Augmented intelligence is a powerful concept and a foundational technology that has the potential to transform data into a technique to predict customer behavior, provide a deep understanding of information, and plan for the future. Much of the excitement around artificial intelligence (AI) has been focused on using machine learning (ML) models to automate business processes. However, organizations that understand the power of creating collaboration between humans and machines can outpace the competition. In this chapter, we explore how businesses can capitalize on the power of augmented intelligence to gain an advantage, both in the short term and into the future as markets and competition change.

Organizations have never lacked data. In fact, most businesses have more data in various forms than they can manage and understand. The challenge has always been the complexity of extracting meaningful relationships or patterns about overall market dynamics and understanding how this relates to changing market conditions. How do you capture meaningful insights that aren’t obvious? The problem goes beyond the ability to process data quickly—it is a problem of how to understand context and relationships that are meaningful, not simply anomalies.

The risks of inaction have never been higher. Emerging companies with little revenue are disrupting entire industries and markets overnight and causing established companies to scramble to create new strategies on the fly. Retailers have found that e-commerce has upended their business model, and to stay relevant they must engage with customers in new ways. Cab companies and vehicle manufacturers are being threatened by new ride sharing models. Manufacturing companies have found that innovative new automated processes and new supply chains have caused them to rethink their cost structures overnight.

It is not surprising that businesses must be able to understand hidden warnings about changes in market dynamics. The problem of being caught ill prepared for market transitions is not a new problem—it has plagued businesses for decades. Can artificial intelligence and machine learning models become tools to help businesses maintain their position in competitive markets? It is not simple to determine how organizations can gain the right level of insights to make a difference between success and failure. Simply automating processes and using weak augmentation is not the solution. To be successful, business leaders have to understand the data that defines their business—both structured and unstructured data—and then transform their business processes. To put this data to work requires that it is available in the right form to provide subject matter experts and leaders with the tools to accelerate change.

How does a business use the power of augmented intelligence to prepare for change? Data is only valuable in context with the business issue being addressed. It is not enough to have data about who your customers are, what they buy, and the problems they are having. This type of data will only let you understand what has happened in the past; it will not prepare you for the future. To be successful organizations need to take a holistic approach in order to gain insights from their data. What are the meaningful relationships or patterns from the data about your customers, partners, suppliers, and employees? What information can you find that indicates changes in customer buying patterns? Is there data about the availability of raw materials and how those prices are changing? Do you understand how customer preferences are different today from what they were five years ago or even two months ago? Are there changes in regulations that will impact your ability to satisfy customers? For example, if you are a financial services company, are you striking the right balance between giving customers easy access to their account information while also securing their data? Are governmental bodies establishing new regulations that will impact your business across the globe? There are countless examples of how businesses are being fined by governments for a lack of adherence to security and privacy requirements.

Do you truly understand how customer expectations are changing? A few years ago, it was common for businesses to charge customers for shipping. Now, customers are increasingly expecting that shipping will be included in the price of the product. Ironically, prices might be higher when shipping costs are built into the price but that may not deter customers from purchasing. It is critical that management understands the nuances of customer behavior and how it is changing as new business models emerge.

Every era sees the advent of new technologies that disrupt the way we live, buy, and manage our lives. The steam engine transformed commerce in 1698; the telegraph invented in 1837 changed communications forever. Alexander Graham Bell’s first US patent for the telephone in 1876 is the most transformative technology because it changed the pace of business as never experienced before. The invention of the automobile changed how individuals conducted their daily lives and how businesses transformed commerce. And, of course, the commercialization of the Internet in the early 1990s led to dramatic business changes. The Internet and then the advent of cloud computing and innovations in distributed computing has made the world of AI and machine learning commercially viable, as we discussed in Chapter 2. The bottom line is that what seems to be focused on a single purpose initially will often lead to dramatic changes in the way businesses must operate.

How can an organization take advantage of disruption to create new business models? First, it is important to recognize that dominating a market is no guarantee that this supremacy is sustainable. Today, it is possible for an emerging company to gain access to massive amounts of data that can be analyzed and understood in a way that can upend a market. One thing differentiates companies that can disrupt a market from those that succumb to the competition— data and how that data is analyzed. In fact, if you examine the companies that are upending markets and gaining credibility, they are businesses that have data at the core of their strategy.

However, it isn’t enough to simply capture a lot of data. Rather, it is the ability to breakdown silos across data sources and integrate that data in significant ways. To be successful, these companies are extracting the patterns or relationships from the data in order to provide unique services. If you are able to understand what customers will need to buy based on their data, you will be in a better position to understand the nuances of their current and future requirements. Too often an online retailer will send an offer to a customer who has just purchased a product an opportunity to purchase the same product again. It would be more beneficial if the retailer were able to anticipate the next product that the customer might need based on that purchase. For example, customers who purchased a set of power tools may also need to purchase rechargeable batteries, a storage case, or other complementary tools and accessories. Making an attractive offer at the right time may increase the possibility of customers increasing their spending. Getting to the point of making the right offers to prospects at the right time requires being able to understand the relationships between products, the dynamics of the market, and customer buying patterns. Naturally, these patterns are not static. A product that is popular with customers one month may fade a few months later. The business that figures out innovative ways to put data to use in predicting customer trends and requirements will have a good chance to beat the competition.

Getting to the point where you can use data to anticipate the future and understand with certainty what customers need is complicated. To gain deep understanding requires that organizations are able to observe and capture data from external sources, ranging from social media data, demographic data, data about market trends, competitive data, research reports on the latest trends in a market, etc. The business needs to also leverage internal data—both structured data about customer trends and unstructured data about what customers are saying in their communications with the company. The ability to monetize data comes from the ability to discover what customers consider important and will be willing to pay for—even before the customer knows what they want.

One of the benefits of augmented intelligence is that it provides a machine learning approach that focuses on understanding the context of all types of data that are part of the core knowledge of the business. With augmented intelligence, it is possible to discover insights and patterns that would be difficult to understand without the support of advanced analytics. There will be situations, as we discussed in Chapter 1, in which your data is straightforward, and it is possible to easily automate in a consistent and predictable process. However, when you are dealing with complex and ever-changing business knowledge, it is likely that you will be using a corpus of data that can work in collaboration with subject matter experts. The value of this type of augmentation is critical to being able to disrupt a market. You cannot assume that an AI system will be able to easily discover the answers and solutions to complex problems. If, on the other hand, you can turn your massive amount of both structured and unstructured data into a well-engineered data platform, you can be prepared to plan for the future. This new augmented intelligence system will not necessarily provide immediate answers to complex problems. However, it will provide access and insights into patterns that experts can combine with their own understanding of their industry. The alternative approach requires that an expert manually discover patterns in complex data. The most experience experts will often know where to look and find the best sources of answers. However, often these experts are expensive to hire and in short supply.

The deeper meaning of information often remains hidden. Even if your industry experts have sufficient time, it is typical to miss key patterns and nuances hidden in data. In some cases, an expert may rely on techniques and knowledge related to how they have always thought about solving a problem. In competitive markets, you often need augmentation to be able to see the solution to a problem in a totally different light.

It isn’t enough to simply hope that the data scientists in your organization will have the knowledge and ability to make the most of your complex data. It is tempting to assume that these experts will take the right path in isolation from the business. When data scientists work in isolation from the business, they often make assumptions about what models should look like. In addition, they may not understand the nuances of the data they need to train to solve complex business problems. One of the key issues for business organizations is that management often does not understand the role of data scientists and therefore assumes that they understand the business. It is a difficult situation, since the cost of hiring a data scientist is very high. It is no wonder that upper management assumes that if you pay such a high salary, you are getting a professional who understands the business. This is often not the case.

The solution to taking advantage of the skills and knowledge of data scientists is to create a hybrid team. This team consists of a combination of different individuals that understand technology and the business. Who should be part of the team? The following provides you with some guidelines when constituting your team:

• Business Analysts. Analysts who understand the business are critical to the success of your team. These team members may be part of a business unit or provide guidance and understanding at the overall corporate level.

• Business Strategists. Strategists are in the best position to provide an understanding of where the company is headed. What are the new directions the company is headed in? What are the roadblocks in terms of the analysis that is needed to create a successful strategy? You want to make sure that the strategists are at a high enough level that they have a clear understanding of where the company is headed.

• Data Analysts. It is critical to have team members who understand the nature and characteristics of the business data across business units. What is the source of the data that the company relies on today? What data isn’t available that would help in planning for the future of business processes?

• Security and Governance Experts. Team members who understand how to secure the data are critical to the success of your data initiatives. As you strategically move to augmented intelligence, you will have added responsibility to ensure that you are protecting sensitive data against inappropriate exposure.

Creating this team is instrumental in your success with augmented intelligence. However, simply bringing a team together is not enough. To be successful will require leadership. It is critical that the team understands their mission and goal and that there is upper management direction. Therefore, you need to select a team leader who can work with all of the constituents that are part of the process so that you have a successful outcome. Your outcome will be a data management environment that provides a way for experts to be able to leverage knowledge in a way that supports business goals.

All businesses deploy techniques to manage their data and use analysis of that data to make informed business decisions. However, organizations operate at different levels of sophistication and experience. Many organizations rely on traditional business intelligence reporting tools that provide insights into performance of various business units in terms of sales. Other businesses are moving to predictive analytics and are beginning to leverage machine learning models. As organizations gain more expertise, they are able to gain more insights into their businesses. Organizations that reach a higher level of maturity are able to take advantage of the knowledge and changing business processes to transform their organizations in light of new opportunities and challenges. A business does not get to the highest level of maturity overnight. Gaining this level of maturity requires building teams as well as gaining an understanding of the data you have and the data that you need.

There is a strong demand to create industry-specific augmented intelligence applications. The requirement for industry-focused applications stems from the fact that each industry has their own governance requirements, business challenges, and specific nuances. All of these solutions, whether we are looking at banking, transportation, or commerce, have common characteristics. The commonalities include:

• Large amounts of data in many different forms

• Industry-specific data (typically unstructured) that is constantly expanding

• The need to correlate a variety of data sources to determine context, patterns, and anomalies

• A requirement to find a way to match the data with deep expertise

• The need to analyze large amounts of data to support decision making, such as next best action

• The ability to have the systems learn and change as business conditions change

Augmented intelligence is changing the way people interact with computing systems, to help them find new ways of exploring and answering questions about their business. These systems will learn and interact to provide expert assistance to scientists, engineers, lawyers, and other professionals in a fraction of the time it now takes.

What makes augmented intelligence different is that these systems are built to change. The system continues to change based on the ingestion of more data and the ability to identify patterns and linkages between elements. The models are continually adjusting, rather than relying on previous data. Therefore, companies can look for associations and links between data elements that they might not even have known existed beforehand.

The results of creating these types of solutions can be profound. They enable a new level of engagement in which the business leader can have an intuitive interface between the system and the huge volume of data managed in the corpus. Even more important is that these systems are not static. As new data is added, the system learns and determines new ways of understanding situations. For example, new associations may suddenly appear that were not visible or present in the past. Perhaps there is an association between someone who buys books and takes a certain type of vacation. Perhaps there is a relationship between two drugs that can cause a never-before-seen interaction. There may be a new method of treating a serious condition based on a series of new research findings that were published only in the past month in an obscure journal.

The underlying value of augmented intelligence is that it has the potential to change the way individuals in organizations think about information. How do we ask systems about what the data we are seeing means? How can we interact with a system to provide insight when we don’t know what direction to take or what question to ask?

It is becoming clear that we have only scratched the surface of the power of information managed in new ways to discover new ways to act and transform organizations.

When industries are in transition with new competitive threats, it is impossible to simply build an application. Traditional applications are intended to automate processes and manage data. When a business is trying to transform a traditional industry such as travel or customer care, innovators need sophisticated technologies that allow leaders to discover new techniques and new knowledge. A travel company that can discover what customers want will have a differentiation. What if a travel company can know what the customer will buy even when the customer has no idea? What if a customer service representative can anticipate that the customer’s problem is related to a partner’s product within minutes rather than hours?

The new generation of solutions will look beyond codified practices and find the answers that are not obvious. Disrupters in every industry throughout the centuries have done precisely this: They have taken traditional approaches to solving problems and turned them upside down.

Augmented intelligence is emerging as a technical and cultural approach to analytics that has the potential to change the way humans interact with machines. Using machine learning and AI combined with the decision-making capabilities of humans is proving to be a transformational approach. It is important to remember that you cannot begin your journey by assuming that you will quickly transform every process within your company. In the next chapter, we discuss ways that you can approach getting started with augmented intelligence. The bottom line is that you have to focus on discrete business challenges and getting early wins in order to gain momentum. As you progress through the data-maturity stages, the value you are able to extract from your data will increase.