these AI systems have been developed in-house by skilled teams of programmers, working around the clock at great expense to employers. This reality is now changing. Companies like IBM, Google, and Microsoft are making AI capabilities available on a pay-as-you-go basis, dramatically lowering the barrier to entry. For example, each of these companies provide speech-to-text and text-to-speech services to easily build voice interfaces for pennies per use. This is opening the door for smaller companies with less disposable capital to introduce AI initiatives that will produce substantial results. With the aforementioned backdrop of consumers interacting with AI on a daily basis, these consumers are becoming increasingly more comfortable and receptive to their brands adopting and incorporating more AI technology. The combination of all of these components makes it a smart bet for any modern company to start down the road toward AI adoption.
But how do these companies get started? This question is one we have seen time and again working with clients in the AI space. The drive and enthusiasm are there, but what organizational thought leaders are missing is the “how to” and overall direction. In our day jobs working with IBM Watson Client Engagement Centers and clients around the world, we repeatedly saw this pattern play out. Clients were eager to incorporate AI systems into their business models. They understood many of the benefits. They just needed a way in. While attending tech conferences and meetups, we find similar stories as well. Though the technological barriers are lower, with vendors providing accessible AI technology in the cloud, the challenge of coming up with the overall plan was still preventing many businesses from adopting AI. Having a good roadmap is essential to feeling comfortable with starting the journey. It is for this very reason that we wrote this book. Our goal is to empower you with the knowledge to successfully adopt AI technology into your organization. And you've already taken the first step by opening this book.
In addition to helping you adopt and understand emerging AI technology, this book will give you the tools to use AI to make a measurable impact in your business. Perhaps you will find some new cost-saving opportunities to unlock. Maybe AI will allow your business to uniquely position itself to enter new markets and take on competitors. Although AI has become more widespread and mainstream in its use in recent years, we are still seeing a tremendous amount of room for disruption in every field. That's the great thing about AI—it can be applied in an interdisciplinary fashion to all domains, and the more it grows, the more its capabilities grow along with it. All that we ask of you, the reader, is to start with an open mind while we provide that missing roadmap to help you successfully navigate your way to driving value within your organization using AI.
Acknowledgments
This book would not have been possible without the help of the following:
All of our AI experts, who kindly contributed their knowledge to provide a snapshot of AI
Nick Zephyrin, for his amazing book edits, which have kept our message consistent
Wiley's production team, for helping us get this book out and in the hands of the world
Our families (especially our wives, Denise and Libby), for all of their support throughout our careers
All of our friends, especially Jen English, who read early drafts and provided feedback along the way
IBM and Comp Three, for providing ample opportunities for learning and education
CHAPTER 1 Introduction
The modern era has embedded code in everything we use. From your washing machine to your car, if it was made any time in the last decade, there is likely code inside it. In fact, the term “Internet of Things (IoT)” has emerged to define all Internet-connected devices that are not strictly computers. Although the code on these IoT devices is becoming smarter with every upgrade, the devices are not exactly learning autonomously. A programmer has to code every new feature or decision into a model. These programs do not learn from their mistakes. Advancement in AI will help solve this problem, and soon we will have devices that will learn from the input of their human creators, as well as from their own mistakes. Today we are surrounded by code, and in the near future, we will be surrounded by embedded artificially intelligent agents. This will be a massive opportunity for upgrades and will enable more convenience and efficiency.
Although companies may have implemented software projects on their own or with the help of outside vendors in the past, AI projects have their own set of quirks. If those quirks are not managed properly, they may cause a project to be a failure. A brilliant idea must be paired with brilliant execution in order to succeed. Following the path laid out in this book will put you on a trajectory toward managing AI projects more efficiently, as well as prepare you for the age of intelligent systems. Artificial intelligence is very likely to be the next frontier of technology, and in order for us to maximize this opportunity, the groundwork must be laid today.
Every organization is different, and it is important to remember not to try to apply techniques like a straitjacket. Doing so will suffocate your organization. This book is written with a mindset of best practices. Although best practices will work in most cases, it is important to remain attentive and flexible when considering your own organization's transformation. Therefore, you must use your best judgment with each recommendation we make. There is no one-size-fits-all solution, especially not in a field like AI that is constantly evolving.
Ahead of the recent boom in AI technologies, many organizations have already successfully implemented intelligent solutions. Most of these organizations followed an adoption roadmap similar to the one we will describe in this book. It is insightful for us to take a look at a few of these organizations, see what they implemented, and take stock of the benefits they are now realizing. As you read through these organizations' stories, keep in mind that we will be diving into aspects of each approach in more detail during the course of this book.
Case Study #1: FANUC Corporation
Science fiction has told of factories that run entirely by themselves, constantly monitoring and adjusting their input and output for maximum efficiency. Factories that can do just-in-time (JIT) ordering based on sales demand, sensors that predict maintenance requirements, the ability to minimize downtime and repair costs—these are no longer concepts of speculative fiction. With modern sensors and AI software, it has become possible to build these efficient, self-bolstering factories. Out-of-the-box IoT equipment can do better monitoring today than industrial sensors from 10 years ago. This leap in accuracy and connectivity has increased production threshold limits, enabling industrial automation on a scale never before imagined.
FANUC Corporation of Japan,1 a manufacturer of robots for factories, leads by example. Its own factories have robots building other robots with minimal human intervention. Human workers are able to focus on managerial tasks, whereas robots are built in the dark. This gives a whole new meaning to the industry saying “lights-out operations,” which originally meant servers, not robots with moving parts, running independently in a dark data center. FANUC Japan has invested in Preferred Networks Inc. to gather data from their own robots to make them more reliable and efficient than ever before. Picking parts from a bin with an assortment of different-sized parts mixed together has been a hard problem to solve with traditional coding. With AI, however, FANUC has managed to achieve a consistent 90 percent accuracy in part identification and selection, tested over some 5,000 attempts. The fact that minimal code has gone into allowing these robots to achieve their previously unobtainable objective is yet another testament to the robust capabilities of AI in the industrial setting. FANUC and Preferred Networks have leveraged the continuous stream of data available to them from automated plants, underlining
