{"product_id":"artificial-intelligence-for-business-isbn-9781119651734","title":"Artificial Intelligence for Business","description":"\u003cp\u003e\u003ci\u003eArtificial Intelligence for Business: A Roadmap for Getting Started with AI\u003c\/i\u003e 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. \u003c\/p\u003e\u003cp\u003ePreface ix\u003c\/p\u003e \u003cp\u003eAcknowledgments xi\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 Introduction 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCase Study #1: FANUC Corporation 2\u003c\/p\u003e \u003cp\u003eCase Study #2: H\u0026amp;R Block 4\u003c\/p\u003e \u003cp\u003eCase Study #3: BlackRock, Inc. 5\u003c\/p\u003e \u003cp\u003eHow to Get Started 6\u003c\/p\u003e \u003cp\u003eThe Road Ahead 10\u003c\/p\u003e \u003cp\u003eNotes 11\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 Ideation 13\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAn Artificial Intelligence Primer 13\u003c\/p\u003e \u003cp\u003eBecoming an Innovation-Focused Organization 23\u003c\/p\u003e \u003cp\u003eIdea Bank 25\u003c\/p\u003e \u003cp\u003eBusiness Process Mapping 27\u003c\/p\u003e \u003cp\u003eFlowcharts, SOPs, and You 28\u003c\/p\u003e \u003cp\u003eInformation Flows 29\u003c\/p\u003e \u003cp\u003eComing Up with Ideas 31\u003c\/p\u003e \u003cp\u003eValue Analysis 31\u003c\/p\u003e \u003cp\u003eSorting and Filtering 34\u003c\/p\u003e \u003cp\u003eRanking, Categorizing, and Classifying 35\u003c\/p\u003e \u003cp\u003eReviewing the Idea Bank 37\u003c\/p\u003e \u003cp\u003eBrainstorming and Chance Encounters 38\u003c\/p\u003e \u003cp\u003eAI Limitations 41\u003c\/p\u003e \u003cp\u003ePitfalls 44\u003c\/p\u003e \u003cp\u003eAction Checklist 45\u003c\/p\u003e \u003cp\u003eNotes 46\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 Defining the Project 47\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe \u003ci\u003eWhat\u003c\/i\u003e, \u003ci\u003eWhy\u003c\/i\u003e, and \u003ci\u003eHow \u003c\/i\u003eof a Project Plan 48\u003c\/p\u003e \u003cp\u003eThe Components of a Project Plan 49\u003c\/p\u003e \u003cp\u003eApproaches to Break Down a Project 53\u003c\/p\u003e \u003cp\u003eProject Measurability 62\u003c\/p\u003e \u003cp\u003eBalanced Scorecard 63\u003c\/p\u003e \u003cp\u003eBuilding an AI Project Plan 64\u003c\/p\u003e \u003cp\u003ePitfalls 66\u003c\/p\u003e \u003cp\u003eAction Checklist 69\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 Data Curation and Governance 71\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eData Collection 73\u003c\/p\u003e \u003cp\u003eLeveraging the Power of Existing Systems 81\u003c\/p\u003e \u003cp\u003eThe Role of a Data Scientist 81\u003c\/p\u003e \u003cp\u003eFeedback Loops 82\u003c\/p\u003e \u003cp\u003eMaking Data Accessible 84\u003c\/p\u003e \u003cp\u003eData Governance 85\u003c\/p\u003e \u003cp\u003eAre You Data Ready? 89\u003c\/p\u003e \u003cp\u003ePitfalls 90\u003c\/p\u003e \u003cp\u003eAction Checklist 94\u003c\/p\u003e \u003cp\u003eNotes 94\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 Prototyping 97\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIs There an Existing Solution? 97\u003c\/p\u003e \u003cp\u003eEmploying vs. Contracting Talent 99\u003c\/p\u003e \u003cp\u003eScrum Overview 101\u003c\/p\u003e \u003cp\u003eUser Story Prioritization 103\u003c\/p\u003e \u003cp\u003eThe Development Feedback Loop 105\u003c\/p\u003e \u003cp\u003eDesigning the Prototype 106\u003c\/p\u003e \u003cp\u003eTechnology Selection 107\u003c\/p\u003e \u003cp\u003eCloud APIs and Microservices 110\u003c\/p\u003e \u003cp\u003eInternal APIs 112\u003c\/p\u003e \u003cp\u003ePitfalls 112\u003c\/p\u003e \u003cp\u003eAction Checklist 114\u003c\/p\u003e \u003cp\u003eNotes 114\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 Production 117\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eReusing the Prototype vs. Starting from a Clean Slate 117\u003c\/p\u003e \u003cp\u003eContinuous Integration 119\u003c\/p\u003e \u003cp\u003eAutomated Testing 124\u003c\/p\u003e \u003cp\u003eEnsuring a Robust AI System 128\u003c\/p\u003e \u003cp\u003eHuman Intervention in AI Systems 129\u003c\/p\u003e \u003cp\u003eEnsure Prototype Technology Scales 131\u003c\/p\u003e \u003cp\u003eCloud Deployment Paradigms 133\u003c\/p\u003e \u003cp\u003eCloud API’s SLA 135\u003c\/p\u003e \u003cp\u003eContinuing the Feedback Loop 135\u003c\/p\u003e \u003cp\u003ePitfalls 135\u003c\/p\u003e \u003cp\u003eAction Checklist 137\u003c\/p\u003e \u003cp\u003eNotes 137\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 Thriving with an AI Lifecycle 139\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIncorporate User Feedback 140\u003c\/p\u003e \u003cp\u003eAI Systems Learn 142\u003c\/p\u003e \u003cp\u003eNew Technology 144\u003c\/p\u003e \u003cp\u003eQuantifying Model Performance 145\u003c\/p\u003e \u003cp\u003eUpdating and Reviewing the Idea Bank 147\u003c\/p\u003e \u003cp\u003eKnowledge Base 148\u003c\/p\u003e \u003cp\u003eBuilding a Model Library 150\u003c\/p\u003e \u003cp\u003eContributing to Open Source 155\u003c\/p\u003e \u003cp\u003eData Improvements 157\u003c\/p\u003e \u003cp\u003eWith Great Power Comes Responsibility 158\u003c\/p\u003e \u003cp\u003ePitfalls 159\u003c\/p\u003e \u003cp\u003eAction Checklist 161\u003c\/p\u003e \u003cp\u003eNotes 161\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8 Conclusion 163\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Intelligent Business Model 164\u003c\/p\u003e \u003cp\u003eThe Recap 164\u003c\/p\u003e \u003cp\u003eSo What are You Waiting For? 168\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix A AI Experts 169\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAI Experts 169\u003c\/p\u003e \u003cp\u003eChris Ackerson 169\u003c\/p\u003e \u003cp\u003eJeff Bradford 173\u003c\/p\u003e \u003cp\u003eNathan S. Robinson 175\u003c\/p\u003e \u003cp\u003eEvelyn Duesterwald 177\u003c\/p\u003e \u003cp\u003eJill Nephew 179\u003c\/p\u003e \u003cp\u003eRahul Akolkar 183\u003c\/p\u003e \u003cp\u003eSteven Flores 187\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix B Roadmap Action Checklists 191\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eStep 1: Ideation 191\u003c\/p\u003e \u003cp\u003eStep 2: Defining the Project 191\u003c\/p\u003e \u003cp\u003eStep 3: Data Curation and Governance 192\u003c\/p\u003e \u003cp\u003eStep 4: Prototyping 192\u003c\/p\u003e \u003cp\u003eStep 5: Production 193\u003c\/p\u003e \u003cp\u003eThriving with an AI Lifecycle 193\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix C Pitfalls to Avoid 195\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eStep 1: Ideation 195\u003c\/p\u003e \u003cp\u003eStep 2: Defining the Project 196\u003c\/p\u003e \u003cp\u003eStep 3: Data Curation and Governance 199\u003c\/p\u003e \u003cp\u003eStep 4: Prototyping 203\u003c\/p\u003e \u003cp\u003eStep 5: Production 204\u003c\/p\u003e \u003cp\u003eThriving with an AI Lifecycle 206\u003c\/p\u003e \u003cp\u003eIndex 209\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eJEFFREY L. COVEYDUC\u003c\/b\u003e 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.  \u003c\/p\u003e\u003cp\u003e\u003cb\u003eJASON L. ANDERSON\u003c\/b\u003e 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.   \u003c\/p\u003e\u003cp\u003eWe 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.  \u003c\/p\u003e\u003cp\u003eWe 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 \u003ci\u003eArtificial Intelligence for Business\u003c\/i\u003e, 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. \u003c\/p\u003e\u003cp\u003eAI 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.     \u003c\/p\u003e\u003cp\u003e\u003cb\u003eA PROVEN PROCESS FOR TRANSFORMING YOUR ORGANIZATION WITH AI TECHNOLOGY\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eThe 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. \u003ci\u003eArtificial Intelligence for Business\u003c\/i\u003e 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: \u003c\/p\u003e\u003cul\u003e \u003cli\u003eIdentify opportunities to reduce costs and capture market share using AI\u003c\/li\u003e \u003cli\u003eLocate the data you need to train AI models, and manage data assets professionally\u003c\/li\u003e \u003cli\u003eCreate a functional AI prototype to limit risk and demonstrate the AI value proposition\u003c\/li\u003e \u003cli\u003eConfidently deploy and iterate your AI solutions in production\u003c\/li\u003e \u003cli\u003eEstablish AI maturity using model libraries to capture profits and improve over time\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eThis book is perfect for business leaders who want a high-level roadmap showing the way to proven success in the world of AI.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47988765196517,"sku":"NP9781119651734","price":39.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119651734.jpg?v=1761781505","url":"https:\/\/k12savings.com\/es\/products\/artificial-intelligence-for-business-isbn-9781119651734","provider":"K12savings","version":"1.0","type":"link"}