Artificial Intelligence for BusinessA Roadmap for Getting Started with AI
<p><i>Artificial Intelligence for Business: A Roadmap for Getting Started with AI</i> will provide the reader with an easy to understand roadmap for how to take an organization through the adoption of AI technology. It will first help with the identification of which business problems and opportunities are right for AI and how to prioritize them to maximize the likelihood of success. Specific methodologies are introduced to help with finding critical training data within an organization and how to fill data gaps if they exist. With data in hand, a scoped prototype can be built to limit risk and provide tangible value to the organization as a whole to justify further investment. Finally, a production level AI system can be developed with best practices to ensure quality with not only the application code, but also the AI models. Finally, with this particular AI adoption journey at an end, the authors will show that there is additional value to be gained by iterating on this AI adoption lifecycle and improving other parts of the organization.
<p>Preface ix</p> <p>Acknowledgments xi</p> <p><b>Chapter 1 Introduction 1</b></p> <p>Case Study #1: FANUC Corporation 2</p> <p>Case Study #2: H&R Block 4</p> <p>Case Study #3: BlackRock, Inc. 5</p> <p>How to Get Started 6</p> <p>The Road Ahead 10</p> <p>Notes 11</p> <p><b>Chapter 2 Ideation 13</b></p> <p>An Artificial Intelligence Primer 13</p> <p>Becoming an Innovation-Focused Organization 23</p> <p>Idea Bank 25</p> <p>Business Process Mapping 27</p> <p>Flowcharts, SOPs, and You 28</p> <p>Information Flows 29</p> <p>Coming Up with Ideas 31</p> <p>Value Analysis 31</p> <p>Sorting and Filtering 34</p> <p>Ranking, Categorizing, and Classifying 35</p> <p>Reviewing the Idea Bank 37</p> <p>Brainstorming and Chance Encounters 38</p> <p>AI Limitations 41</p> <p>Pitfalls 44</p> <p>Action Checklist 45</p> <p>Notes 46</p> <p><b>Chapter 3 Defining the Project 47</b></p> <p>The <i>What</i>, <i>Why</i>, and <i>How </i>of a Project Plan 48</p> <p>The Components of a Project Plan 49</p> <p>Approaches to Break Down a Project 53</p> <p>Project Measurability 62</p> <p>Balanced Scorecard 63</p> <p>Building an AI Project Plan 64</p> <p>Pitfalls 66</p> <p>Action Checklist 69</p> <p><b>Chapter 4 Data Curation and Governance 71</b></p> <p>Data Collection 73</p> <p>Leveraging the Power of Existing Systems 81</p> <p>The Role of a Data Scientist 81</p> <p>Feedback Loops 82</p> <p>Making Data Accessible 84</p> <p>Data Governance 85</p> <p>Are You Data Ready? 89</p> <p>Pitfalls 90</p> <p>Action Checklist 94</p> <p>Notes 94</p> <p><b>Chapter 5 Prototyping 97</b></p> <p>Is There an Existing Solution? 97</p> <p>Employing vs. Contracting Talent 99</p> <p>Scrum Overview 101</p> <p>User Story Prioritization 103</p> <p>The Development Feedback Loop 105</p> <p>Designing the Prototype 106</p> <p>Technology Selection 107</p> <p>Cloud APIs and Microservices 110</p> <p>Internal APIs 112</p> <p>Pitfalls 112</p> <p>Action Checklist 114</p> <p>Notes 114</p> <p><b>Chapter 6 Production 117</b></p> <p>Reusing the Prototype vs. Starting from a Clean Slate 117</p> <p>Continuous Integration 119</p> <p>Automated Testing 124</p> <p>Ensuring a Robust AI System 128</p> <p>Human Intervention in AI Systems 129</p> <p>Ensure Prototype Technology Scales 131</p> <p>Cloud Deployment Paradigms 133</p> <p>Cloud API’s SLA 135</p> <p>Continuing the Feedback Loop 135</p> <p>Pitfalls 135</p> <p>Action Checklist 137</p> <p>Notes 137</p> <p><b>Chapter 7 Thriving with an AI Lifecycle 139</b></p> <p>Incorporate User Feedback 140</p> <p>AI Systems Learn 142</p> <p>New Technology 144</p> <p>Quantifying Model Performance 145</p> <p>Updating and Reviewing the Idea Bank 147</p> <p>Knowledge Base 148</p> <p>Building a Model Library 150</p> <p>Contributing to Open Source 155</p> <p>Data Improvements 157</p> <p>With Great Power Comes Responsibility 158</p> <p>Pitfalls 159</p> <p>Action Checklist 161</p> <p>Notes 161</p> <p><b>Chapter 8 Conclusion 163</b></p> <p>The Intelligent Business Model 164</p> <p>The Recap 164</p> <p>So What are You Waiting For? 168</p> <p><b>Appendix A AI Experts 169</b></p> <p>AI Experts 169</p> <p>Chris Ackerson 169</p> <p>Jeff Bradford 173</p> <p>Nathan S. Robinson 175</p> <p>Evelyn Duesterwald 177</p> <p>Jill Nephew 179</p> <p>Rahul Akolkar 183</p> <p>Steven Flores 187</p> <p><b>Appendix B Roadmap Action Checklists 191</b></p> <p>Step 1: Ideation 191</p> <p>Step 2: Defining the Project 191</p> <p>Step 3: Data Curation and Governance 192</p> <p>Step 4: Prototyping 192</p> <p>Step 5: Production 193</p> <p>Thriving with an AI Lifecycle 193</p> <p><b>Appendix C Pitfalls to Avoid 195</b></p> <p>Step 1: Ideation 195</p> <p>Step 2: Defining the Project 196</p> <p>Step 3: Data Curation and Governance 199</p> <p>Step 4: Prototyping 203</p> <p>Step 5: Production 204</p> <p>Thriving with an AI Lifecycle 206</p> <p>Index 209</p>
<p><b>JEFFREY L. COVEYDUC</b> is Vice President and Master Inventor at IBM. His diverse background consists of positions that encompass the creation of innovative, technologically advanced global AI solutions and client adoption. <p><b>JASON L. ANDERSON</b> is a Partner and CTO with the data consultancy, Comp Three, where he established a new AI line of business. He is also a former IBM Cognitive Architect and Master Inventor. He received both BS and MS degrees in Computer Science from California Polytechnic State University, SLO.
<p>We have reached a critical mass in the development of artificial intelligence. Thanks to products and services offered by the cloud, AI is now accessible even to smaller organizations or those with smaller budgets. And consumers are comfortable interacting with AI on a daily basis—think Apple's Siri, Netflix recommendations, and realtime GPS routing. With these two shifts, we see an elimination of the barriers to entry that once prevented many organizations from getting started with AI. Today, businesses know that AI is within their reach, and they know that their competitors, or disruptive startups, are working to leverage this new technology. AI is no longer an optional proposition. <p>We all need to think about implementing AI to stay competitive, but where do we start? Until now, there was no proven, step-by-step process to help businesses begin cutting costs and innovating using AI technology. In <i>Artificial Intelligence for Business</i>, Jeffrey L. Coveyduc and Jason L. Anderson provide just such a roadmap. This much-needed guide walks readers through the process of adopting AI technology, starting with identifying the opportunities most suited to AI solutions and leading all the way through deploying AI and iterating AI models for continuous improvement. <p>AI is inherently interdisciplinary, and, accordingly, this book takes an interdisciplinary approach. From a business perspective, leaders must understand that their most valuable resource is data. Locating (or, if necessary, creating), managing, and leveraging data resources is the name of the AI game. From a software development perspective, AI programming is very different from traditional application coding. If organizations and dev teams fail to understand the unique requirements of AI, their chances for success decrease. Readers will gain insight into each facet of AI and learn how to make them all work together for tangible value and innovation.
<p><b>A PROVEN PROCESS FOR TRANSFORMING YOUR ORGANIZATION WITH AI TECHNOLOGY</b> <p>The AI adoption journey is long, but the potential rewards are great. Many leaders have the drive and enthusiasm needed to get started with AI but no clear picture of how the process will unfold. <i>Artificial Intelligence for Business</i> minimizes the risk involved in making the transition to AI, both by providing concrete action steps and by identifying the most common pitfalls and how to avoid them. Such guidance could be the key to ensuring a profitable foray into the world of AI. Inside, you'll learn how to: <ul> <li>Identify opportunities to reduce costs and capture market share using AI</li> <li>Locate the data you need to train AI models, and manage data assets professionally</li> <li>Create a functional AI prototype to limit risk and demonstrate the AI value proposition</li> <li>Confidently deploy and iterate your AI solutions in production</li> <li>Establish AI maturity using model libraries to capture profits and improve over time</li> </ul> <p>This book is perfect for business leaders who want a high-level roadmap showing the way to proven success in the world of AI.
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