Book Review: Human + Machine

Book Specifications

Title: Human + Machine: Reimagining Work in the Age of AI

Authors: Paul R. Daugherty and H. James Wilson

Formats: Hardcover, Kindle, Audiobook

Publisher: Harvard Business Review Press

Published: March 2018, 1st edition

Pages: 264

ISBN-10: 1633693864

ISBN-13: 978-1633693869

The Book’s Organization and Content

I’ll provide a brief overview of the book’s organization and content, sharing some key concepts.

Introduction: What’s Our Role in the Age of AI?

Daugherty and Wilson introduce a few key concepts of the book, first setting the context by describing three historical waves of business transformation:

1. The first wave—Henry Ford’s deployment of the assembly line in 1913 launched the era of mass production in factories, with standardized, linear, step-by-step processes whose efficiency companies measured and optimized over time.
2. The second wave—Beginning in the 1970s and culminating in the business process reengineering (BPR) movement of the 1990s, advances in information technology enabled automated processes in the back office.
3. The third wave—AI is now transforming business processes to complement and augment human capabilities. In an emerging symbiosis between man and machine, “machines are doing what they do best—performing repetitive tasks, analyzing huge data sets, and handling routine cases. And humans are doing what they do best—resolving ambiguous information, exercising judgment in difficult cases, and dealing with dissatisfied customers.” These adaptive business processes consistently deliver better business outcomes.

The authors’ research has shown that “the leading companies in various industries … are already riding the third wave. They have maximized automation and are now developing the next generation of processes and skills to capitalize on human-machine collaborations.” By leveraging real-time data, these companies are reimagining adaptive processes.

The Missing Middle

Daugherty and Wilson introduce their concept of the missing middle—an unfortunately negative term for what is really a positive concept, so one that I think is unlikely to endure in the long term. This term refers obliquely to the ability of AI systems to amplify human skills and collaborate with us to achieve huge gains in productivity that have previously been impossible—a huge win. The authors refer to this concept as missing because few are even talking about it now and only a small fraction of companies are working toward achieving this goal. Figure 1 depicts the authors’ model of the missing middle, to which they refer throughout the book.

Studying early adopters of AI has clarified the authors’ view of the future, enabling them to identify the different ways in which companies can fill the missing middle by creating new, enhanced jobs that deliver unique economic and employment opportunities.

It is this missing middle that has resulted in today’s polarizing jobs debate, which pits humans against machines.

The MELDS Framework

Daugherty and Wilson also introduce their MELDS framework. As their research has shown, the leading companies that are successfully developing next-generation, adaptive processes and the skills that are necessary to capitalize on human-machine collaborations are accomplishing this by adopting five crucial principles relating to:

1. Mindset
2. Experimentation
3. Leadership
4. Data
5. Skills

The books’ authors refer to these principles collectively as their MELDS framework. They cover the first four principles in depth in Chapter 7; the fifth principle, in Chapter 8.

Part One: Imagining a Future of Humans + Machines … Today

Part one of the book describes “the current state of AI in companies”—“how executives are deploying AI in their businesses.” Daugherty and Wilson posit that “those companies that are using machines merely to replace humans will eventually stall, whereas those that think of innovative ways for machines to augment humans will become the leaders of their industries.”

Chapter 1: The Self-Aware Factory Floor: AI in Production, Supply Chain, and Distribution

The authors explore AI in manufacturing and supply chain, in factories where humans are working in partnership with smaller, more adaptable, context-aware robots. They describe how flexible, highly productive human-machine teams “fulfill customized orders and handle fluctuations in demand.” This chapter also takes a quick look at the use of AI in unmanned vehicles and concludes with a brief history of AI.

Chapter 2: Accounting for Robots: AI in Corporate Functions

This chapter focuses on the role of AI in business processes and back-office operations. Daugherty and Wilson affirm: “AI technology can help filter and analyze streams of information from a variety of sources and enable the automation of tedious, repetitive tasks as well as the augmentation of human skills and expertise.” The authors consider how to determine what processes to change and how much to change—even to the extent of redefining an entire industry—and explore reimagining processes around people. The chapter concludes with an excellent glossary of the many AI technologies in use today—covering the machine-learning component, AI capabilities, and AI applications.

Chapter 3: The Ultimate Innovation Machine: AI in R&D and Business Innovation

The authors describe “how companies are using AI in research and development. In each major step of the R&D process—observation, hypothesis generation, experiment design, and results analysis—AI technologies can lead to increased efficiencies and markedly improved outcomes.” Sidebars discuss learning from failure, AI in product and service design, responsible AI—including ethics as a precursor to discovery—and AI in healthcare and life sciences.

Chapter 4: Say Hello to Your New Front-Office Bots: AI in Customer Service, Sales, and Marketing

Daugherty and Wilson look at the impacts of machine-learning technologies on customer service, sales, and marketing. For example, Amazon’s Alexa, Apple’s Siri, and Microsoft’s Cortana “are increasingly becoming the digital embodiment of those companies’ well-known brands.” Sidebars look at AI in retail sales and AI in the sales and marketing process.

Part Two: The Missing Middle: Reimagining Processes with AI

Part-Two Introduction

Part two explores the so-called missing middle in depth, providing an executive guide to reimagining traditional work processes. Daugherty and Wilson contend: “To exploit the full power of AI, companies must fill that gap by considering new employee roles, by establishing novel types of working relationships between humans and machines, by changing traditional concepts of management, and by overhauling their very concept of work itself.”

Chapter 5: Rearing Your Algorithms Right: Three Roles Humans Play in Developing and Deploying Responsible AI

Daugherty and Wilson describe “how humans are helping machines to extend and amplify their capabilities.” This chapter considers in depth the ways in which humans can complement machines by training, explaining, and sustaining them. It also describes specific jobs and responsibilities for trainers, explainers, and sustainers. The authors state, “Machine learning, when integrated into processes, will lead to a variety of brand-new jobs. … Employees will be needed to design and train algorithms, to explain the algorithms used, and to do so in a way that sustains the algorithms within a process.”

Chapter 6: Super Results from Everyday People: Three Ways AI Unleashes New Levels of Productivity

In this chapter, the authors discuss the superpowers that AI can give humans, describing three types of augmentation through which “people are achieving huge performance boosts by working with AI technologies that dramatically improve their human capabilities; they amplify, interact, and embody new human potential. These new types of human-machine relationships are helping people … by offloading tedious tasks and by enabling them to perform their work faster and more effectively through the expert guidance, advice, and support from AI systems.” The chapter describes specific jobs for AI agents that amplify, interact, and embody.

Chapter 7: A Leader’s Guide to Reimagining Process: Five Steps to Getting Started

Daugherty and Wilson discuss the challenges that AI introduces, which “require different, new responses from management and leadership.” The authors consider the first four of five crucial steps that executives must take to facilitate the reimagining of processes. These are the first four practices of the MELDS framework, as follows:

1. “Mindset—Imagine processes that might be.” “Executives must adopt the proper mindset, with a focus on not just improving business processes but rather on completely reimagining business processes and the way that work is performed.” They must discover, describe, co-create, scale, and sustain.
2. “Experimentation—Imagine an experiment.” “[Executives] need to foster a culture of AI experimentation that allows them to quickly realize how and where the technology can change a process, and where it makes sense to increase the scale and scope of a process.” They must build, measure, and learn.
3. “Leadership—Imagine a blended culture of people and machines.” “[Executives] must exercise the proper leadership in promoting responsible AI by managing the trust, legal, and ethical concerns that accompany AI and by considering the societal consequences of some process changes.” They must install guardrails, use human checkpoints, minimize protections for the technology itself, and consider legal and psychological issues.
4. “Data—Imagine a data supply chain.” “Executives need to recognize the crucial importance of data, not just their firm’s own AI-enabling data but also the broader landscape of available data.” They must think dynamically, widen access and increase variety, increase velocity, enable discovery, and fill the missing middle.

Chapter 8: Extending Human + Machine Collaboration: Eight New Fusion Skills for an AI Workplace

This chapter looks at the fifth step that executives must take as human-machine collaborations become more prevalent. Daugherty and Wilson consider the future of work and hiring for and developing eight fusion skills that are essential to an AI workplace.

Identifying “the concept of fusion skills—humans and machines coming together to [create] new kinds of jobs and work experiences [that form] the missing middle”—was a key discovery of the authors’ qualitative research.

Fostering these fusion skills constitutes the fifth practice of the MELDS framework. These skills include the following:

1. “Rehumanizing time—Reimagining business processes to amplify the time available for distinctly human tasks and learning.
2. “Responsible normalizing—Shaping the purpose and perception of human-machine collaborations as [they relate] to individuals, businesses, and society.
3. “Judgment integration—Choosing a course of action amid machine uncertainty.
4. “Intelligent interrogation—Knowing how best to ask an AI agent questions, across levels of abstraction, to get the insights you need.
5. “Bot-based empowerment—Collaborating with intelligent agents to punch above your weight at work.
6. “Holistic melding—Developing mental models of AI agents that improve collaborative outcomes.
7. “Reciprocal apprenticing—Teaching AI agents new skills while also undergoing on-the-job training to work well within AI-enhanced processes.
8. “Relentless reimagining—Thinking of novel ways to overhaul work, processes, and business models to obtain exponential increases in improvement.”

Conclusion: Creating Your Future in the Human + Machine Era

Daugherty and Wilson conclude that “it’s within [the] missing middle that leading-edge companies have been reimagining their work processes, achieving outsize improvements in performance. To obtain such results, … executives must lead their organizations through the transformation by making the necessary investments, including training workers to fill those missing-middle roles.”

“Today, 61% of activities in the missing middle require employees to do different things and to do things differently,” the authors report. Therefore, companies must reimagine their work processes and “reskill their employees.” Employees’ new jobs include training data models and “explaining and responsibly sustaining an AI system’s performance.” Doing things differently means employees’ “using amplification, interaction, and embodiment to get a job done with superhuman performance.” When humans and machines do what they do best, “the result is a virtuous cycle of enhanced work that leads to productivity boosts, increased worker satisfaction, and greater innovation.”

According to the authors’ research, “companies that use AI to augment their human talent while reimagining their business processes achieve step gains in performance, propelling themselves to the forefront of their industries. Firms that continue deploying AI merely to automate in a traditional fashion may see some performance benefits, but those improvements will eventually stall.”

Daugherty and Wilson predict a huge performance gap between the winners and the losers—based “not on whether an organization has implemented AI, but on how….” AI gives humans superhuman capabilities. It has “the potential to rehumanize work, giving us more time to be human, rather than using our time to work like machines.”

Back Matter

The book’s back matter includes a brief postscript, quite extensive chapter notes, a detailed and thorough index, acknowledgments, and author profiles.

Key Messages for UX Professionals

In Human + Machine, Daugherty and Wilson discuss some potential impacts that AI could have on the future of the UX professions:

“New kinds of jobs may emerge in the front office. As bots become critical components of the customer-service infrastructure, for instance, their personalities will need to be designed, updated, and managed. Experts in unexpected disciplines such as human conversation, dialogue, humor, poetry, and empathy will need to lead the charge. Moreover, in the new world of augmented and automated work, user interface and experience designers will have utmost importance, as the interface between people—whether an organization’s customers or its employees—will have a disproportionate impact on whether an AI-based product or service survives and thrives, or if it fails.”

Thus, Daugherty and Wilson see an expanding role for UX professionals in the human-machine era. Both our soft skills and our professional skills make us uniquely capable of providing solutions to many of the problems that AI presents. The book’s authors have also identified new opportunities for UX professionals to apply both their deep understanding of people’s needs and their creativity in addressing the demand for new types of user interfaces:

“New augmentation-based relationships demand new kinds of human-computer interfaces. What user interfaces (UI) will dominate in the missing middle? Is AI the new UI? How might augmentation affect [an] industry?”

UX professionals can help provide answers to the many questions that arise as companies adopt AI and design the new interfaces that AI systems require. Daugherty and Wilson recognize the value that User Experience provides in determining what users’ need, as well as the value of the methods we use in addressing those needs when designing new products and features or resolving pain points that users encounter while using a company’s existing products and services. They specifically describe this value, as follows:

“One effective means is to deploy a methodology like design thinking or empathic design to identify a product or process user’s true needs. The goal is to transform the customer experience into providing a novel product or service to meet those needs. Of particular importance are any pain points in the customer experience. By first identifying those problem areas, managers can then think about ways to resolve them through the use of AI and real-time data. Many of these pain points might not have been practical or even possible to address in the past—the cost of a solution may have been prohibitive or the technical capabilities nonexistent. But today, given the advanced state of AI technologies, companies might now be able to resolve those very same pain points that had plagued them in the past.”

“The lesson here is that identifying opportunities for reimagination takes time—executives must capture the current business context, distill insights from various observations, and identify the potential value impact of the reimagined process.”

Of course, this is just the sort of work at which UX professionals excel. Plus, there are significant problems with users’ perceptions of AI that UX professionals can help solve:

“Ninety-two percent of automation technologists don’t fully trust robots. Part of the problem is human uncertainty around what the robot is thinking or planning to do next—that the machine is an inscrutable black box. These same technologists—76 percent—suggest that the top solution is to use some sort of visual output that provides analytics and a dashboard with other metrics. It’s a simple solution that can reduce opacity in the system—and keep humans firmly in the loop. Here, the role of the explainer is key. Even if the entire mind of an AI system can’t be known, some insights into its inner workings can be very beneficial. Explainers should understand both what’s useful for people to see in a visualization and what’s important for the system to share.”

UX professionals can provide user interfaces that visually communicate how AI systems make decisions and the many factors they take into account, thus creating transparency and engendering greater trust of AI systems in humans.

Conclusion

Human + Machine provides a clear, perceptive analysis of the current state of AI in a variety of business domains—and numerous, inspiring examples of businesses at the cutting edge of AI. Its authors, Daugherty and Wilson, make a compelling case for their vision of possible future applications of AI that would transform business, and they offer practical advice for organizations that are ready to embrace the future that only partnerships between humans and machines can deliver.

This book communicates an essential message to leaders in business, government, and education. If we get this transformation right, AI will enrich the lives of workers and deliver superior business results to the companies who fully leverage AI. If not—if business leaders cling to the obsolete model of driving ever greater efficiencies, cutting costs, demanding increasingly high levels of productivity from their employees, and emphasizing profits over all else—we’ll find ourselves in the midst of a socioeconomic crisis. This book has gotten an encouragingly positive response from the business community and technologists—check out the six pages of endorsements at the front of the book. We must create a future that ensures everyone benefits from the power of AI. This book shows us how to do it. 

References

Daugherty, Paul R., and H. James Wilson. Human + Machine: Reimagining Work in the Age of AI. Boston: Harvard Business Review Press, 2018.

Kucheriavy, Andrew. “Artificial Intelligence Will Take Your Job: What You Can Do Today to Protect It Tomorrow.” Forbes, February 26, 2018. Retrieved February 1, 2019.

Pew Research Center. “Artificial Intelligence and the Future of Humans,” PDF. Pew Research Center, December 10, 2018. Retrieved February 1, 2019.

Rosenblatt, Gideon. “Artificially Intelligent Design.” Vital Edge, August 20, 2018. Retrieved February 1, 2019.

Wikipedia. “Douglas Engelbart.” Wikipedia, undated. Retrieved February 1, 2019.