book insisting so much on the value of data. The same ideas get repeated unnecessarily throughout the book. Also, I'm annoyed by the choice of target audience of this book. This book is really only written for high level managers of big corporations. That's it. Everyone else is collateral damage in the forecasted AI revolution. Even as a software engineer in one of the companies the book praises, I find myself very limited in what I can apply from this book. I recognize the ideas in it and I'm happy to see they're being applied, but realistically there's little I can do to apply these myself. If you're working for McDonald's and want to know what you can do, this book basically tells you that you'll get fired, but maybe if you don't fight the new technologies you won't. That's really the only takeaway from this. this This is fine, but at least make it obvious in the title that it's only for CEOs. For people that don't have the entrepreneur obsession, this book is completely useless.

[Alexis Chateau · Rating 4 out of 5]

This was a very interesting book that kept me engaged the whole time. However, despite the authors claiming to have studied the topic extensively, they focused overwhelmingly on the positives and glossed over the real or potential negatives. I would argue the book was overly optimistic about the way AI would impact the job market, particularly in light of how reality has panned (The book was written in 2017) out. If anything, it reads like a persuasive argument for leaders to adopt AI, versus actually telling people what to do after AI "takes over".

(Required reading for school)

[Jack Duncan · Rating 3 out of 5]

This book is great for MBA students and actual business executives, because it is essentially as a guide for how to adapt a company for the 4th industrial revolution. If you're like me and are more interested in the policy implications and economic trends of the data the economy, then you might not want to put this on the top of your reading list. However, the information is pretty insightful and the authors did a good job of engaging the reader.

[Rathi · Rating 4 out of 5]

This is a good primer to both camps of people - the one that thinks AI and advanced analytics are just passing fads and those that thinks that Robots will take over the world in a few years. This book takes a balanced approach towards the impending boom and explains what one must do in preparation.

[Meltem Ozker Gunduz · Rating 4 out of 5]

Bir gun makineler herseyi yapacak. Ama o gun, yine sadece insana ozgu islere de ihtiyac olacak. Simdiden kendimizi gelecek icin hazirlamaliyiz. Bu kitabi okuduktan sonra “data obsessed” olmak icin herseyi yapacagim...

[Abhi · Rating 1 out of 5]

You'd think people would know when a blog post would've been enough.

[Krish · Rating 3 out of 5]

My rating for this book would be closer to 3.5/5. Rather than a book, I would call this a well-written essay on the current state-of-the-art in technology, what to expect in future and what businesses should be doing (now). Although there is no singular novel idea, it is an amalgamation of good practical advice and/or paraphrased ideas. I found a few of these to be interesting:

The question "How do I make my business model/process better using this specific technology?" is not the right one. Instead, ask "How would I design my business model/process given that I have this specific technology?". The former is likely to create an e-commerce platform for sharing video tapes and CDs, whereas the latter creates a video streaming platform like Netflix.

There is a difference between the terms, job and task. A job is composed of numerous tasks. AI will likely automate most tasks in future but not necessarily most jobs. In essence, the future will see a move towards jobs which consist of intellectually challenging and rewarding tasks.

Businesses should align the 3 M's: raw Materials, Machines and business Models in order to create products of the future. The role of humans (if they want to be relevant) should move towards innovative and creative business models that leverage the utility of the state-of-the-art machines.

Businesses should focus on getting AHEAD by:
Automating everything possible (e.g., automate the repeatable and boring work of answering FAQs via customer care using speech recognition and synthesis)
Instrumenting, i.e., creating a code Halos around everything possible (e.g., attach sensors to shoes to get fine-grained human health data)
Enhancing humans with technology (e.g., create bionic arms to help humans lift heavy weights)
Create Abundance of product/service delivery using technology that will in turn create more and possibly a different market opportunity
Create a culture of Discovery and innovation to avoid stagnating

[Luis F. · Rating 3 out of 5]

Me acerqué a este libro pensando que se trataba de un ensayo de divulgación científica sobre el campo de la Inteligencia Artificial. Por desgracia, se trata más de un tratado empresarial orientado a la implantación de estas tecnologías en la empresa moderna, por lo que aunque tiene algunas ideas interesantes, no el mejor libro para acercarte al tema desde una perspectiva técnica, o simplemente curiosa. Es más, el enfoque ultraliberal del libro, que ignora conscientemente la dimensión social o humana de la introducción de estas tecnologías en el mundo laboral, da bastante grima. No ha sido de mis lecturas más gratificantes este año, me temo.

[Leonardo Longo · Rating 5 out of 5]

A great book, illustrated with cases studies, precise data, and insight, in which the authors provide clear strategic guidance and actionable steps to help people and organizations, offering balanced, insightful and pragmatic advice on how to do AI right.
The authors provide a non-alarmistic point of view that artificial intelligence makes certain jobs obsolete, but take an optimistic and pragmatic overview of where AI stands today, how it will impact both individuals and organizations.

[Al · Rating 3 out of 5]

Contenido bastante ligero para un tema con tantas aristas. Da una versión de algunas grandes empresas, pero ya parecen de hace tiempo ya que el libro es de 2015, y es un campo muy cambiante. También parece que el lector objetivo es un gerente de una pequeña o mediana empresa, con lo que algunos de los puntos de vista quedan lejos de lo que se puede aplicar en éstas.

[Ella · Rating 5 out of 5]

Great really well-research hand written study on the digital revolution. Popular enough for a layman, and has some really interesting insights for technology executives as well...

[Michael · Rating 3 out of 5]

Might be just me, and the books I've read before, but it seemed far to generalist to be of much benefit. Then again that probably makes it a good starter read on Business AI.

[Юра Мельник · Rating 2 out of 5]

Не дуже відрізняється від інших книг на подібну тематику.

[William Schram · Rating 3 out of 5]

In What to Do When Machines Do Everything the book asks that all important question. The authors start by averring that we are in the midst of another economic boom that is taking time to rev up. All of the previous economic super explosions went through the same process. These three revolutions created unprecedented wealth in a select few with an eventual spread of that wealth in a more democratic manner. I honestly don’t know if I can swallow all of that hogwash, but the authors have statistics and historical records to back up their claims. The first super boom was with the advent of the Power Loom. The Luddites realized that their skills would go to the wayside, but it allowed more people to work on weaving and making textiles. The second boom was with the Steam Engine. It made horses less useful. The third boom was with the Assembly Line.

As with every boom, they go through an established pattern. First comes the innovation, then comes a bubble, then the bubble bursts and there is an economic downturn, and finally, the technology finds a stable point to reside in. This manifests itself in an S-curve. With advanced Artificial Intelligence, the same rules apply. I guess the main introduction was with the Internet back in the late 1990s. I recall the dot-com bubble, but I was a bit too young to care at the time. Following that came a long period of economic downturn with the housing bubble and the Great Recession. Now we just have to wait for AI to go and take a great many jobs. For instance, the authors dream of a future where your car will self-diagnose an issue, drive itself to a repair station, and be back in time to take you home from work. Wow. I can foresee a future wherein we all become like the people from the Dune novels if that is the case.

I have issues with AI driving in the first place. It isn’t that it is a bad idea, far from it. I just don’t know if people will go for it. I mean, on the one hand, more and more people love to text and drive. It might be some kind of perception bias on my part, but it does seem that way. Automated cars would enable you to text ridiculous things all you want. On the other hand, I don’t think the car would break the speed limit, and from how people drive where I live, that is something that would need to be rectified. The car probably wouldn’t break any other driving laws either, and where I live there are people that run stop signs and red lights. Would such technology make it easier to enforce some kind of police state Dystopia? I don’t know.

This book didn’t really fill me with hope since I am pretty sure that my job can be replaced quite easily. So we will see what becomes of everyone as AI can compose better music, play Chess and Go better, and do all sorts of things that we always envisioned as being uniquely human.

[Jung · Rating 5 out of 5]

"What To Do When Machines Do Everything: How To Get Ahead In A World Of AI, Algorithms, Bots and Big Data" by Malcolm Frank, Paul Roehrig, and Ben Pring, discusses the impending revolution in the business world due to the rise of automation and artificial intelligence (AI). The book highlights the impact of new technologies on industries and jobs, offers insights into how companies can adapt to the changes, and provides actionable advice for both individuals and organizations to thrive in this new era.

The book is divided into several sections, each focusing on a specific aspect of the automation revolution. The authors begin by discussing the history of technological advancements and how they have affected the workforce over time. They draw parallels between the current situation and previous industrial revolutions, emphasizing that while new technologies may cause disruptions, they also create new opportunities and jobs.

The authors then delve into the role of data in the new digital economy. They explain how businesses can use data to gain insights into customer behavior, improve decision-making, and drive innovation. They also highlight the importance of business analytics in turning data into actionable insights.

One of the key themes of the book is the need for organizations to transform their business models to embrace digital technologies. The authors argue that companies must move away from traditional paper-based processes and adopt digital strategies to remain competitive in the digital age. They provide examples of companies that have successfully made this transition and offer practical advice for others looking to do the same.

Another important topic covered in the book is the impact of automation on jobs. The authors discuss the potential for job displacement due to automation and AI but also highlight the creation of new jobs and opportunities. They stress the importance of continuous learning and skills development to adapt to the changing job market.

The book also addresses the role of leadership in navigating the automation revolution. The authors argue that leaders must embrace digital transformation and empower their employees to innovate and adapt. They provide examples of companies with visionary leaders who have successfully led their organizations through digital transformations.

Overall, "What To Do When Machines Do Everything" is a comprehensive guide to the automation revolution and its implications for businesses and individuals. The book provides valuable insights and practical advice for anyone looking to thrive in the digital economy.

[Arti · Rating 2 out of 5]

The book introduces the system of intelligence... it explains the 3 distinctive elements that set it apart - software that learns, massive hardware processing power and enormous amounts of data... and emphasises the value that the system brings with its ability to learn... and the potential to disrupt exiting business processes...

it suggests the need for new business models that are built on data driven personalisation and drive enhanced experiences and abundance... and calls data, system of intelligence and new business models as the three raw materials of the digital age...

it suggests that the goal should be set up high so that you are forced to think differently... it uses data to show that a target of 25-30% cost reduction and similar revenue increase is achievable as many organisations today are already seeing 8-10% impact with their initial efforts...

The book is mostly targeted at executives who are grappling with the hype of AI and looking to define their strategies for data, automation, and AI. It lists down the steps to take to embark on this journey, suggests best practices, and warns readers with typical pitfalls... it outlines a framework AHEAD - (1) automate to strip costs, speed processes, improve quality instrument, and achieve scale (2) instrument to turn everything into a data generator (3) enhance people and systems thru new technologies to improve performance levels (4) lower prices to increase size of market and make your offering abundant (5) drive innovation to discover new futures.

It turned out to be a light read for me as the subject was familiar and there were no new ideas presented... the suggestions are simplistic and obvious and the challenge is in execution... the biggest roadblock - that I have seen - is in change management required to drive the level of transformation that is needed and that gets just a cursory mention...

It fails to spark ideas or stimulate the thinking... its more an informational or an operational book and misses the opportunity to make the readers think differently... It would have been more fun if there were at least a few contentious ideas thrown in... a few questions left unanswered... (for example I had read the book Prediction Machines last year and it had brought an interesting twist to the AI story by reducing everything to a simple prediction - you could agree or not - at least you were forced to react!)

Nonetheless, the book will be an easy introduction for people outside the AI community - and probably that’s the target of the book - and they will find it comprehensible...

[Pablo María Fernández · Rating 2 out of 5]

Es muy similar al anterior libro de los autores, repitiendo -textualmente incluso- temas y conceptos como IOT, Big Data, los tres horizontes, etc.). Es también una lectura liviana que no informa más que un video de YouTube o que algún artículo de un blog.

El tema de la inteligencia artificial siempre me interesó y en los últimos años con la nube, las nueva fuentes de datos y la capacidad de procesamiento mucho de lo que era teoría ya es realidad. Google Photos nos permite encontrar una foto de hace diez años tan solo escribiendo algo acerca de ella, Snapchat o Instagram ponen filtros de rostro en tiempo real: hoy nos parece natural pero detrás hay mucho machine learning.

Hay dos opciones para encarar este tema: un libro técnico donde se explore a fondo deep learning, procesamiento de lenguaje natural y otras ramas, o uno que apunte más al aspecto filosófico o cultural. Por su título creí que este libro era lo segundo pero no alcanza a cubrir ninguno: tiene profundidad de PowerPoint o de charla en un evento por lo que el formato libro le queda grande, incómodo. Cita textos militares como lo hicieron antes y mejor Al Ries y Jack Trout, y recomienda hacer ‘safaris digitales’ por Silicon Valley como una estrategia para acelerar el aprendizaje sobre el tema.

De todas formas rescaté algunos conceptos: la distinción entre job y work o tarea y trabajo (que está compuesto por una cantidad de tareas), los tres tipos en los que cambia la tarea por automatización, mejora o creación y el informe de Forrester Research sobre cómo afecta el avance de la inteligencia artificial a cada profesión. También señala dos verdades no siempre confiesas: que casi nunca se apagan o matan los sistemas legacy (antiguos) sino que la mayoría de los nuevos se construyen encima de sus predecesores convirtiéndose la infraestructura tecnológica en un plato de spaghetti. Coincido también en la desacralización de la inteligencia artificial: “no es un unicornio mágico, es el siguiente nivel en herramientas de productividad.”. Por último me gustó un consejo que vi en algún otro lado pero que es muy potente y reencarna la filosofía Gillette de canibalizarse a uno mismo ante que lo haga la competencia: “Pedile a tus colaboradores más brillantes que maten a tu compañía.“

[Niketan Rao · Rating 4 out of 5]

The authors are from Cognizant Centre for the Future of work. Overall, they have given a good overview of the digital future. There are good and varied examples throughout the book ranging from the Netflix system of intelligence to Devi Shetty’s Narayana Health.

The interesting portion is their framework for automation (also a call to action) with the AHEAD roadmap which is an abbreviation for Automate, Halo, Enhance, Abundance and Discovery.
Some of their recommendations seem quite straight forward for practitioners especially the Process automation targets.

The authors provide a useful technical break down of the overall System of Intelligence and also provide useful lists of candidates for automation – both Back office and Middle office.
There are some areas which are treated somewhat superfluously such as the authors conviction that jobs would be enhanced via automation. This may not be necessarily true as was seen in the US when blue collar jobs moved to China and other low-cost countries. The rust belt states played a major role in Trump’s election.

Overall, the strength of this book are some concrete roadmaps and good pointers on the “how to automate and what to do”. The weakness is some examples such as WeWork and Uber are looking precarious now. Even the Netflix algorithm which possibly could provide a good indicator of the shows being watched and could possibly work in the US market. However, this does not work for green lighting new shows – particularly in the Indian market where it still lags behind other streaming services and seems to have become a dumping ground for flop, trashy filmmakers.

[Dimitri · Rating 5 out of 5]

📕 Why (Not) to read this book (Target Audience)

In the information age this book should be dated. But it isn`t, great insights how technology should enhance humans.

👀 How this book changed my daily live (Takeaways)

Curiosity is a human trait: why, who, when, what
• Technology created longer work days
• Data is a liability before it is an asset
We did not get smarter, are tools got smarter.
• More tools = more efficiency
• The machine as coach

⁉ Spoiler Alerts (Highlights)

It is curiosity, something that is the key defining characteristic of intelligence (as it is currently manifested in our human form). From our first words to our first steps, to our first journey, it is intrinsic to our very being to want to know who, what, why, and where. Nobody tells us to ask questions. No parent, or teacher, or TV show, or social media feed tells us or programs us to want to know what's up. We just do.

Thus, AI is not about building robots that ape human form and behavior.

[SVEN · Rating 2 out of 5]

The author reassures the readers that with new inventions come new aspirations and new areas of employment. While I by no means expect societal collapse as the result of AI or automation I do get the sense the author glosses over how much human blood is spilled with technology leaps directly or indirectly : Spanish horse technology and south american society; steam technology and colonial expansion; nucleair technology and tsjernobyl, fukushima; fossil fuel and global warming.

As we see with COVID having people excluded from the social interaction and status resolving mechanisms of work unsettles many minds. Not having to work, even when that doesn't bring about hardships, will still not be as ideal to most as it seems to some. Idle hands do the devil's work is not a proverb to be forgotten lightly.

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[Darrell McCauley · Rating 3 out of 5]

Recommended by Jorge Viso, President of APA, "What to Do When Machines Do Everything" paints a plausible picture of how automation will change everything and argues that we are on the cusp of a revolution. Revolutions like this, from the rear view mirror, look like they occur rapidly. However, revolutions are a series of incremental steps. This is a good book for business leaders whose plans have a 50-year outlook beyond the myopic quarterly/annual reporting grind. It makes a good companion to "Good to Great" by Jim Collins.

[Puugu Menikheim · Rating 4 out of 5]

A great review of the pros and cons of a future where artificial intelligence is a common tool. This book draws on historical research about previous major shifts in how work is done, looks at realistic applications of A.I. in thr present and near future, and backs up its arguments with facts and sources. It talks about the pros and cons of the shift, addresses common concerns, and talks about the ultimate benefits. Overall, I'd say the authors take a pragmatic-leaning-optimistic view. Definitely worth a good read and very accessible.

[Michel Kant · Rating 5 out of 5]

Must read for forward thinking business leaders, entrepreneurs and innovators.

It's also great fuel for those who believe in the AI future and want pragmatic information on how to sell this future or today to those who live in the past.

The book reads easy and is very straight forward with great references and research from the cognizant team themselves.

From now on this book is my standard as what I like to expect from AI / forward thinking books.

[Brandon Bejsovec · Rating 5 out of 5]

This is a great guide that takes a look at emerging technologies in business and provides a set of guidelines for improvement in organizations. It's a really engaging look at applying changes to your organization to remain current in today's digital age. AI as systems of intelligence vs the Hollywood image paints a very pragmatic look at the current and immediate future state of AI in businesses.

[Tom · Rating 3 out of 5]

Two hundred pages merited only one dog-ear. It's not the repetition, or the lack of insightful examples, it's the lack of actionable instruction. Pointing to the arthritic burden of legacy systems is old hat, giving 1-2-3 suggestions on how to actually kill those systems is omitted. Usually the systems die when the company does... Best reading is account page 150-155, the rest deserves a speed read.