AI Strategy to Execution

Preface

About the Authors

Acknowledgement

Chapter 1 Strategy to Execution Gap

1.1 Introduction

1.2 Business Strategy for AI and An Execution Plan – Why?

1.3 AI Business Strategies

1.4 Strategy to Execution Gap

1.5 Preparing Organizations for AI Journey

1.6 Journey Towards a “Data-Driven” Company

1.7 Transformation and Change Management

1.8 Contexts Explained by Historical Facts and Reasons to Become Data-Driven

1.9 Challenges

Chapter 2 Analytics Landscape

2.1 Introduction

2.2 Data and Analytics in a Nutshell

2.2.1 Artificial Intelligence (AI)

2.2.2 Machine Learning (ML)

2.2.3 From Statistics to Statistical Learning (SL)

2.2.4 Statistical Learning (SL)

2.2.5 Deep Learning (DL)

2.3 AI As Competitive Strategy

Chapter 3 School of Outputs and Outcomes

3.1 Introduction

3.2 Outputs, Outcome, and Operationalization: An Introduction

3.3 AI as an Enabler of Outcome

3.4 AI Operationalization

3.5 Roles and Responsibilities of Personnel and Technology in AI Operationalization

3.6 Operationalization via the Circle of Influence

3.7 AI Readiness Framework and Adoption Model

3.8 Outcome Calculation

3.9 Governance Principles for Outcome Realization

3.10 Outcome Measures

3.11 Data and Analytics-Specific Adoption Rate

Chapter 4 Data Culture and Change Management

4.1 Introduction

4.2 Need for Data-Driven Culture

4.3 Strong Organizational Change Management – Basis for AI Success

4.4 Change Management – Literature View

4.5 Change Management in Practice

4.6 Data-Driven Decision-Making (3DM) Execution Strategy

4.7 Culture Change from People and Project Perspectives

Chapter 5 The School of Expertise, Innovation, and Organizational Intelligence

5.1 Introduction

5.2 Expertise, Dynamic Capabilities, and Organizational Intelligence

5.3 Expertise Linked to Innovation

5.4 Strategic Workforce for AI Initiatives – Headhunting, Hiring, Onboarding, Chapter Lead, and HR Coach

5.5 School of Expertise and Chapters within AI Journey Initiative

5.6 Collaboration with Educational Institutes/Innovative Stakeholders

5.7 Expertise versus Innovation?

Chapter 6 The School of Execution

6.1 Introduction

6.2 The Silo Effect

6.3 The Golden Rules to Become Hyper-Relevant

6.4 Data Product Management/Leadership

6.5 Ways of Working

Chapter 7 Data Value Management

7.1 Introduction to Data Value Management

7.2 Data Governance

7.2.1 Data Governance: Implementation

7.3 Governance per Design

7.4 Data Architecture

7.5 Data Quality

7.6 Master Data

7.7 Metadata Management

7.8 Data Gathering Process and Warehousing

7.9 Data Governance Success Stories

7.10 Data Value Management – End-to-End Implementation

Chapter 8 Strategy for Data and Analytics

8.1 Strategy for Data and Analytics

8.2 Data Strategy and Analytics Strategy

8.3 Data and Analytics Setup

8.4 Framework to Support the Organization

8.5 The Federated Center of Competence

8.6 The CRISP-DM Model

8.7 Audit and Maintenance

8.8 MLOps via CI-CD Development

Chapter 9 Ethics and Privacy by Design

9.1 Introduction

9.2 Regulation

9.3 Fairness – Anti-Discrimination

9.3.1 Data Bias

9.3.2 Indirect Bias

9.3.3 Model Bias

9.4 Human Control and Interpretation

9.5 Unethical Use of AI

9.6 Enterprise Readiness to Manage AI-Related Risks

Chapter 10 Strategy to Execution (S2E) Framework

10.1 Introduction

10.2 Strategy and Execution Link

10.3 Strategy Block

10.4 Bridge to Execution Block

10.5 Execution Block

Chapter 11 Data Inspired Organization Management Technologies

11.1 Introduction

11.2 Strategic Workforce Management with DAFL (Data As Future Language)

11.3 Data As Future Language (DAFL) – in Reality

11.4 DAFL Seen As a Company Asset for Strategic Workforce Creation

Chapter 12 Data Storytelling

12.1 Introduction

12.2 History of Storytelling

12.3 Why Storytelling Matters?

12.4 Importance of Business Storytelling

12.5 The Columbia Story

12.6 Storytelling Frameworks

12.7 Data Storytelling

Summary

References

Index