AI for People and Business

Chapter 1. Success with AI

This book is for you if you are an executive or manager interested in engaging AI within your organization. This book is for you if you want to understand exactly what AI is, why AI is able to provide value for your business and the people who interact with it, how to identify AI opportunities, and how to develop and execute a successful AI vision and strategy.

Reading this book should help dissolve the often nebulous and mysterious perception of AI and give you the right assessment tools, processes, and guidance so that you and your business can gain the requisite, level-appropriate understanding and begin using AI today. This book will also benefit data and analytics practitioners (e.g., data scientists) and anyone else who is interested in learning more about AI from a strategic, business-level perspective.

This book, and the AIPB Framework that it introduces, will hopefully help answer your AI questions and guide your journey to success with AI.

Racing to Business Success

As I mentioned in the preface, the ultimate goal in American professional motorsports is to win the Indianapolis 500. And in this event, where anything can happen, the timely advanced analysis of data—including historical events, sensor data, telemetry, computer simulation, driver feedback, and more—makes all the difference. Since shifting to tech from working as an IndyCar engineer and race strategist for various teams, I’ve discovered that the same goes for business. In the age of big data and advanced analytics, developing and executing a vision and strategy to turn your company’s data into top results might be the only way to win.

Making decisions and taking action based solely on historical precedent, simple analytics, and gut feel no longer gets the job done—nor does pursuing near-sighted goals or commoditized technologies. And yet, too many businesses remain mired in the status quo. More and more, it’s those that effectively use analytics who succeed; that is, those that extract information such as patterns, trends, and insights from data in order to make decisions, take actions, and produce outcomes. This includes both traditional analytics and advanced analytics, which are complementary.

I use the umbrella term advanced analytics in a way similar to a definition given by Gartner: “Advanced Analytics is the autonomous or semiautonomous examination of data or content using sophisticated techniques and tools, typically beyond those of traditional business intelligence (BI), to discover deeper insights, make predictions, or generate recommendations.” Advanced analytics techniques include those associated with AI, machine learning, and others covered in this book.

Data is a core advantage if, and only if, you know how to use it. All companies should begin to think of themselves as data and analytics companies, regardless of what their core offerings are. As long as data is involved, this is a critical step in getting ahead of the competition while also gaining an increased ability to create huge benefits for both people and business.

Many companies increasingly know this and want to undergo a data and analytics transformation, but struggle to identify real-world AI opportunities, use cases, and applications, as well as create a vision and strategy around them.

Turning an AI idea into actual benefits that are realized by people and businesses is difficult and requires the right goals, leadership, expertise, and approach. It also requires buy-in and alignment at the C-level. All of this is what I call applied AI transformation; it is what this book, and particularly the framework that it presents, is all about. Note that I call it applied AI transformation, and not digital transformation. I think this distinction is critical and I’ll briefly explain why.

Terms like innovation, transformation, and disruption are thrown around all the time, and usually in a broad context. Similarly, the phrase digital transformation is equally as broad and therefore its meaning isn’t necessarily clear. Don’t get me wrong—there is value to the phrase and its intended meaning, and there are many companies that absolutely need to undergo a digital transformation, and the sooner the better. But simply saying you need to undergo a digital transformation may generate more questions than answers. Some of these questions include: what does digital transformation mean exactly? What specific technologies or technology systems (e.g., AI, blockchain, Internet of Things[IoT]) should we be using and which should we choose first? How do we prioritize between different digital goals and initiatives? How will digital transformation meet our goals and by how much? How much will it cost and what’s the potential ROI? When will we realize that ROI?

All three words in the phrase “applied AI transformation” have a specific and intended meaning. Due to the relative infancy of AI and its limited use (so far) in real-world applications, AI is widely viewed as being largely theoretical. The term applied is intended to distinguish between theoretical AI and AI that is applied to real-world use cases, something for which we’re now seeing a significant and diverse proliferation. The term transformation is as expected, and in the case of AI, means harnessing AI to generate certain benefits or outcomes not attainable through other methods, or in other cases, to produce high-impact outcomes much more efficiently (time and cost) and with greater value. In this context, applied AI transformation leaves no room for ambiguity—it means applying existing and emerging AI techniques to build real-world solutions that can transform businesses and people’s lives. Whether pursuing a digital transformation or applied AI transformation, both require a vision and strategy. AIPB helps guide this in the case of an applied AI transformation.

Why Do AI Initiatives Fail?

There are many reasons why AI initiatives might fail. One reason is that AI is still generally not well understood. Few executives and managers truly understand what AI really is, the current state of AI and its capabilities, the value it represents, what’s required for AI success, the difference between AI hype and reality, the differences and unique benefits of AI as compared to alternate forms of analytics, the differences between AI and machine learning, and much more. AI can have tremendous benefits for companies, customers, users, and/or employees, but it’s not always obvious how, nor is it obvious what data, techniques, time, cost, and trade-offs are required. It’s also not always obvious how to measure the success of AI solutions after you build them.

Companies also might not have the “right” data and advanced analytics leadership, organizational structure, or talent in place. AI is an extremely technical subject area and requires translators between management and advanced analytics experts, a responsibility usually held in the context of software by business analysts and product managers. Like their executive counterparts, very few of these folks understand AI either, thus spawning new data-centric versions of these roles (e.g., data product manager), although that’s relatively new and talent is scarce. Also, due to the relative infancy of data organizations within companies, real-world data organization structures (e.g., leadership, reporting, functional alignment) are all over the place. Most important, these data organization structures might not be optimized for cultivating internal adoption, alignment, and understanding around AI initiatives, nor for successful delivery of AI initiatives in general (e.g., roles, responsibilities, resources).

When considering investments in technology, executives are rightfully concerned with understanding final outcomes, costs, time to value, ROI, risk mitigation and management (e.g., bias, lack of inclusion, lack of consumer trust, data privacy and security), and whether to build or buy. Unlike traditional technology investments associated with undergoing a digital transformation—for example, building a mobile app or data warehouse—AI is better characterized as scientific innovation, a concept that implies an inherent amount of uncertainty in a way similar to that associated with R&D.

AI is a field based in statistics and probability and is rapidly advancing in both state-of-the-art and potential applications. It might be impossible to avoid some amount of appreciable uncertainty with AI. Not understanding this or incorrectly setting expectations is another potential cause of failure. So is not pursuing AI in an Agile and Lean way and appropriately respecting the exploratory and experimental nature of AI. Appropriate assessments should be performed as part of a broader approach tailored specifically to the unique characteristics and potential challenges of AI. The AIPB Framework is intended to help companies address and avoid potential points of failure and maximize their chance of success with AI.

Lastly, building successful AI solutions that benefit both people in addition to business requires a basic understanding of what people need and want, and also what the ingredients are for making great products and user experiences given that many of these ingredients will apply to making great AI solutions, as well. Fundamentally, people use products and services that are useful, better than the alternatives, are enjoyable and delightful, and that result in a good experience. AI solutions that are able to deliver on all of these will succeed, whereas those that miss on just one ingredient can fail.

Why Do AI Initiatives Succeed?

AI initiatives (and undergoing an applied AI transformation) succeed when decision makers like you try to better understand AI, including its benefits, opportunities, potential applications, and challenges. AI initiatives also succeed when the why behind them is clearly and concretely established, is aligned to goals for both people and business, and is used as the North Star that guides everything else.

Further, AI initiatives succeed when the appropriate data and analytics organization is prioritized and built (some recommendations for which we cover in this book). This includes leadership, organizational structure, and talent that fills strategically appropriate analytics roles and responsibilities. This type of organization is able to do the following:

• Identify and prioritize AI opportunities.

• Help prioritize company-wide investment in AI.

• Cultivate AI adoption and alignment.

• Properly set expectations around AI initiatives.

• Generate a shared vision and strategy around AI.

• Help break down silos.

• Democratize data and analytics.

• Help continually advance the organization’s data and analytics competency.

• Foster a cultural transition from a gut-driven, historical precedent–based, simple analytics–based organization to a data-driven and/or data-informed organization.

• Build, deliver, and optimize successful AI solutions.

Additionally, successful data and analytics organizations are able to properly assess their AI readiness and maturity level and identify gaps and develop a prioritized strategy for filling in those gaps. They are also able to analyze specific key considerations and any associated trade-offs on an initiative-by-initiative basis, similarly identify gaps and prioritize filling them, and also make the right decisions as needed throughout the initiative’s life cycle.

Data and analytics organization members must be able to work cross-functionally and collaboratively with experts from all functional areas of an organization in strategic ways, and as needed. AIPB uniquely defines a high-level set of cross-functional experts who must work together during certain phases of AI initiatives to ensure successful outcomes.

Creating a real-world deliverable that delivers on its intended benefits requires an effective sequence of iterative phases, which the AIPB Framework uniquely defines in the context of AI. Each of these phases has a related output defined by AIPB as well, all of which are key ingredients of successful AI solutions. Understanding concepts that we discuss, such as scientific innovation, particularly in the context of AI, contributes to success, as well.

Harnessing the Power of AI for the Win

To help answer the questions and accomplish the goals discussed so far, this book presents the AIPB Framework that I have created based on my nearly 20 years of innovation experience and expertise. It is a formalization of the real-world strategies, approaches, and techniques that I’ve used successfully throughout my professional career, with companies spanning many industries and ranging from IndyCar racing teams, to early-stage startups, to large corporate enterprises. It also represents a unification of my expertise, knowledge gained from experience, and what I’ve found works best in the areas of business, analytics and product management and pursuing innovation in general.

I call it AI for People and Business (AIPB), because it is specifically focused on creating successful AI solutions for better human experiences and business success. AIPB will help executives and managers due to its unique and purpose-built North Star, benefits, structure, and approach. It is an end-to-end framework to guide pursuing AI initiatives, including everything from performing appropriate assessments, to developing an AI vision and strategy, through to building, delivering, and optimizing production AI solutions.

The intention of this book isn’t to say that AIPB is the definitive framework that should replace everything else. In fact, as we will soon discuss, AIPB is high level and modular. This means that for your initiative or project, your team should use whatever subframeworks that it thinks work best (or those that I recommend, if preferred).

In explaining the framework that I’ve developed, my intention is to help guide your thinking at a high level so as to help eliminate some of the confusion that comes with trying to innovate with AI. Whether or not this particular framework is implemented, I think this discussion of AIPB and other topics covered in this book will provide a conceptual way of thinking about successfully using AI in an organization.

We cover the comprehensive, end-to-end AIPB Framework in detail in the next couple of chapters. The remainder of the book will cover almost everything that any executive or manager should understand about AI at the appropriate level, with a primary focus on developing an AI vision and strategy. In my experience, developing an AI vision and strategy is what the target audience of this book tends to struggle with most.

This focus should help decision makers better understand AI and more confidently make decisions and investments around AI initiatives. If, by simply understanding the concepts presented by AIPB and the contents of this book, executives and managers are able to progress further ahead with advanced analytics than where they are today, that’s a win.

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