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Introduction

What we want is a machine that can learn from experience.

— Alan Turing, Lecture to the London Mathematical Society, 20 February 1947

The whizbang aspects of artificial intelligence get lots of press and screen time. Consider a few recent headlines:

 The U.S. Army is creating robots that can follow orders.

 DeepMind’s AI has now outcompeted nearly all human players at StarCraft II.

 A robotic hand taught itself to solve a Rubik’s Cube after creating its own training regime.

 A new AI fake text generator may be too dangerous to release, say creators.

 This AI bot writes such convincing ads that Chase just “hired” it to write marketing copy.

Remember back when the caption “Wi-Fi Ready” or “Bluetooth Ready” was stamped in a starburst graphic on the front of boxes for everything from televisions to refrigerators? AI has now reached that exalted status.

Of course, you have the smart speaker of your choice and maybe a smart thermostat. But wait, there’s more. You can get an AI-powered toothbrush that tattles to your smartphone about your brushing habits, via Bluetooth of course. An AI-enabled pill dispenser reminds you to take your medicine. And an AI-powered vacuum cleaner tidies up before the dinner guests arrive.

But AI is not just about gadgets and novelties. It also keeps the store from running out of water, batteries, and strawberry-flavored Pop-Tarts during hurricane season. It makes sure that a factory doesn’t exceed emissions standards. It figures out supply chain logistics, taking into account product quality, weather, tariffs, geo-political hotspots, compliance, and a host of other factors.

AI is also about increasing revenue and creating jobs. Yes, you read that last part right. Contrary to common warnings, AI could boost employment levels by 10 percent if the rest of the world invested in AI and human-machine collaboration at the same level as the top performing 20 percent.

About This Book

In this current AI renaissance, new advances appear on a near-daily basis, and that’s a good thing. But this book isn’t about the new, sexy, flashy, bleeding-edge, headline-grabbing utopian AI. It isn’t about the futuristic dystopian AI dreams portrayed in movies, books, and conspiracy theories either.

This book is about what AI can do for you, right now, in your business. It’s about well-established, tried-and-true technology and processes that are currently being used in businesses and organizations all over the world to help humans become more productive, more accurate, more efficient, and more understanding.

What you won’t find in this book:

 Deep dives into the mathematics and science underpinning AI

 Coding tutorials, examples of coding, or coding exercises

 Libraries and packages that you have to download and install

 Exercises to complete or problems to solve

What you will find in this book:

 A survey of the market drivers for AI and the enabling technology that makes it possible

 A very high-level, layperson’s overview of the algorithms and techniques that pragmatic AI uses

 A quick stroll down AI memory lane to see if you recognize early implementations you likely used

 Some tips on picking a solid use case for your first AI project for your business

 A survey of 21 vertical and horizontal markets to see how pragmatic AI can help you now

Strong, Weak, General, and Narrow

Often, people don’t differentiate between the AI that checks the grammar on your resume and the AI that becomes Skynet and ushers in the robot apocalypse. Just like coffee, ice cream, Pringles, and Pop-Tarts, AI comes in many flavors, but at a high level, it falls into two categories:

 Strong/general AI: Also known as artificial general intelligence (AGI), general AI is an intelligence that is indistinguishable from human intelligence. In other words, for now, AGI resides solely in the land of science fiction and speculation.

 Weak/narrow AI: In contrast to general AI, narrow AI lives firmly in the land of the now and the real. Each implementation has a very targeted (hence narrow) focus on accomplishing a specific, practical task. In fact, narrow AI is often called practical or pragmatic AI.

Pragmatic artificial intelligence is the subject of this book. You can apply AI to many problems, but in your business, the solutions all fall under three business goals.

Every enterprise AI project aims to reduce cost, increase revenue, or explore new business models.

As you read about the various vertical and horizontal markets and the related use cases, I might talk about workflow optimization or recommendation engines or predictive maintenance, but ultimately every use case falls under one of these three goals.

Foolish Assumptions

I am assuming that you, the reader, fall into one or more of the following categories:

 You have a college-level education, such as a bachelor’s degree, MBA, or professional certifications, or are pursuing a business degree.

 You read trade publications and books on business management.

 You possibly have leadership and/or IT skills, but not necessarily programming knowledge.

 You fall somewhere on the spectrum between:Business executive and decision-maker at a mid-sized to large organizationConsultant and strategic advisor, formal or informalAmbitious junior and up-and-coming employeeBusiness school or related student

Icons Used in This Book

As you read this book, you see icons in the margin that indicate material of interest. This selection briefly describes each icon in this book.

Everybody likes a tip, a little inside knowledge about a good thing. A life hack. A hint about how to save time or money. How to make things easier. This icon marks the spot where the goods are buried.

A few things are good to know, and remember, about how AI works. This icon reminds you to remember those things — and makes it easy to find them again if you forget to remember.

This icon is the reverse of a tip. It tells you how to avoid the bad thing. You see this? Don’t do that.

Once or twice for a second or so, the book gets down in the weeds, kicks over a rock to see what’s underneath. If you like that kind of thing, when you see this icon, keep on reading. If not, just skip it. You won’t miss anything you can’t live without.

Beyond the Book

To extend the experience beyond what’s in print here today, I’ve put together these additional resources:

 Cheat sheet: A quick reference to the major bullets and tables from the book. Feel free to print and post to your wall or simply glance at it when you need a reminder of some of the most fundamental concepts of Enterprise AI. You can find the cheat sheet by going to www.dummies.com and searching for Enterprise AI For Dummies Cheat Sheet.

 Updates: I've written this book to expose essential groundwork and use cases that will remain evergreen. That said, as this topic will likely only receive more prominence, not less, over the years to come, I also plan to publish updates, as applicable. They will be available on www.dummies.com as well by searching for Enterprise AI For Dummies. Additionally, input about this content is welcome directly through my site, www.zachonomics.com, where book-related talks and articles are also posted.

Where to Go from Here

There’s no harm in starting at Chapter 1 and reading right through, but unless you want to learn how AI can be used in a wide array of vertical markets and horizontal applications, you will likely want to dip into the areas of most interest to you and save the rest for another time.

Maybe you’ve noticed AI in the news, glanced at the headlines, skimmed a few articles, watched a video or two, but you’re still not completely certain that you know how it works and how you can use it. If so, before diving into the practical applications, start with Chapter 1, which provides some background about why companies are turning to AI to solve their problems and takes you on a tour of the four pillars on which modern AI is built.

You might have heard the term algorithm tossed about casually and wondered what one looks like. In that case, the last third of Chapter 1 is for you. It covers all the cool ideas, such as machine learning, deep learning, and text mining, to mention a few, not at a deep technical level, but at the level required to understand how you can use them to address your business challenges.

If you are looking for real-life examples of how AI has been used in the past and how it is being used now to solve business problems, read Chapter 2.

If you want to explore what it takes to get an AI project up and running in your business, check out Chapter 3.

If you’d like to take a deeper look at how you can use AI in your market, flip to Part 2. For each market, the chapters cover these areas:

 The challenges facing that market

 How AI can save costs, increase revenue, and support new business models

 A look under the hood to see the AI techniques that make it happen

 Specific use cases that allow you to leverage AI to grow your organization

Part 3 looks forward to future applications of AI, as well as sets out a framework of guardrails, so instead of approaching the topic like a panacea, you are equipped with a grounding that will set you and your organization up for a successful implementation.

Part 4 looks at ways AI will affect the coming decades and why AI is not the final answer for all your business issues.