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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
Unlock unprecedented levels of value at your firm by implementing artificial intelligence
In The Secrets of AI Value Creation: Practical Guide to Business Value Creation with Artificial Intelligence from Strategy to Execution, a team of renowned artificial intelligence leaders and experts delivers an insightful blueprint for unlocking the value of AI in your company. This book presents a comprehensive framework that can be applied to your organisation, exploring the value drivers and challenges you might face throughout your AI journey. You will uncover effective strategies and tactics utilised by successful artificial intelligence (AI) achievers to propel business growth. In the book, you’ll explore critical value drivers and key capabilities that will determine the success or failure of your company’s AI initiatives. The authors examine the subject from multiple perspectives, including business, technology, data, algorithmics, and psychology.
Organized into four parts and fourteen insightful chapters, the book includes:
- Concrete examples and real-world case studies illustrating the practical impact of the ideas discussed within
- Best practices used and common challenges encountered when first incorporating AI into your company’s operations
- A comprehensive framework you can use to navigate the complexities of AI implementation and value creation
An indispensable blueprint for artificial intelligence implementation at your organisation, The Secrets of AI Value Creation is a can’t-miss resource for managers, executives, directors, entrepreneurs, founders, data analysts, and business- and tech-side professionals looking for ways to unlock new forms of value in their company.
The authors, who are industry leaders, assemble the puzzle pieces into a comprehensive framework for AI value creation:
Michael Proksch is an expert on the subject of AI strategy and value creation. He worked with various Fortune 2000 organisations and focuses on optimising business operations building customised AI solutions, and driving organisational adoption of AI through the creation of value and trust.
Nisha Paliwal is a senior technology executive. She is known for her expertise in various technology services, focusing on the importance of bringing AI technology, computing resources, data, and talent together in a synchronous and organic way.
Wilhelm Bielert is a seasoned senior executive with an extensive of experience in digital transformation, program and project management, and corporate restructuring. With a proven track record, he has successfully led transformative initiatives in multinational corporations, specialising in harnessing the power of AI and other cutting-edge technologies to drive substantial value creation.
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Seitenzahl: 446
Veröffentlichungsjahr: 2024
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Table of Contents
Cover
Table of Contents
Title Page
Copyright
Dedication
Introduction
Overcoming the Challenges of a Multimillion‐Dollar AI Project
Three Authors, Three Perspectives, One Framework
Who This Book Is For
What You Will Learn
Part I: Value Creation Potential
Part II: Overcoming Value Challenges
Part III: Enterprise Integration
Part IV: Required Capabilities
Part I: Value Creation Potential
1 The Journey of AI Achievers
The Finish Line
The Milestones
2 Three Factors of AI Business Value Creation
The AI Industry Use Case Approach
The AI Value Factor Approach
Evaluating AI's Business Opportunity
Evaluating AI's Feasibility Risk
Evaluating AI's Adoption Risk
3 Four Types of AI Value Creation
Process Optimisation
Decision‐Making Augmentation
Decision‐Making Automation
AI Products and Services
Part II: Overcoming Value Challenges
4 AI‐Centric Elements
Business Domain Knowledge
Algorithmic
Data
Stakeholder Motivation
5 Collecting Valuable Data
A Data Value Framework
Putting a Value on Data
Identifying the Right Data
Defining the Value of a Data Portfolio
6 Creating Actionable Insights
Actionability Through Output‐Based Selection
Actionability Through Output Adaptation
Actionability Through AI Automation
Identifying Actionable Inputs for Output Adaptation
Increasing Actionability Through Decision Disaggregation
Increasing Actionability Through Experimentation
7 Building Stakeholder Trust
Creating Trust
Establishing Trust in an AI Initiative
Establishing Trust in an AI Solution
The Requirement of AI Transparency Beyond Building Trust
8 Managing AI's Decision‐Making
Explaining AI's Decision‐Making
The Limitations of AI's Decision‐Making
Controlling AI's Decision‐Making
Part III: Enterprise Integration
9 Crafting an AI Strategy
Delivering an Executable AI Strategy
Aspiration
Focus and Value Creation
Capabilities
Management System
10 Leading Successful Projects
AI Project Management
AI Project Life Cycle and Project Development
Project Development
Selecting the Right Development Approach
Project Planning Components
Leading Change in AI Projects
11 Cultivating an AI‐Friendly Culture
The Threat of AI to the Status Quo of Corporate Culture
Culture as a Framework for Corporate Identity
The Depth of an Organisation's Culture
The AI‐Friendly Culture of AI Achievers
Breaking Down a Corporate Culture
Methods for Cultivating an AI‐Friendly Culture
Best Practices for Cultivating an AI Enterprise Culture
Part IV: Required Capabilities
12 Technology
The Technology Requirements for an AI Pipeline
AI Software Capabilities Along the AI Pipeline
Hardware/Compute Resources Along the AI Pipeline
13 Data Management
Data Management
Data Sourcing
Data Governance
Data Infrastructure for AI
Data Archiving and Deletion
The Trend to a Decentralised Data Management
14 Talent
From Tasks to Competencies and Skills to Roles
Identifying Competencies and Skills
Defining the Roles for AI Value Creation
Organising an AI initiative
Conclusion
About the Authors
Michael Proksch
Nisha Paliwal
Wilhelm ‘Wil’ Bielert
About the Contributors
References
Index
End User License Agreement
List of Tables
Chapter 2
Table 2.1 AI Business Opportunity Calculation for Insurance Retention
Chapter 3
Table 3.1 AI‐Process Optimized Versus Traditional Campaign Comparison
Table 3.2 Value‐Driving Factors for Process Optimisation
Table 3.3 Value‐Driving Factors for Decision‐Making Augmentation
Table 3.4 Value‐Driving Factors for Decision‐Making Automation
Table 3.5 Value‐Driving Factors for AI Products and Services
Chapter 13
Table 13.1 Comparison Primary Versus Secondary Data
Chapter 14
Table 14.1 Tasks × Competencies/Skills
Table 14.2 Technical Competencies Overview
Table 14.3 Nontechnical Competencies Overview
Table 14.4 Soft Skills Overview
Table 14.5 Tasks × Roles
Table 14.6 AI Value Creation Role Overview
List of Illustrations
Three Authors, Three Perspectives, One Framework
Figure I.1 Framework for AI Value Creation
Figure I.2 Scan to Discover Exclusive Insights at www.aivaluesecrets.com
Chapter 2
Figure 2.1 Value Creation Formula
Figure 2.2 Value Creation Factors
Figure 2.3 Core AI Stakeholders
Chapter 5
Figure 5.1 From Data to Wisdom Pyramid
Figure 5.2 All Potential Customers with Percentage Unable to Pay Credit Card...
Figure 5.3 Potential Customers by Age with Percentage Unable to Pay Credit C...
Figure 5.4 Potential Customers by Income with Percentage Unable to Pay Credi...
Figure 5.5 Potential Customers by Age × Income with Percentage Unable to Pay...
Figure 5.6 Working Backwards from Business Outcome to Data
Figure 5.7 RODA Formula
Chapter 6
Figure 6.1 The Two Pathways of Insights
Figure 6.2 Overview of Car Dealerships of Luxury Car Manufacturer across the...
Figure 6.3 Overview of Most Important Sales Volume Predictor Importance Acro...
Figure 6.4 Impact of Sales Volume Predictors for Dealership ABC
Figure 6.5 Total Short‐Term Media ROI Calculation Across Media Channels
Figure 6.6 Disaggregation of Media‐Related Decision Opportunities
Figure 6.7 Total Short‐Term Media ROI Calculation Across Social Media Channe...
Chapter 8
Figure 8.1 Comparison of Variable Importance Between Two Methods
Figure 8.2 Comparison of Variable Importance Between Two Perspectives
Figure 8.3 Individual and Cumulated Variable Importance
Figure 8.4 Confusion Matrix Concept
Figure 8.5 Two Confusion Matrix Examples
Chapter 9
Figure 9.1 AI Strategy Choices
Figure 9.2 AI and Business Strategy Choice Alignment
Chapter 10
Figure 10.1 Predictive Project Development Approach
Figure 10.2 Adaptive Project Development Approach
Chapter 11
Figure 11.1 The Layers of Culture
Chapter 12
Figure 12.1 AI Pipeline Steps
Chapter 14
Figure 14.1 From Tasks to Roles
Guide
Cover Page
Title Page
Copyright
Dedication
Introduction
Three Authors, Three Perspectives, One Framework
Table of Contents
Begin Reading
Conclusion
About the Authors
About the Contributors
References
Index
Wiley End User License Agreement
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THE SECRETS OF AI VALUE CREATION
PRACTICAL GUIDE TO BUSINESS VALUE CREATION WITH ARTIFICIAL INTELLIGENCE FROM STRATEGY TO EXECUTION
MICHAEL PROKSCHNISHA PALIWALWILHELM BIELERT
This edition first published 2024
©2024 Michael Proksch, Nisha Paliwal, and Wilhelm Bielert
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions.
The right of Michael Proksch, Nisha Paliwal, and Willhelm Bielert to be identified as the authors of this work has been asserted in accordance with law.
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John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA
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Limit of Liability/Disclaimer of Warranty
While the publisher and authors have used their best efforts in preparing this work, they make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives, written sales materials or promotional statements for this work. This work is sold with the understanding that the publisher is not engaged in rendering professional services. The advice and strategies contained herein may not be suitable for your situation. You should consult with a specialist where appropriate. The fact that an organization, website, or product is referred to in this work as a citation and/or potential source of further information does not mean that the publisher and authors endorse the information or services the organization, website, or product may provide or recommendations it may make. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read. Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages.
Library of Congress Cataloging‐in‐Publication Data
ISBN 9781394233625 (Cloth)
ISBN 9781394233632 (ePub)
ISBN 9781394233649 (ePDF)
Cover Design and Image: Wiley
This book is dedicated to our spouses and children – the value creators in our lives.
With love and appreciation,
Michael, Nisha, and Wilhelm
We would like to express our gratitude to all contributing experts, reviewers, and especially our editor, Sheshagiri Hegde. Their contributions and guidance have been invaluable, and we are tremendously grateful for their dedication to this book.
Introduction
There is no passion to be found playing small – in settling for a life that is less than the one you are capable of living.
—Nelson Mandela (politician, former president of South Africa)
Welcome to the world of AI, where the pace of change is relentless, and the opportunities and stakes seem higher than ever – especially with the latest buzz surrounding AI's tremendous potential for value creation. Through the newest developments in generative AI with solutions such as ChatGPT and Midjourney, the age‐old question of AI hype versus reality takes center stage once more. Although there's undoubtedly substantial potential for this technology to create value (McKinsey 2023), Sequoia, a prominent venture capital firm known for its early investments in companies such as Apple, Google, Oracle, YouTube, WhatsApp, and Airbnb, has pointed out that even though it has outperformed the SaaS market and generated more than $1 billion in revenue within a mere few months, ‘generative AI's biggest problem [right now] is not finding use cases or demand or distribution, it is proving value’ (Huang and Grady 2023). But this challenge is by no means new to the field of AI and forms the central theme of this book.
Whether you're just someone interested in exploring the subject or a seasoned business leader, an AI expert, an enterprise data and AI manager, a data scientist, an engineer, or an AI start‐up, you might be seeking guidance on how to create value with AI. Regardless of your background, you may find yourself wrestling with the puzzle of AI's value creation. You may have even turned to some of the greatest minds across algorithmic, data, and information technology – legends like Alan Turing, Geoffery Hinton, Marvin Minsky, Trevor Hastie, Yoshua Bengio – in your quest for insights and inspiration. In your search for answers, you might have even sought advice from others within your network of AI experts. Engineers stress the importance of deploying AI algorithms quickly with the right software and hardware, whereas data scientists insist that a more accurate algorithm is the key to value creation. Data management experts emphasise the critical role of data quality and accessibility, and business experts highlight the need to connect AI outcomes to key performance indicators (KPIs). Each answer provides a valuable piece of the puzzle, but none offers a comprehensive solution. The fact is that the creation of value through AI necessitates a more holistic perspective. If this story sounds familiar to you, you are not alone. Even AI experts lack the view of the entire value creation process and its multifaceted components.
Although AI's value creation indeed entails unique nuances, this book demonstrates that AI‐mature organisations leverage proven methodologies to sustainably generate value with this technology. This insight stands in contrast to the belief that AI is an entirely unprecedented and exclusively distinctive domain, rendering past experiences irrelevant. This contrast underscores the fact that despite the influx of new algorithms, cloud computing capabilities, and access to big volumes of data during the latest AI wave, only a limited number of organisations have successfully created business value with AI (Rayome 2019). Consequently, the questions that remain are as follows:
How can you create value with AI?
What are the challenges of AI value creation, and how can they be overcome?
What kind of methodologies and capabilities are needed for your AI initiative to succeed?
Overcoming the Challenges of a Multimillion‐Dollar AI Project
Let's explore some of the AI value creation challenges through a fictitious example. Your organisation, a company that produces and sells a wide range of family care, health care, and beauty products is experiencing a decline in its market share and sales volume. An increase in competition and changing consumer preferences have influenced your industry and created pressure on your leadership team to adapt. The leadership team puts you in charge to leverage data and AI to gain a competitive edge with the goal to improve sales.
Creating a Business Opportunity
As you are trying to improve sales, you treat the sales team as your stakeholders. When you ask the sales team how AI could help augment their operations, their initial response is to focus on forecasting sales volume. However, even with accurate sales volume forecasts generated by AI, a significant challenge remains: how does that insight create a business opportunity for the sales team? The salespeople will not know how, where, and which actions to take to affect the predicted sales volume.
You know that the real business opportunity in your AI project comes from providing insights to drive actions with AI's capabilities on an individual store level. For example, AI could be applied to predict sales opportunities for each store instead of just predicting sales volume that creates a valuable business opportunity and helps to prioritise stores based on their business potential. However, AI's output not only equips the sales team with the necessary insights to identify which stores to prioritise but also empowers them to take high‐impact actions that can drive sales growth. For instance, if an AI algorithm predicts that a store has the potential to increase sales volume by 10% in a given month, the sales team can take targeted and high‐impact actions, such as keeping more stock to avoid stock‐outs, making promotional offers, adjusting pricing strategies, increasing product visibility, and improving customer engagement.
Building a Feasible AI Solution
By harnessing the power of data and connecting it with the help of AI to business outcomes, organisations can optimise their sales operations. However, managing the decision‐making process of AI in augmenting or automating business decisions with accurate information poses its challenges. Selecting the right algorithmic approach to explain the reasons behind business opportunities requires a keen understanding of the limitations and capabilities of AI.
The feasibility challenge of your AI project is related to choosing the right approach to not only accurately predict sales opportunities but also identify the actions to drive sales. Selecting the wrong approach and data can easily lead to taking the wrong actions and a negative business outcome. For example, the variables you choose have to accurately represent potential actions, the data has to accurately reflect them, and the solution's output has to align with the business operations of the sales team. However, AI is not infallible and market and environmental conditions might change, and, therefore, proper monitoring of AI's decision‐making has to be implemented, and limitations defined.
The Importance of AI Adoption
Last, but not least, the adoption of the AI solution is crucial for driving business outcomes. Without adoption, even the most sophisticated AI algorithms will fail to deliver the desired business value. Therefore, you prioritise understanding the motivations, risks, and concerns of all stakeholders involved in your AI project and build trust with each of them.
For example, the sales team has a central role in the success of your AI solution aimed at growing sales volume through impactful actions. Therefore, it is essential to understand how the AI solution will affect the commission and incentives of the sales team, align with the goals and bonuses of team leadership, and fit within the budget of the sales department. It is also important to address any potential risks or concerns, such as fears of job loss due to increased efficiency or the perception of the AI solution as a tool for monitoring and tracking sales representatives’ performance and actions. Concerns are often subjective, but you take them seriously and proactively address them to gain buy‐in ideally from all stakeholders.
This example about your imaginary organisation illustrates that creating business value with AI requires an understanding of various value creation factors and their connected challenges. No worries, you did a great job and were successful in creating a significant business impact in that project. Your project was rolled out in various countries and created a tremendous multibillion dollar value for your organisation. However, you might not be that lucky in the next project and are looking for a repeatable framework to reduce the risk of failure.
The goal of developing value‐creating AI solutions is an ambitious one due to the origin of those challenges in business, data and algorithmics, psychology, and technology. This book aims to help you have an impact on your organisation by providing a holistic framework for delivering value with AI.
Three Authors, Three Perspectives, One Framework
We – Michael, Nisha, and Wilhelm (‘Wil’) – wrote this book in order to share our experience. We integrate multiple perspectives to provide a holistic understanding of AI and its impact in the world of business. Collectively we have a wealth of knowledge and experience, with a combined 65+ years of expertise in AI, psychology, business, data, and technology. Having led data and AI organisations, dozens of AI projects for various global organisations, and engaged in many conversations with hundreds of experts in AI, we offer you a comprehensive and multifaceted view of the complex and rapidly evolving field of AI value creation to help you make your AI opportunity reality. We will help you figure out how to unlock the value of AI in your context. Each of us provides answers evolving from different experiences in AI value creation.
Michael is an AI strategy and execution expert with an academic background in business and economics. His work focusses on the importance of creating a competitive advantage, building customised analytics solutions, and driving AI adoption through the creation of value and trust. He emphasises the execution and successful integration of AI solutions into enterprise business processes.
Nisha is a senior technology executive with an academic background in microbiology. She is known for her expertise in various technology services, focussing on the importance of bringing AI technology, computing resources, data, and talent together in a synchronous and organic way. She highlights the capabilities required to drive value for all stakeholders across a multinational organisation.
Wil is a seasoned senior executive with an academic background in management and extensive experience in digital transformation, program and project management, and corporate restructuring. He specialises in the strategic implementation of AI technology in organisational infrastructure. He stresses the significance of implementing AI solutions that fit into business processes and are able to support and guide business decisions.
Though we have each travelled different roads, we realised that we are missing a cohesive framework that can bring together the essential elements of AI and analytics, business and culture management, technology and data. Drawing on our diverse backgrounds and perspectives, all three of us held a piece of the overall value creation framework in our hands. As we compared our individual pieces, a more significant big picture emerged, with many connections and interdependencies. With this larger, holistic perspective, we set out to propose a comprehensive framework for creating business value with AI – one that would be practical, actionable, and effective for organisations seeking to integrate this powerful technology.
Who This Book Is For
This book's goal is to provide you with a framework to unlock the potential of AI for business value creation. Our aim is to guide you through potential pitfalls by sharing our learnings on our journey to AI value creation. It offers approaches for your own evolution to become an AI value creator, helping you to make the most of this powerful technology. With actionable advice and insights from our industry experts, this book is essential reading for you to explore the subject, especially if you are one of the following:
Business leader:
AI and other digital technologies are a key part of your overall digital transformation journey. You know many business areas where you see optimisation potential but do not know how AI can help. What best practices can you learn from those like you who have been creating value with AI? What are the required capabilities and what is the necessary budget to build your own AI initiative?
Analytics/AI/data science leader:
You've accepted the challenge of guiding your organisation's journey to success. You have excellent technical skills to manage AI projects, but some pieces for value creation seem to still be missing. How can you overcome the various challenges of value creation and build AI capabilities as a solid foundation to create tangible business value?
Enterprise data and AI manager:
You are tasked with leading multiple data and analytics projects within your organisation, working with business stakeholders and heading a team of data scientists, analysts, or engineers. But how can you be sure that these projects will be successful and create value? What factors can help ensure that business value is created from these initiatives? How can you plan your capabilities and resources efficiently?
Data scientist/analyst/AI and data engineer:
If you are part of the data science department of your organisation, you are responsible for building solutions and providing insights that support business outcomes. Although you are aware of the scope of your individual contribution, you might feel that you are missing the bigger picture. How can you make sure that your contribution fits into the overall value‐creating solution? How can you increase the individual impact of your work to increase your value within your organisation?
Technology/engineering leader:
You are well positioned to collaborate with digital leaders and drive enterprise IT operations. However, with new AI technology innovations you might need to adjust your technology stack and want to know what are the required capabilities that AI requires within your technology environment. Moreover, some of your engineers may need to acquire new skills, but what are the skills that are required for the integration of AI into your organisation's technology stack?
AI software vendor/start‐up:
Your company has so much to offer to its clients, but you recognise the hesitancy they may have when it comes to the adoption of your AI solution. So, how do you find the right clients, showcase the right elements of your AI solution, and effectively focus your business development? How can you construct a strategy that will not only enable you to secure the right clients but also ensure the successful adoption of your product?
No matter your role, from executives to employees, leaders to collaborators, this book will help you understand how to maximise the value of AI. With practical advice and real‐world examples, you will be able to make an impact that is felt far beyond your job title.
What You Will Learn
This book provides a comprehensive overview of the processes for and capabilities needed to create business value with AI, from understanding what AI value creation means and how it can be achieved, to overcoming the challenges on the journey, to integrating AI in the enterprise to scale it, to understanding the capabilities needed for successful AI value creation. However, there is no guarantee for success and every framework has to be adjusted to be successful in your context. Illustrated in Figure I.1 is our framework for AI value creation, connecting the various topics within this book.
Figure I.1 Framework for AI Value Creation
Part I: Value Creation Potential
In Part I of this book, we explore how AI can be applied to create business value. We will provide an inside look at how AI achievers such as John Deere, Coca‐Cola, Google, Microsoft, and IBM have used it to successfully create sustainable value for their operations. We will investigate the journey AI achievers went through and show best practices of how to create value with AI. We will introduce the major factors of AI value creation explained in various use cases across industries and functional areas, from sales and marketing to customer management and underwriting. We'll also introduce a classification of different types of AI value creation with detailed examples to help you identify potential AI use cases in your own business environment. By the end of the first part of this book, you'll have a good overview of how to approach the topic of AI value creation.
Part II: Overcoming Value Challenges
Although there is a clear potential to create value with AI and AI achievers have made it reality, many AI projects in enterprises and AI start‐ups fail to do so (Rayome 2019). It is the challenges on the way to AI value creation that many organisations are not aware of. The hurdles many organisations face are due to the subtle but crucial distinctions between AI's value creation elements and those of business intelligence or software development. Additionally, we explore how to evaluate the value of data, identify valuable data that can be applied to AI, create actionable insights, and ultimately turn those findings into a business opportunity. Although AI is typically seen as a way to generate business value for organisations, its successful adoption depends on the trust from all of its stakeholders and so we explore how to build it. The last hurdle to AI value creation is understanding how AI supports and makes decisions. Although we may assume that mastering the math behind an algorithm is enough to comprehend how it works, this is not the case. Managing AI's decision‐making is therefore critical to develop solutions that deliver sustainable value.
Part III: Enterprise Integration
You've done it: you've successfully implemented an AI project, and it's already creating value. But now the real challenge begins: how do you scale AI value creation across your organisation? How do you drive enterprise‐wide adoption and maximise the potential of your AI capabilities? And, most important, what do you need to do to ensure that your AI adoption efforts don't backfire? The answers to these questions will determine the success or failure of your AI initiative. Therefore we address the enterprise‐wide integration of AI, demonstrating how to craft an executable AI strategy that aligns with your business's overall strategy. Doing so will enable you to drive AI adoption at the leadership level, focus on the AI value areas within your organisation; plan for the necessary capabilities, budgets, and measures of success; and showcase the initiative's value contribution. Although it might seem obvious that AI projects should be managed, the appropriate steering of AI projects with the appropriate methodologies is often missing. To overcome their value creation challenges we discuss various development approaches related to AI projects, crucial project planning elements, and how to lead change to drive AI adoption within AI projects. Organisations that have achieved success with AI implementation understand the importance of cultivating an AI‐friendly culture. To foster an AI‐friendly environment, AI achievers have understood the depth of corporate culture and implemented tools to facilitate the right corporate values of experimentation, collaboration, and innovation. This has enabled them to unlock the potential of AI, scaled the technologies’ decision‐making capabilities across the enterprise, and maximised its benefits.
Part IV: Required Capabilities
Tackling AI value creation is a complex challenge, one that can only be overcome with the right capabilities in place. To enable AI value creation, organisations must have the necessary technology, data management, and, most important, the right talent. We therefore dive into the fundamental capabilities and elements of AI value creation. Understanding the technical capabilities required to build an AI pipeline that brings value to an organisation is essential. We explore the hardware and software requirements that are necessary to develop AI algorithms and to ensure the successful deployment of AI in the production environment. There is no AI at scale without data at scale. To realise value creation with AI, your data must be managed properly, and the right data storage and processes must be employed to develop and deploy AI algorithms. From an AI talent perspective, data scientists and data engineers may be crucial roles for successful AI initiatives, but the experience of AI achievers shows that a broader set of competencies and skills is necessary for value creation. We describe the tasks at hand, the required competencies and skills to fulfil, and the specifics of the roles that need to be defined for an AI initiative to be successful. Additionally, we address various organisational structures of the AI initiative to tackle the various challenges presented throughout the book.
Reading this book, you'll learn that there are many misconceptions about how to generate value with AI. We'll discuss the misconception that a large investment in AI is necessary to create value, that AI value creation is a purely technological issue, that an AI project should start with a business problem, and that complex algorithms are necessary to produce the best business results. We focus on the value creation we have experienced and seen and will not focus on the hype about AI's potential future risks or value creation potential. With a wealth of examples and use cases, this book will show you how to unlock immense value with AI. This book is also about giving you the skills to become an AI value creator regardless of the industry you are in, whether you work for someone or have your own company. (See Figure I.2.)
You don't have to do it alone; join the conversation with our authors on our website (www.aivaluesecrets.com), give us feedback, and access more content. Unlock your potential and become an AI value creator today.
Figure I.2 Scan to Discover Exclusive Insights at www.aivaluesecrets.com
Part IValue Creation Potential
As the excitement surrounding AI continues to grow, the question remains: what is the real potential of AI to create value? Although the overall answer may not always be clear, one thing is certain: few organisations, which we will refer to as AI achievers, have leveraged AI to create significant business value. Although there are some industry‐specific use cases that contribute to value creation with AI for all organisations, there are company‐specific best practices that drive AI business value creation.
By studying their AI journeys and mindsets, we can gain invaluable insight into the potential of AI, the best practices for leveraging it, and how to create sustained business value with it. In Part I of this book, we will uncover the primary factors to AI value creation, examine when it might be wise to buy AI solutions rather than building your own, and outline detailed examples of how to create business opportunities and reduce the risks in AI. We will also provide a classification of AI value creation types, which make it possible to identify the right AI use cases for any organisation.
1The Journey of AI Achievers
In the coming years we will be limited not by technology but by imagination.
—John Leonard (CEO of Intellia Therapeutics)
As we look at the history of various organisations, we see that all evolved and adapted to new technologies over the years. But what sets apart the AI achievers is not their sole focus on AI, but rather their ability to incorporate AI into their tool set and drive sustainable value for their organisation. We will take a closer look at some of the AI achievers, including Amazon, John Deere, Chipotle, Commonwealth Bank of Australia, Coca‐Cola, IBM, Intel, Microsoft, and Google, among others. These organisations are examples of how innovative applications of AI can create value within and beyond the enterprise.
However, success did not happen overnight. Their AI journey, often spanning two decades or more, has been a marathon, which required not just time but dedication. But, it's not the total time invested that yields results at the end of their AI journey, it's the milestones they set along the way that have created value. The secret of their persistence seems to be their mindset. Similar to marathon runners, who visualise the finish line to fight the pain and discomfort on the way, AI achievers have a specific mindset to stay focussed on the long and winding journey ahead of them.
Although many manufacturing organisations have recently made significant investments in their AI capabilities, one particular industrial manufacturer stands out as an AI achiever: John Deere. With nearly two centuries of history, John Deere is recognised as one of the world's foremost industrial manufacturers. Today, they extensively integrate AI into their agricultural equipment and operations to the extent of providing AI‐driven services to farmers (Meffert and Swaminathan 2017).
It was 2013 when John Deere introduced a visionary approach to the future of agriculture, placing significant emphasis on cutting‐edge technologies such as AI. Their ‘Farm Forward’ vision showcased the concept of an autonomous farm, where machinery could be centrally controlled and managed. During a compelling demonstration, a farmer was portrayed monitoring data and supervising machinery from a home console, while AI made real‐time operational decisions (21 Century Equipment 2013). This demonstration effectively communicated the company's forward‐thinking vision. John Stone, senior vice president of the Intelligent Solutions Group (ISG) at John Deere, captures the future role of AI in farming, stating, ‘Farmers have long served as the primary “sensors” on their farms, relying heavily on visual observations. The appearance of the soil, the health of the plants indicated by their visual cues, the presence of pests—these visual elements play a crucial role in farming. The ongoing revolution in deep learning has opened up exciting possibilities to solve long‐standing problems that farmers have dreamt of solving for years. Computer vision systems and deep neural networks present a highly promising future for these technologies within the agricultural sector’ (Marr 2020).
The Finish Line
Running a marathon requires its participants to deal with challenging terrain, varying environmental conditions, and a constantly changing landscape. But how do marathon runners stay in the zone for more than four hours and make it to the end? Those successful in finishing the marathon keep track of their progress and stay focussed on the finish line (Samson et al. 2015). In the same way, the journey of our AI achievers shows that their success requires the focus on the state of the possible with AI. It is that unique perspective that will shape the business opportunities of AI and define what organisations are able to do with the technology.
However, what is the unique perspective AI achievers have in common, that enables them to truly tap into the technology's potential? Some of the AI achievers think about AI in the following way:
Ginni Rometty, CEO and president of IBM, has articulated IBM's mindset towards AI: ‘Some people call this artificial intelligence, but the reality is this technology will enhance us. So instead of artificial intelligence, I think we'll augment our intelligence’ (Marr
2017b
).
Microsoft CEO Satya Nadella talks about the company's efforts to develop an AI ‘with human preferences and societal norms and you're not going to do that in a lab. You have to do that out in the world’ (O'Brien
2023
).
Elon Musk, CEO of SpaceX and Tesla, issued a stern warning about AI: ‘Robots will be able to do everything better than us. I have exposure to the most cutting edge AI, and I'm telling you, the future is worrying’ (Clifford
2017
).
Drawing on the mindset of AI achievers and the experiences of organisations that have embraced AI, Sebastian Thrun, German innovator, entrepreneur, and computer scientist, summarises the shared vision of AI in the following way: ‘Nobody phrases it this way, but I think that artificial intelligence is almost a humanities discipline. It really is an attempt to understand human intelligence and human cognition’ (Marr 2017b).
In other words, AI achievers are those who embrace the idea that AI can augment humans to overcome their limitations. This collective belief in AI is propelling AI achievers closer to a new horison of business opportunities and keeping them on their AI marathon journey. This very belief forms the foundation that we share, and the core theme of this book is based on the augmentation of human potential through AI.
The Milestones
AI achievers had to overcome their fair share of obstacles on their long and rocky journey, but each milestone they achieved helped to spur them on to reach their ultimate goal. Furthermore, each milestone brought them significant value, new insights, and propelled them ever closer to their finish line.
Data‐Driven Decision‐Making
The first step of the journey, however, did not begin with AI for many AI achievers, but with an ambition to revolutionise their organisations with a data‐driven mindset. A data‐driven mindset is rooted in the application of data to improve the quality of human decision‐making. It involves collecting and analysing data to gain insights that can help guide decisions in a complex environment where intuition does not lead to good decisions.
John Deere has emerged as a shining example of an AI achiever, successfully integrating AI into its product portfolio. As a prominent and longstanding enterprise, the company's journey serves as a north star for others on the path to AI maturity. Established in 1837, John Deere holds a substantial revenue of approximately $52.57 billion in 2022 and employs a global workforce of about 75,000 individuals (Global Data 2022).
The path to AI maturity was not instantaneous for John Deere. They embarked on a progressive journey characterised by the first stage of data‐driven decision‐making. In the early 2000s, John Deere started its journey by investing in sensor technology and harnessing its data. As part of their precision agriculture initiatives, the company deployed sensors in farm fields and equipment, with the aim of collecting data to enhance farmers’ yields. Additionally, John Deere made strategic investments in GPS technology, starting in the mid‐1990s and culminating in the acquisition of NavCom, a GPS technology firm, in the late 1990s (Mergr 1999). This GPS system facilitated the tracking of harvesting equipment and tractor movement, resulting in reduced labor costs and increased convenience for farmers. By combining GPS location data with their sensor‐equipped harvesting equipment, John Deere gained valuable insights about grain quality, crop and seed types, and soil conditions (Horwitz 2020). These insights contributed to internal product development efforts and advisory services provided to farmers. For example, John Deere offered services to optimise crop health and nutrition, leading to enhanced agricultural productivity (Horwitz 2020; Marr 2020). Over the following decade, John Deere continued its journey of experimentation and data‐driven decision‐making, gradually realising the transformative power of data‐based insights.
Similarly Coca‐Cola has been taking measured strides on the same journey. Since its inception in 1886, Coca‐Cola has become the undisputed leader in the beverage industry, with more than 200 brands and various beverages, serving millions of customers in over 200 countries. Each day, Coca‐Cola's customers drink more than 2.2 billion servings of their products, creating a treasure trove of data that can be used to optimise production and distribution, as well as analyse sales and customer feedback (The Coca‐Cola Company 2023a).
Though it took some time, Coca‐Cola's journey from launch to creating value eventually paid off. At the 2013 Transforming Data with Intelligence (TDWI) Business Intelligence (BI) Executive Summit, Justin Honaman, former vice president of customer intelligence at bottling operation Coca‐Cola Refreshments, recounted a decade‐long battle the company had begun waging in 2003 to tackle the obstacles associated with collecting data across the different databases and software systems used by the company's bottlers (Burns 2013). But it was less a technical challenge than an organisational one, as Honaman pointed out, one that was worth taking on, especially since it enabled data‐driven decision‐making through the quick generation of sales reports, information about production output management, and helped to proactively resupply retailers to avoid out‐of‐stock situations, a problem that can have a 10% revenue impact on fast‐moving consumer goods companies (Andersen Consulting 1996).
In 2014, Coca‐Cola conducted an analysis of sensor data collected from 60 vending machines to examine the transaction patterns associated with these machines. The results of this project were highly impactful, resulting in a significant reduction in instances of stock‐outs and a notable increase in sales within the areas where the pilot was conducted. Specifically, transactions saw a remarkable increase of 15%, and the need for restocking visits was reduced by an impressive 18%. The success of this pilot project served as the foundation for the establishment of the vending analytics platform HIVERY. This platform has since been deployed across the company's vending machines in multiple countries, including Australia, New Zealand, and the United States (Prime 2021).
As more and more businesses are entering their AI journey, Coca‐Cola's and John Deere's stories serve as inspiring tales of success and resilience as well as where to start – with a data‐driven mindset. According to Harvard Business Review's 2012 survey, that first milestone of the AI journey has been shown to create value for many organisations. Data‐driven enterprises have outperformed their competitors by up to 6% in profitability and 5% in productivity (McAfee and Brynjolfsson 2012).
Supporting Human Decision‐Making
As organisations are gaining the means to live up to the challenge of becoming data‐driven, they are also overcoming a major obstacle to the next step of their journey: the application of AI.
By 2013, John Deere had embarked on several ambitious big data projects, showcasing their commitment to effectively harnessing data. They have state‐of‐the‐art data and technology platforms that enable them to process data and deploy AI at scale. For instance, a vast amount of data is telematically streamed from thousands of connected machines from about 100 locations in 30 countries where the company operates. All this raw data from agricultural machines and sensors flows into their cloud‐based enterprise data lake, and from there, it is transferred to their data factory. Here, the data is stored, curated, and made accessible to analytics and AI algorithms to generate valuable insights (Marr 2020).
Through the integration of data and AI, John Deere has reached a stage where their AI‐driven products actively support human decision‐making. Their online platform, www.myjohndeere.com, provides highly personalised AI‐driven recommendations to farmers based on weather data, soil conditions gathered from field sensors, and information about the seeds being used (Meffert and Swaminathan 2017). By analysing these data points, John Deere assists farmers in maximising their crop yields. However, John Deere's AI‐driven approach extends beyond yield optimisation. Through continuous equipment monitoring using sensors installed in vehicles, John Deere helps farmers reduce repair costs and extend the life span of their machinery through timely maintenance. The collected data is sent to a central data center, where AI algorithms convert it into valuable insights about their equipment maintenance requirements that farmers can access conveniently through the platform or the Farm Manager app on their smartphones or tablets (Horwitz 2020). Through its adoption of data and AI‐based services, John Deere, despite being a traditional manufacturing company, has evolved to a stage where it actively supports human decision‐making. This transformation aligns with the viewpoint expressed by Jeff Immelt, the former CEO of GE, who stated that every industrial company today is a software and analytics company (Rose 2015).
Coca‐Cola's next step in their journey was to leverage AI to support human decision‐making through insights from their vast amounts of data, especially social data. In 2017, Greg Chambers, global director of digital innovation at Coca‐Cola, declared, ‘AI is the foundation for everything we do. We create intelligent experiences. AI is the kernel that powers that experience’ (Brandon 2017). For example, the company used AI to gain customer targeting insights. With more than 105 million Facebook followers and 3.3 million followers on X (formerly Twitter), Coca‐Cola has a substantial fan base (Twitter 2023). By using image recognition to track when customers post images with the Coca‐Cola logo across these social media channels, the company can gain an understanding of the surrounding context. AI can recognise people along with Coke in posted images, and also enable Coca‐Cola to track the brands of its competitors. This insight helps Coca‐Cola better understand its target group and support human decision‐making in reaching those individual customers (Marr 2017a).
As most AI achievers today are in their journey stage in leveraging AI for supporting human decision‐making, recent research shows that AI's impact can be up to 30% of an organisation's revenue (Vohra et al. 2019), or 20% of earnings before interest and taxes (Chui et al. 2021), and 42% of organisations report that the ROI of their AI initiatives exceeded their expectations (Vohra et al. 2019).
Exceeding Human Decision‐Making Capabilities
The last milestone and finish line of the journey of our two AI achievers is an outcome of acknowledging human limitations and embracing AI's potential to exceed human capabilities.
Thanks to their early investments in data, coupled with a decade of experience in adopting relevant technologies such as GPS, John Deere has been able to take bold steps towards leveraging AI to surpass human capabilities, particularly in the realm of intelligent agricultural machinery and robotics.
In 2017, the company acquired Blue River Technology, a robotics start‐up based in California, followed by the acquisition of Bear Flag Robotics in 2021, both of which contribute to John Deere's AI strategy (Colodony 2017; Vincent 2017). These acquisitions have integrated unique AI capabilities into John Deere's AI initiatives, particularly in the development of autonomous‐driving technology. Merely a year later, John Deere proudly showcased their state‐of‐the‐art AI technology at the esteemed 2022 CES (Consumer Electronics Summit) in the US. The spotlight was on their flagship AI product, the autonomous and self‐driving tractor. This advanced machine is equipped with six pairs of cameras that possess the capability to detect obstacles in all directions and calculate distances. Images captured by these cameras are swiftly processed through AI algorithms, enabling pixel classification in a mere 100 milliseconds, thereby facilitating the tractor's movement (John Deere 2022a). The autonomous tractor continuously verifies its position relative to a geofence, ensuring precise operation within an inch of accuracy. Remarkably, farmers can control the tractor conveniently using their mobile phones.
Their unwavering dedication to embracing innovation, coupled with the utilisation of data and AI, has resulted in substantial value creation for their customers. This accomplishment has been recognised and appreciated not only by their customers but also by investors, resulting in a remarkable stock price premium in comparison to their competitors. From 2020 to 2023, John Deere's stock witnessed an impressive appreciation of about 200% (Google Finance 2023).
In a similar fashion as John Deere, Coca‐Cola made their next step by leveraging AI to overcome human limitations. In order to improve sales, the Coca‐Cola Company is taking their vending machines to the next level by incorporating AI's automated decision‐making, creating intelligent vending machines that can adjust their ‘mood’ to fit in any setting. For example, a vending machine placed in a gym would focus more on selling water and energy drinks than sugary beverages. These intelligent machines use customer data to further customise the products they advertise in a given area, while offering exclusive discounts and deals for the targeted audience (Chaturvedi 2021). But not just vending machines will become smarter with AI's capabilities. Even coolers will become smarter and take over some of the sales representative's tasks. Coolers will provide real‐time visibility and control to continuously manage stock levels to avoid out‐of‐stock situations. Furthermore, they can alert on price changes, provide information on competitor products and maintenance, as well as offer insight into customer traffic and purchase behaviour. This can help to identify intraday consumption patterns, giving businesses such as Coca‐Cola a competitive edge (Trax 2016).
Moreover, as one of the first organisations to appoint a head of generative AI, the company just recently appointed their head of generative AI and professionally applied this new technology to their advertising. They created an AI‐generated campaign that was able to show how AI, combined with human creativity and skills, can create something very new that is able to ‘bring the wow factor’ (Marr 2023
