Succeeding with AI

How to make AI work for your business

Veljko Krunic

• Publisher: Manning
• Year: 2020
• ISBN:
  9781638356318
• Topic: Business
• Subtopic: Project Management
• Index: Business

Summary Companies small and large are initiating AI projects, investing vast sums of money on software, developers, and data scientists. Too often, these AI projects focus on technology at the expense of actionable or tangible business results, resulting in scattershot results and wasted investment. Succeeding with AI sets out a blueprint for AI projects to ensure they are predictable, successful, and profitable. It's filled with practical techniques for running data science programs that ensure they're cost effective and focused on the right business goals.Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Succeeding with AI requires talent, tools, and money. So why do many well-funded, state-of-the-art projects fail to deliver meaningful business value? Because talent, tools, and money aren't enough: You also need to know how to ask the right questions. In this unique book, AI consultant Veljko Krunic reveals a tested process to start AI projects right, so you'll get the results you want. About the book Succeeding with AI sets out a framework for planning and running cost-effective, reliable AI projects that produce real business results. This practical guide reveals secrets forged during the author's experience with dozens of startups, established businesses, and Fortune 500 giants that will help you establish meaningful, achievable goals. In it you'll master a repeatable process to maximize the return on data-scientist hours and learn to implement effectiveness metrics for keeping projects on track and resistant to calcification. What's inside Where to invest for maximum payoff
How AI projects are different from other software projects
Catching early warnings in time to correct course
Exercises and examples based on real-world business dilemmasAbout the reader For project and business leadership, result-focused data scientists, and engineering teams. No AI knowledge required. About the author Veljko Krunic is a data science consultant, has a computer science PhD, and is a certified Six Sigma Master Black Belt.Table of Contents: 1. Introduction2. How to use AI in your business3. Choosing your first AI project4. Linking business and technology5. What is an ML pipeline, and how does it affect an AI project?6. Analyzing an ML pipeline7. Guiding an AI project to success8. AI trends that may affect you

Chapter 1. Introduction

This chapter covers

• The state of the AI project landscape today
• Distinguishing between critical and nice-to-have elements of a successful AI project
• Understanding business actions you can take based on AI project results
• A high-level overview of the process that a successful AI project should use

Today, the topic of AI comes up quite often, not only in the technical and business communities but also in the news intended for nontechnical audiences. Discussions of AI are even entering the domain of public policy. It’s likely that your own organization is considering the impact of AI and big data on its business, and that will lead to projects that use AI. I’ve written this book to help organizational leaders succeed with those AI projects.

As a consultant and trainer, I’ve been privileged to work with a large number of clients since topics like big data, AI, and data science have been taking off. Those clients have ranged from startups to Fortune 100 companies. Between projects, I’ve witnessed an emerging picture of the state of the industry. That picture includes many positive elements, with many millions of dollars made on successful projects. It also includes less talked-about projects. Those projects were managed in a way that doomed them from the start. But before they met their doom, those projects sent millions of dollars circling down the drain. The goal of this book is to help your project avoid becoming one of those doomed projects.

You might have heard of an AI platform called IBM Watson. The University of Texas MD Anderson Cancer Center partnered with IBM to create an advisory tool for oncologists. It was reported that Watson was canceled after $62 million was spent on it [5]! This example shows that even a high-profile project supported by a famous company with highly visible technology isn’t a guaranteed success.

In this book, you’ll see that the successful use of AI requires significant human involvement and insight. AI on its own isn’t a substitute for business knowledge, nor can it improve your business by solely looking at the data and making recommendations.

Warning

The fastest way to fail with AI is for the executive and business leaders to think, “AI can solve our problems; we don’t need to do anything except hire the right tech geeks and unleash them on our data,” or for the whole data science team to think, “Businesspeople take care of the business; we focus on technology.” Business and technology must work together for success.

Initially, this situation may seem disappointing, but on closer examination, it’s good news for a project leader. If AI could figure out your business, it would quickly put you out of a job. It can’t, so your job is safe for the foreseeable future. But to intelligently apply AI, you need special skills and knowledge to enable you to combine your business domain with it.

Warning

Technical knowledge regarding AI algorithms isn’t sufficient to get business results using AI.

This book teaches you what you need to know to run and get good results from an AI-based project. It’s assumed that you can run a general technical project.

The methods and techniques I teach are process neutral; you can use them in organizations of all sizes. To help you successfully run AI projects in such a wide range of organizations, this book focuses on the principles and skills that you must apply to your project, as opposed to providing rigid checklists and sequences of steps that you must perform in only one way. By learning these principles and skills, you can apply the techniques that are critical for the success of your project to any environment and process. But before we talk about how to get results with AI, let’s first review the skills you need to have to get the most out of this book.

1.1. Whom is this book for?

If you feel that insufficient information has been published on how to deliver business results with AI, this book is for you. You’ll find many books on the technical side of AI, data science, and big data, and some universities recently have started to add data science and AI programs. The result is that a lot of data scientists (and academics) know a lot about AI technology; however, they know far less about the business applications of AI.

I wrote this book for the business leader who is tasked with delivering results with AI and views technology as a vehicle to deliver those results. I also wrote it for the leadership team that’s working with and advising such a business leader. This section provides an overview of the skills these leaders need to follow the book.

To get the most value out of this book, you’ll need the following qualifications:

• You’ve been part of the leadership team of a successful software project.
• It doesn’t matter whether the project used Agile or some other software development methodology. It doesn’t matter whether the project used Java, Python, or some other programming language. What matters is that this isn’t your first software project and that you’re confident you can deliver a successful software project with the technologies you’ve used before.
• Whatever software development methodology you’re using (Agile or not), you must understand how your organization manages software development. This includes managing the requirements, deliverables, resources, and reporting mechanisms used to track progress in a timely fashion.
• You understand the basics of the business your organization is in, on a level commensurate with your position in the organization.
• This means that you understand your organization’s day-to-day business and what it involves, what business actions are possible for your organization, the main sources of income for your organization, and the basics of its budgeting process.
• If you’re a leader with profit and loss (P&L) responsibility, it’s also assumed that you understand how your business generates profit, as well as how to succeed in your business.
• You have experience with using business metrics to score the success of a business initiative.
• You know why metrics are important, how to measure the value of metrics, and how to recognize a metric that’s inappropriate for your business. Data science and AI are quantitative fields, and the data sizes used make it difficult to get an intuitive feel for how well a project is progressing based on a few examples.
• Although an engineering background or previous deep knowledge about AI isn’t required, an open mind and a willingness to facilitate conversations between people with technical and business backgrounds is.

With regards to the prior technical knowledge you’ll want to have, the due diligence you already did before you decided to join the AI project should suffice. You need to have a basic understanding of what terms such as AI and big data mean. You need to know that you’ll need data scientists on your AI team. And you need to know that before you can use a...