Bradshaw, Anthony <br/><br/>
AI strategy to execution 

 - New Delhi  Wiley India Pvt. Ltd.  2024 
<br/><br/> Table of content:<br/>Preface<br/><br/>About the Authors<br/><br/>Acknowledgement<br/><br/>Chapter 1 Strategy to Execution Gap<br/><br/>1.1 Introduction<br/><br/>1.2 Business Strategy for AI and An Execution Plan – Why?<br/><br/>1.3 AI Business Strategies<br/><br/>1.4 Strategy to Execution Gap<br/><br/>1.5 Preparing Organizations for AI Journey<br/><br/>1.6 Journey Towards a “Data-Driven” Company<br/><br/>1.7 Transformation and Change Management<br/><br/>1.8 Contexts Explained by Historical Facts and Reasons to Become Data-Driven<br/><br/>1.9 Challenges<br/><br/>Chapter 2 Analytics Landscape<br/><br/>2.1 Introduction<br/><br/>2.2 Data and Analytics in a Nutshell<br/><br/>2.2.1 Artificial Intelligence (AI)<br/><br/>2.2.2 Machine Learning (ML)<br/><br/>2.2.3 From Statistics to Statistical Learning (SL)<br/><br/>2.2.4 Statistical Learning (SL)<br/><br/>2.2.5 Deep Learning (DL)<br/><br/>2.3 AI As Competitive Strategy<br/><br/>Chapter 3 School of Outputs and Outcomes<br/><br/>3.1 Introduction<br/><br/>3.2 Outputs, Outcome, and Operationalization: An Introduction<br/><br/>3.3 AI as an Enabler of Outcome<br/><br/>3.4 AI Operationalization<br/><br/>3.5 Roles and Responsibilities of Personnel and Technology in AI Operationalization<br/><br/>3.6 Operationalization via the Circle of Influence<br/><br/>3.7 AI Readiness Framework and Adoption Model<br/><br/>3.8 Outcome Calculation<br/><br/>3.9 Governance Principles for Outcome Realization<br/><br/>3.10 Outcome Measures<br/><br/>3.11 Data and Analytics-Specific Adoption Rate<br/><br/>Chapter 4 Data Culture and Change Management<br/><br/>4.1 Introduction<br/><br/>4.2 Need for Data-Driven Culture<br/><br/>4.3 Strong Organizational Change Management – Basis for AI Success<br/><br/>4.4 Change Management – Literature View<br/><br/>4.5 Change Management in Practice<br/><br/>4.6 Data-Driven Decision-Making (3DM) Execution Strategy<br/><br/>4.7 Culture Change from People and Project Perspectives<br/><br/>Chapter 5 The School of Expertise, Innovation, and Organizational Intelligence<br/><br/>5.1 Introduction<br/><br/>5.2 Expertise, Dynamic Capabilities, and Organizational Intelligence<br/><br/>5.3 Expertise Linked to Innovation<br/><br/>5.4 Strategic Workforce for AI Initiatives – Headhunting, Hiring, Onboarding, Chapter Lead, and HR Coach<br/><br/>5.5 School of Expertise and Chapters within AI Journey Initiative<br/><br/>5.6 Collaboration with Educational Institutes/Innovative Stakeholders<br/><br/>5.7 Expertise versus Innovation?<br/><br/>Chapter 6 The School of Execution<br/><br/>6.1 Introduction<br/><br/>6.2 The Silo Effect<br/><br/>6.3 The Golden Rules to Become Hyper-Relevant<br/><br/>6.4 Data Product Management/Leadership<br/><br/>6.5 Ways of Working<br/><br/>Chapter 7 Data Value Management<br/><br/>7.1 Introduction to Data Value Management<br/><br/>7.2 Data Governance<br/><br/>7.2.1 Data Governance: Implementation<br/><br/>7.3 Governance per Design<br/><br/>7.4 Data Architecture<br/><br/>7.5 Data Quality<br/><br/>7.6 Master Data<br/><br/>7.7 Metadata Management<br/><br/>7.8 Data Gathering Process and Warehousing<br/><br/>7.9 Data Governance Success Stories<br/><br/>7.10 Data Value Management – End-to-End Implementation<br/><br/>Chapter 8 Strategy for Data and Analytics<br/><br/>8.1 Strategy for Data and Analytics<br/><br/>8.2 Data Strategy and Analytics Strategy<br/><br/>8.3 Data and Analytics Setup<br/><br/>8.4 Framework to Support the Organization<br/><br/>8.5 The Federated Center of Competence<br/><br/>8.6 The CRISP-DM Model<br/><br/>8.7 Audit and Maintenance<br/><br/>8.8 MLOps via CI-CD Development<br/><br/>Chapter 9 Ethics and Privacy by Design<br/><br/>9.1 Introduction<br/><br/>9.2 Regulation<br/><br/>9.3 Fairness – Anti-Discrimination<br/><br/>9.3.1 Data Bias<br/><br/>9.3.2 Indirect Bias<br/><br/>9.3.3 Model Bias<br/><br/>9.4 Human Control and Interpretation<br/><br/>9.5 Unethical Use of AI<br/><br/>9.6 Enterprise Readiness to Manage AI-Related Risks<br/><br/>Chapter 10 Strategy to Execution (S2E) Framework<br/><br/>10.1 Introduction<br/><br/>10.2 Strategy and Execution Link<br/><br/>10.3 Strategy Block<br/><br/>10.4 Bridge to Execution Block<br/><br/>10.5 Execution Block<br/><br/>Chapter 11 Data Inspired Organization Management Technologies<br/><br/>11.1 Introduction<br/><br/>11.2 Strategic Workforce Management with DAFL (Data As Future Language)<br/><br/>11.3 Data As Future Language (DAFL) – in Reality<br/><br/>11.4 DAFL Seen As a Company Asset for Strategic Workforce Creation<br/><br/>Chapter 12 Data Storytelling<br/><br/>12.1 Introduction<br/><br/>12.2 History of Storytelling<br/><br/>12.3 Why Storytelling Matters?<br/><br/>12.4 Importance of Business Storytelling<br/><br/>12.5 The Columbia Story<br/><br/>12.6 Storytelling Frameworks<br/><br/>12.7 Data Storytelling<br/><br/>Summary<br/><br/>References<br/><br/>Index<br/>[https://www.wileyindia.com/ai-strategy-to-execution.html] 
<br/><br/> In this book, we discuss the “strategy to the execution gap” a leader of an organization encounters while adopting Artificial Intelligence (AI) in that organization.  The main focus is on value creation using AI and use of AI as competitive strategy. Although every organization across various industries is interested in integrating Artificial Intelligence into their business, a significant dilemma is the right AI strategy for their organization. <br/>(https://www.wileyindia.com/ai-strategy-to-execution.html) 
<br/><br/><label>ISBN: </label>9789357469975 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Artificial Intelligence
<br/><br/><label>Dewey Class. No.: </label>006.3  / BRA