Writing this book was a collaborative process among four seasoned professionals who have a common belief that when paired with human intuition and knowledge, artificial intelligence (AI) can change the world. The most pragmatic and useful way to benefit from AI and machine learning is to implement these powerful technologies as an augmentation to human intelligence. This hybrid approach—a partnership between humans and machines—is what is called augmented intelligence.

All four authors have a deep appreciation of the nuances of how we can harness the power of AI as a tool for transforming business. The value of our collaboration is that each of us brought a different perspective to a common goal of providing guidance and direction. Judith Hurwitz served as the author team leader. Judith has been a trusted advisor to many companies in a broad range of emerging technologies, ranging from data and analytics to cloud computing and business process. She has served on a variety of boards of advisors. In addition, Judith has coauthored 10 books, hundreds of e-books and articles, and is a frequent speaker and guest lecturer. Henry Morris is a technology thought leader with a PhD in philosophy from the University of Pennsylvania. Henry founded the analytics practice at the global research firm International Data Corporation (IDC), where he coined the industry term “analytic applications.” Henry has decades of expertise in analytics applications, business process management software, and the complete data life cycle. Henry has consulted with a variety of organizations and businesses across the globe. Candy Sidner is a renowned AI research scientist with a PhD in artificial intelligence from MIT. She has written more than 100 research papers and is a fellow of the Association for the Advancement of Artificial Intelligence. Candy’s involvement in the AI community stretches from some of the earliest days of AI to today’s emerging research areas. Daniel Kirsch is an attorney, senior consultant, thought leader, and author. Dan has written dozens of e-books and white papers on topics focused on the cloud, data, security, and compliance. Dan’s research focuses on emerging technologies and their impact on businesses as well as the security, governance, and compliance implications of new technology.

The best part of writing this book was our weekly team meetings, where we discussed and debated the meaning of the movement to augmented intelligence and the need to create a hybrid approach that pairs humans with machines. Therefore, we bring a unique perspective to both the business and technical nuances required to use artificial intelligence on a collaborative platform that makes human responses to complex problems more achievable.

There is no doubt that artificial intelligence is powerful and provides huge benefits in being able to determine patterns that impact business outcomes. At the same time, the context and nuances of any field are vast. To make well-informed decisions, you must be able to assess data while utilizing human intuition and an overall understanding of the field in question. The human brain can make certain judgments and decisions incredibly quickly and accurately, but these choices are nearly impossible to codify into an artificial intelligence system. For example, you can’t rely on a programmatic bot to suggest the right mix of painkillers to a patient without having a full understanding of that patient’s physical and emotional state. You also need to understand the community the patient lives in and what support services are available. A person may appear on paper to be stable but might have some underlying issues in their home life that will impact treatment options. There is no machine learning model that can capture the full context of a person and their environment without human assistance.

As a team, we felt it was our obligation to put the extraordinary hype about artificial intelligence and machine learning in perspective. Venture capitalists have poured billions of dollars into companies that are promising to transform entire markets with intelligent systems that will understand everything and automate almost any task one can imagine. In addition, there is a wide variety of companies focused on AI, ranging from newly formed ventures to some of the most well-established public companies that are planning for their future. There are futurists who will tell you that artificial intelligence–based systems will be able to think at the same level as humans. We believe this claim is a misconception. There is no one technology that will have the power and intuition of human experts. The ability to translate complex data into applied knowledge may hold the key to solving some of the most complex problems we as humans face. It is our view that it is not enough to have a machine learning model and all of the bells and whistles of new algorithms and new approaches to AI. What really matters is the context in which data is used to make decisions and to recognize that data is dynamically changing in concert with changing business processes. These observations about the changing context, types, and use of data call for AI to be used in collaboration with business experts.

One of our goals for this book is to put artificial intelligence and machine learning in context so that professionals are armed with the tools they need to make faster and better decisions. There is so much excitement around the benefits and value of artificial intelligence that leads to misconceptions and poor practices. To be successful with machine learning requires a comprehensive understanding of data in context with the business problem you are addressing. It is mandatory to understand that machine learning models are dynamic and must evolve to reflect changes to data. At the same time, businesses have to understand the implications of relying on machine learning models to conduct business. Issues such as risks, ethics, and compliance are foundational issues that must be addressed or chaos and loss of reputation and business integrity can have dire consequences.

This book is aimed at three audiences: business leaders, technology managers, and AI experts who all need to have a common vocabulary and understand the importance of using AI and machine learning in a pragmatic way to solve real business problems. If you are a business leader, you need to understand the value of augmented intelligence in being able to differentiate your business from emerging competitors. If you are a technologist, you need to understand the power of AI and machine learning in context with the problems and needs of business professionals in your organization.

Within this book we provide business leaders with an understanding of the type of teams they need to create and the type of leadership management needs to provide. Technologists must have a detailed understanding of how to work with business leaders to create a platform for growth and differentiation. AI experts often focus on the technical nuances of their field without taking into account the requirement for designing new business processes and creating a platform that embraces changing data sources. If you are an expert in AI and machine learning, you need to understand the importance of the explainability of models in business environments. Although you may use sophisticated machine learning algorithms, you may not be able to have a clear understanding of how and why the system takes specific actions.

We divided this book into 10 chapters that focus on both the technical and business aspects of augmented intelligence.

In this chapter, we define augmented intelligence as a way to use machine learning models and AI tools to perform decision-making tasks. Unlike traditional bots or automated processes, augmented intelligence is focused on humans collaborating with machines to discover results that are instrumental in aiding experts in making better informed decisions.

In this chapter we explain the innovations in infrastructure, including cloud computing and advances in machine learning models. This chapter explains the role of cloud computing in enabling businesses to have the capacity and performance to support complex data management. The chapter puts machine learning techniques into perspective as they relate to providing the foundation for the management of data that is accurate.

In this chapter, we explore the need for a consistent and predictable way to manage data sources, from data acquisition to data preparation, and the ability to build dynamic data models. It is important not to think of preparing and managing data as a one-step process. Rather, data has to be thought of as a continuum, since data sources are updated and changing constantly.

In this chapter, we explain what is required to build models intended for augmenting intelligence to support experts. It is clear that you have to select the most appropriate algorithms and then apply the necessary amount and type of data against the model. Testing and retesting as you add more data sources is a requirement for success.

This chapter focuses on the great potential to apply augmented intelligence to the new generation of business processes. Humans and machines working in collaboration can have a powerful impact on the effectiveness of how organizations operate and manage customer relationships. Augmented intelligence overcomes the limitations of isolating human understanding from the massive amounts of both structured and unstructured data available to analyze complexity in record time. So, how does augmented intelligence change the way tasks are executed and the way that work is getting done?

This chapter explains the potential business and technical risks when organizations take advantage of the power of AI and machine learning. The greatest risk for organizations leveraging machine learning models is to ensure that the correct data sources and the right amount of data is being used to solve a problem. If data scientists work in isolation from subject matter experts, the models may not reflect the real world. There is a huge risk that data will be misunderstood, leading to poor results.

This chapter focuses on the need to provide oversight of your augmented intelligence projects. Business teams are eager to leverage data to support revenue growth; they are often unaware of the potential legal, regulatory, and ethical implications of their project plan. At the same time, data scientists are often focused on gathering data, building models, and improving model accuracy. Issues such as governance and ethics often take a back seat. However, businesses have an obligation to follow prescribed governance rules. Likewise, organizations must make sure that they are handling data in a way that protects individuals’ personal data.

In this chapter, we focus on the need to build a business case for organizations to implement augmented intelligence. The value of this approach requires that technology leaders provide the business with an understanding of the value of both AI and machine learning techniques that can help subject matter experts make better decisions and outpace the competition.

In this chapter, we explore the requirement to plan for implementing augmented intelligence. How you approach creating a strategy for augmented intelligence is complex. Therefore, business teams must start with a well-defined plan based on their business goals. To be successful, you need to have a project that is big enough that it can demonstrate to senior management that it can solve a real business need. At the same time, your project can’t be so expansive that it will take too long and cost too much money to achieve a meaningful result.

This chapter focuses on what we can expect in the future from augmented intelligence. We explore the ways that artificial intelligence and machine learning models will change the way we work. The chapter looks into the future of the value in codifying knowledge so that experts can do a better job by leveraging and codifying the massive amounts of information inside business data sources.