Human + Machine
Reimagining Work in the Age of AI

By Paul Daugherty and James Wilson.
Book Review 7/10
A good book that brings attention to the new challenges that businesses, and especially management layers, face with the rise of AI possibilities. The authors provide several examples of how technology is shaping businesses. Unfortunately, those examples are very superficial. The book left aside reflections about the impact and moral implications that AI is raising on many levels and its respective social impacts.

November 2019
Book Review. Think Like a UX Researcher
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About the authors

Paul R. Daugherty (@PaulDaugh) is the Chief Technology and Innovation Officer of Accenture. He leads Accenture's Artificial Intelligence (AI) initiatives, as well as its R&D facilities around the world.

H. James Wilson (@HJamesWilson) is Managing Director of Information technology and Business research at Accenture Research. He is also coauthor of What's the Big Ideas? Creating and Capitalizing on the best Management thinking.

Other publications:

My notes and highlights

Despite the technological theme, it is a very easy book to the non-technological individual to read. IF you are an expert or and advance individual in the field of AI or Machine Learning (ML) this book will not bring much-added value to your knowledge.

But if you are a person how is interested and curious about AI and google is overwhelming you with information, this is a good book to start comprehending the impact of these promising technologies and starting to be familiar with some concepts.

I felt that the higher purpose of the book was to educate the reader on how to apply AI technology responsibly to the business in an innovation economy.The potential power of AI to transform business is unprecedented, that why the book focuses on how AI is changing the very nature of work. Requiring us, individuals, to reimagine our operations/process on dramatically different approaches.

The book describes how AI has and will take over the current business models and clarify the misconception that AI will replace the Human workforce. The customer experience is the next competitive battleground. Making happy customers is a priority above all. But to achieve this, the business and it manpower structures have to be prepared to support and empowering the business to deliver this type of service. And with AI, this will be essential for successful business output.

While the authors focus their arguments on how machines are capable of doing mundane, repetitive or time-consuming tasks that can free our valuable time for more complex or meaningful endeavors, the book left aside reflections about the impact and moral implications that AI is raising on many levels and its respective social impacts. The book focuses essentially on AI for Leaders, but if Leaders lack understanding or awareness of the moral impact of their decisions, this book serves no purpose to us Humans. Because while AI's latest technological advances have unlocked new levels of productivity and innovation, a range of unexpected consequences on society have caused discrimination and undercut human rights at an unprecedented scale. The book lacks mentions and reflections on this "dark side" of AI innovation.


The focus of the book is more business-oriented rather than on humans.

They start the book by doing a brief overview of the evolution of businesses in order to position their insights. They start with the first wave of business transformation which involved standardized processes.

Next, they exploit the second wave, which was the automation of processes that began in the 1970s and reached its peak in the 1990s. This 'business process re-engineering', was propelled by the ubiquity of computers, large databases and the automation of numerous back-office tasks. Many people were replaced by machines. Next, they explain the third wave, adaptive processes. On one hand, it rests on the previous two waves, on the other hand, is a completely new way of doing business. They explain how this combination is adapting the behaviors, preferences, and needs of workers at this given moment. It is been powered by real-time data rather than by a pre-organized sequence of steps.

The authors envision that when this last wave is fully optimized, it will allow organizations to take full advantage of AI. They will be able to produce individualized products and services which are satisfying beyond the capabilities of the mass-production of the past. And deliver more profit.

With all the benefits of the third wave, we need to have a much deeper understanding of how humans and machines must collaborate so that people are augmented and not replaced. The authors call this the "missing middle".
"systems that extend human capability by sensing, comprehending, acting, and learning."

Book Contributions

There is a widespread misconception that AI systems will gradually replace humans in one industry after another. To demystify this preconception idea, the author built the following scheme to indicates that although AI can be used to automate certain functions, the technology's greater power is in complementing and augmenting human capabilities.
Human + Machine - The missing middle
Figure 1: The missing middle (p.8)
The authors' method requires five deliberate changes which can be summarised by the acronym MELDS.

Rather than replace the need for humans. The first is a change of 'Mindset'. This requires reimagining the work, then discovering how people can improve AI, and how smart machines can give humans superpowers.

The second change is 'Experimentation'. Businesses need to be actively looking for parts of processes where AI can be introduced, and then learn and scale that process, with the enhancing power of people.

The third change is the responsible use of AI by the 'Leadership' of the business. It is too easy to look for the next quarter's improvement, and overlook the long-term catastrophe of unemployed people.

The fourth change is to see 'Data' in its rightful place. Data is the fuel of any intelligent system and, not unlike fossil fuel, requires the building of a data "supply chain" to ensure efficient delivery. Data is not a static body, but an ever growing one that requires the same attention to delivery that is required of any other resource.

The fifth change involves a new set of eight 'Skills' that need developing, that the authors call 'fusion skills'. Each skill draws on the fusion of human talent and machine ability within a business process, to create better outcomes than could be achieved working independently. These skills are more than learning what the machine can do; rather it is the machine learning from the person and the person learning from the machine.

The authors focus on the point that AI is here to perform the tedious grunt work, collecting data and doing the preliminary analysis in order to free human beings to perform work only they can. Such as:
The higher message that this book offers is when working together, humans and machines have the potential to create a superior, collaborative system, and achieve a better outcome than either alone. This belief lies in the idea that systems should be designed by using the strengths of both humans and machines.

The authors highlight a few challenges around this synergy:

1. Data management in corporations
2. Lack of deeply skilled AI talent
3. Responsible AI needs (moral use, bias, or security).

The authors explore this collaboration between humans and smart machines. And they clarify how AI technologies and their potential applications all fit together in the following scheme:
The constellation of AI technologies and business applications
Figure 2: The constellation of AI technologies and business applications (p.61)
For the non-technological readers, the authors provide and useful glossary mapping all these scheme terminologies.

Final Observation

Despite the concepts and theories that authors broth into the book, there was a particular section that most captive my attention. They brought an interesting example of how AI is shaping the scientific method too. They present Quid, a company that is using AI to reimagine the search-and-find part of the researchers' process (p. 69).
Quid technologies are using data science to surface the most relevant information speeding up the researchers' process and creating data visualizations that support patterns recognitions and network of ideas.
Their goal is to leave the person outside the pressure that often leave us stuck with the research we have time for rather than the research we need to. To the authors, Quid provides the next-level of observation. With this tool, the research questions come quicker, and they are more nuanced and incisive. For them, this application opens the gate to unexpected avenues of inquiry, leading to what the authors defend as "smart hypothesis" (p. 72).

Which rise an important question, what will happen to the scientific process and to design problem framing process when hypotheses can be generated automatically?


On one hand, the book is full of insights and great guidance for business leaders on how to apply responsible AI to their business in this innovation economy. On the other hand, the examples and case studies across the book to illustrate the author's ideas are very often quite superficial.

Nonetheless, the examples are a strong way to exemplify how AI is reshaping the business process. They are a window to dig deeper in AI-related case studies outside the book.

The book also lacks ethical considerations around AI implementations. Since the book is very oriented to management layers, the ethical implications of those individuals' decisions should have been considered in their level of analysis. However, it remained fairly shallow in regards to what possible ethical decisions need to be contemplated.

Also, since I am trying to conduct some research between the confluence of AI and Design, I felt frustrated quite a few times because many of the research citations refer to press articles rather than the original work which should have more value to me. Especially, If I wanted to use an example or case study in further research.
Paul Daugherty, Human + Machine
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