Summary

Many AI projects are in progress today, and many of them will fail. This book helps you avoid starting an AI project that's doomed to failure and shows you how to lead the right AI project toward the business results.

Conventional wisdom tells us that the determinant of success or failure of an AI project is the project team's in-depth knowledge of AI technology. It is not. Overfocus on technology, paired with a vague understanding of the actions leaders must take for their AI projects to succeed, causes unrealistic expectations and poor business results. Believing that success with AI is determined solely by technical prowess confounds an enabler with a capability. Although you do need to have technical skills on your team for your AI project to succeed technically, to implement AI in your business, you also need to link technology with business goals. Only humans can do that, and this book shows you how.

Succeeding with AI helps you make your AI projects predictable, successful, and profitable. It's filled with practical techniques for running AI projects that ensure they're cost-effective and focused on the right business goals.

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

You will learn:

• How do you recognize an AI project that will fail business-wise, even if it is technically successful?
• How do you make money for your organization by using AI?
• How do you find actionable uses of AI in your organization?
• How do you manage your AI project to get optimal business results?   
• How do you measure the success of your AI efforts in business terms?
• How do you quickly determine if your technical approach is capable of delivering the intended business results?
• Which part of your current technical solution you should improve to get the best business results?

About the reader

For project and business leadership, result-focused data scientists, and engineering teams. No AI knowledge required.

Table of Contents

1 ■ Introduction

2 ■ How to use AI in your business

3 ■ Choosing your first AI project

4 ■ Linking business and technology

5 ■ What is an ML pipeline, and how does it affect an AI project?

6 ■ Analyzing an ML pipeline

7 ■ Guiding an AI project to success

8 ■ AI trends that may affect you

About the Author

Veljko Krunic is an independent consultant and trainerspecializing in data science, big data, and helping his clients getactionable business results from AI.

He holds a PhD in computerscience from the University of Colorado at Boulder and an additional MSin engineering management from the same institution. His MS degree inengineering management focused on applied statistics, strategicplanning, and the use of advanced statistical methods to improveorganizational efficiency. He is also a Six Sigma Master Black Belt.

Veljko has consulted with or taught courses for five of the Fortune 10companies (as listed in Sept 2019), many of the Fortune 500 companies,and a number of smaller companies, in the areas of enterprise computing, data science, AI, and big data. Before consulting independently, heworked in the PSO organizations of Hortonworks, the SpringSourcedivision of VMware, and the JBoss division of Red Hat. In thosepositions, he was the main technical consultant on highly visibleprojects for the top clients of those PSO organizations.