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- Herausgeber: John Wiley & Sons
- Kategorie: Fachliteratur
- Sprache: Englisch
Leadership wisdom to ride the wave of the seven hottest trends in our new AI-first world of business
Written by seasoned tech executive Sandy Carter, AI First, Human Always: Embracing a New Mindset for the Era of Superintelligence is your guidebook to the seven hottest trends in AI. This book will recalibrate your approach to the exponential curve of emerging AI solutions for business. It will help you transform today's unstoppable currents of change into tailwinds that propel your organization to great heights. From the tokenization of everything, to multi-model learning models, to the importance of technical convergence and the implementation of digital twins across almost every industry imaginable, this book provides an essential core knowledge base as well as examples and case studies to help you transform your approach to leadership to meet the demands of the modern business era.
Throughout the book, Carter drives home the essential coexistence of technology, emotion, intelligence, creativity, intuition, and ethics to enhance, rather than replace, the human experience. Some of the topics that Carter explores include:
- How to foster an AI-driven culture with experimentation
- Strategies for applying first principles before starting an AI project
- Ways to balance human and AI collaboration
AI First, Human Always: Embracing a New Mindset for the Era of Superintelligence is a timely, essential read for all business leaders and professionals aiming to prepare for AI's broader implications on society and the economy. Grab your copy today and stay one step ahead of the competition in a digital world evolving at breakneck speed.
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Seitenzahl: 380
Veröffentlichungsjahr: 2025
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Table of Contents
Cover
Table of Contents
Title Page
Copyright
Foreword
CHAPTER 1: Embracing the AI‐First Era
The AI‐First Mindset: A New Paradigm
What It Means to Put AI‐First
Examples of AI‐First Success
What AI‐First Does Not Look Like
Timing: Navigating the AI Adoption Curve
Do You Need a Chief AI Officer?
The New C‐Suite
Preparing for Disruption
The Human Element: Balancing AI with Empathy
Embracing the AI‐First Future
Notes
CHAPTER 2: Exponential Baby!
Getting Started with Some Definitions
The Exponential Rise of AI
Convergence and Transformation
Data Explosion: The Lifeblood of the AI Revolution
Navigating the Impact on the Workforce
Balancing Automation and Human Creativity
Leading Through Exponential Change
The Inevitability of Exponential Growth
Embracing the Exponential Baby Era
Notes
CHAPTER 3: The Rise of Multimodal Learning Models
Revolutionary Impact
The Game‐Changing Nature of Multimodal Learning
The Five Superpowers of Multimodal Learning Models
Impact of Multimodal Learning Superpowers on Brands
Already Moving Forward: Real‐World Examples of Multimodal Learning
Why Multimodal Learning Is the Next Big Movement
Multimodal Learning and the Flywheel Effect
Actions for Technologists and Business Leaders
The Future of AI Is Multimodal
Notes
CHAPTER 4: The Experiential Age Unfolds
What Is the Experiential Age?
How the Experiential Age Is Different
Business Implications and Strategic Shifts
Why Is the Experiential Age Important?
The Internet of Senses in the Experiential Age
Strategic Actions for Embracing the Experiential Age
AI‐First Experiential Strategy Framework: PULSE
Notes
CHAPTER 5: Everything Is Being Digitally Twinned
What Is a Digital Twin?
Key Characteristics of Digital Twins
How Is a Digital Twin Different from Business as Usual?
The Rise of Digital Twins in Banking and Finance
Why Digital Twinning Is Important
Case Studies and Examples of Digital Twins
Strategic Actions for Embracing Digital Twins
Notes
CHAPTER 6: Tokenization of Everything
What Is Tokenization?
Key Characteristics of Tokenization
Why Tokenization Is Important
Case Studies and Examples of Tokenization
The Future of Tokenization and AI
Strategic Actions for Brands Considering Tokenization
Leadership Actions for Embracing Tokenization and AI
Notes
CHAPTER 7: The Convergence Concept
Why Does Convergence Matter?
How Convergence Is Affecting Our World Today
Brands and Leaders Need to Pay Attention
Leaders as AI‐First Thinkers in the Age of Technological Convergence
Notes
CHAPTER 8: Challenges Brought on by AI
The Current Trust Deficit in AI
Hallucinations in AI Output
Lack of Data
Fear of Job Elimination
Environmental Issues: The Risk for Sustainability
Copyright Concerns
Enhanced Responsible AI Framework
Notes
CHAPTER 9: How to Become an AI‐First Leader
What Does It Mean to Be an AI‐First Leader?
Characteristics of an AI‐First Leader
Your Team Is Your Greatest Asset: Developing AI Competencies
Fostering an AI‐Driven Culture
Balancing Human and AI Collaboration
Building Resilience Through AI
A Bold Call to Action: Revolutionize Your Leadership with AI
Notes
CHAPTER 10: The Future Horizon: AI's Transformative Path
Beyond Exponential Change
The Convergence Revolution: When It All Comes Together
Future Trends in AI Leadership
Top AI Futurist Predictions: What Business Leaders Need to Know
AI and Data Privacy: The New Frontier
AI‐First Leadership: Embracing the AI‐Driven Future
Forging the Future: The AI‐First Imperative
Notes
APPENDIX A: The AI Marketecture: A Practical Guide for Leaders
APPENDIX B: First Principles Thinking and Navigating Rapid Change
Acknowledgments
About the Author
Index
End User License Agreement
List of Illustrations
Chapter 1
FIGURE 1.1 Key roles added to support generative AI.
Chapter 3
FIGURE 3.1 The multimodal data flywheel.
Chapter 4
FIGURE 4.1 The experience–value matrix.
FIGURE 4.2 The PULSE framework.
Chapter 8
FIGURE 8.1 The responsible AI framework.
APPENDIX A
FIGURE A.1 The AI marketecture.
APPENDIX B
FIGURE B.1 Return on transformation.
Guide
Cover
Table of Contents
Title Page
Copyright
Foreword
Begin Reading
APPENDIX A: The AI Marketecture
APPENDIX B: First Principles Thinking and Navigating Rapid Change
Acknowledgments
About the Author
Index
End User License Agreement
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AI FIRST, HUMAN ALWAYS
EMBRACING A NEW MINDSET FOR THE ERA OF SUPERINTELLIGENCE
SANDY CARTER
Copyright © 2025 by John Wiley & Sons, Inc. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies.
Published by John Wiley & Sons, Inc., Hoboken, New Jersey.
Published simultaneously in Canada.
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Library of Congress Cataloging‐in‐Publication Data
Names: Carter, Sandy, 1963‐ author.
Title: The mind‐machine merge : embracing an AI‐first mindset for a limitless future / Sandy Carter.
Description: Hoboken, New Jersey : Wiley, [2025] | Includes index.
Identifiers: LCCN 2024045075 (print) | LCCN 2024045076 (ebook) | ISBN 9781394189823 (hardback) | ISBN 9781394189816 (adobe pdf) | ISBN 9781394189809 (epub)
Subjects: LCSH: Artificial intelligence—Industrial applications. | Artificial intelligence.
Classification: LCC HD45 .C373 2025 (print) | LCC HD45 (ebook) | DDC 658/.0563—dc23/eng/20241118
LC record available at https://lccn.loc.gov/2024045075
LC ebook record available at https://lccn.loc.gov/2024045076
Cover Design: Wiley
Author Photo: © Unstoppable Domains
To my loving family, Todd, Kassie, Maria, and my mom and dad, who bought me that robot! They inspire me every day!
and
To all the amazing women in AI, thanks for your untiring impact!
and
To all of you who are exploring the future as an AI‐first business leader!
Foreword
A few years ago, I had the honor of interviewing Walter Isaacson, the great American historian and author of books on geniuses such as Albert Einstein, Benjamin Franklin, and Steve Jobs. When I spoke to him, he had just finished a triumphant 800‐page book on Leonardo da Vinci, perhaps the most creative man who ever lived.
The obvious question for a man who has spent much of his life studying geniuses was, “What common characteristics make a person a genius?”
He had a ready answer: “Insatiable curiosity and an ability to connect the dots in a new way.”
These are also the traits that come to mind when considering Sandy Carter.
If you look at the history of digital technology, Sandy's curiosity has always kept her on the cutting edge, from the earliest websites to the pioneering days of AI with IBM's Watson, from e‐commerce to Web3. Naturally, she has immersed herself in the world of artificial intelligence (AI). Where else would she be?
Sandy's incredible career and global connections help her uniquely connect the dots, as you'll see in the inspiring case studies in this book, which teach us to consider AI as our new home base.
However, there is also something that sets Sandy apart from other contemporary thought leaders. She exudes a caring humanity that makes her as accessible, kind, and patient as your favorite third‐grade teacher. That is exactly who we need to help us navigate this hurricane‐force world of AI in a way that we can trust and understand.
In short, she is the perfect person to write a book on an AI‐first approach to leadership, and we are lucky to have this gift. Her work is not merely theoretical; it is grounded in real‐world applications and hard‐won insights from the frontlines of technological innovation.
The concept of an AI‐first philosophy that Sandy champions isn't just a catchy phrase—it represents a fundamental reimagining of how businesses operate, innovate, and compete. This philosophy recognizes that AI is not just another technological tool to be adopted piecemeal, but a transformative force that should inform every aspect of an organization's strategy and operations.
Sandy's human touch shines through as she tackles the multifaceted challenges of AI integration. From the technical aspects of implementation to the equally crucial dimensions of change management, ethical considerations, and fostering an AI‐ready culture, this book provides a comprehensive road map for organizations of all sizes and across all sectors.
This is not a book of AI‐prompt magic tricks. Sandy has delivered a blueprint for success in the age of AI. I encourage you to approach it with an open mind and a willingness to embrace her bold thinking. Through Sandy's lens, we don't merely glimpse the potential of an AI‐driven world—we gain the tools and mindset to actively create it, responsibly and brilliantly.
Mark Schaefer
Marketing futurist and author
CHAPTER 1Embracing the AI‐First Era
In today's rapidly evolving technological landscape, we've seen buzzwords like cloud first and mobile first dominate the conversation over the past decade. These first focuses were all about the technology.
But today, let me introduce you to the new mantra that's transforming the way we think about business: AI‐first. Why is this shift so crucial? Artificial intelligence (AI) isn't just about technology; it's a transformative force that's changing our lives at an incredible pace, redefining how we connect, work, and experience the world around us. I've seen firsthand how AI can improve lives, from simplifying daily tasks to providing insights that were once unimaginable. For instance, using AI to analyze health data has given my family critical insights that helped manage a chronic illness more effectively. It's personal because I've experienced the positive impacts and believe in its potential to create a better future for everyone.
The AI‐First Mindset: A New Paradigm
Think about a future where AI is deeply embedded in our daily lives. Imagine a humanoid robot arriving at your doorstep to deliver a pizza. But this is not just any delivery service—it's an AI‐driven experience. The robot doesn't just hand you the pizza and leave. It walks into your home, sets the table, pours the drinks, and serves the pizza. When the meal is over, it even cleans up, selecting the right dish soap and doing the dishes for you.
This might sound like science fiction, but it's a near‐future reality. The implications for brands are profound. If a robot is serving your drinks, do you need to market to the company that makes the robot? Does the AI decide which product to use based on pre‐programmed preferences? How do companies like Coca‐Cola or Pepsi adjust their strategies when the point of consumer interaction shifts from human to robot? These are the questions that AI‐first leaders must ponder.
Just yesterday, I was at the dentist, and instead of the usual manual examination, they used an AI‐powered tool for tooth recognition. This device scanned my teeth and identified potential issues with pinpoint accuracy. Interestingly, it wasn't the dentist who operated the AI tool but the dental hygienist. When I asked her how she felt about it, she said, “I was so intrigued that I took two classes on AI to understand how to use it better.” This shows how AI is not just replacing roles but transforming them, requiring new skills and training.
Consider another innovation—mood jackets. Remember mood rings from the past? These jackets work on a similar principle but are far more advanced. They sense your emotions and physical states, such as when you're hot, cold, or sad. In factories in Asia, they are being tested to determine the best break schedules for workers based on their physiological data. This raises important questions for managers: How do you manage change when implementing such personal data‐driven technology? How do you explain to employees that their jackets are monitoring their moods and breaks?
These scenarios aren't some distant future—they're happening now. AI is already making breakthroughs, like in the mining industry, where an AI system discovered a significant copper deposit in Kenya.
A forward‐thinking exploration company used AI to analyze massive geological datasets. Traditionally, mineral exploration is labor‐intensive and slow, but AI changed the game. The system processed satellite images, geological surveys, and historical mining data, identifying a high‐potential region in Kenya. When the company verified the findings on the ground, they uncovered a major copper deposit. This breakthrough is set to boost Kenya's economy and create jobs, proving AI's transformative power in resource exploration.
This isn't just about technology; it's about revolutionizing industries. AI enhances precision, accelerates discoveries, and minimizes environmental impact. The discovery in Kenya showcases AI's potential to solve complex problems and open new opportunities.
The message is clear: we can't resist AI. It's time to learn and adapt. AI‐first isn't just a strategy—it's a fundamental shift in business and technology thinking.
What It Means to Put AI‐First
So, what does it mean to put AI‐first at the heart of your business strategy?
Adopting an AI‐first approach means deeply integrating AI into the fabric of your organization's decision‐making processes, operations, and customer interactions. It's about leveraging AI as a fundamental driver of innovation, efficiency, and competitive differentiation. “Innovation in the future means applying a layer of AI to everything you do,” commented Kevin Kelly, cofounder and senior maverick at Wired magazine.1 He discusses this topic in his Ted Talk about the pervasive impact of AI across various aspects of our lives and industries.
But how do you go about doing this?
When you adopt an AI‐first strategy, AI becomes the engine of innovation within your organization. This could mean using AI to develop new products or services that were previously unimaginable. For instance, consider Netflix. They use AI to personalize content recommendations, thereby enhancing user experience and keeping customers engaged. Their AI algorithms analyze vast amounts of viewing data to predict what you might want to watch next, ensuring a personalized and satisfying user experience. This level of innovation was made possible by embedding AI into their core business strategy.
This means that one principle is that AI, combined with humans, should be a driver of innovation.
AI‐first also means using AI to streamline and optimize your operations. Take Amazon as an example. They use AI‐driven robots in their fulfillment centers to automate the sorting and packing of products. This integration of AI not only speeds up the delivery process but also reduces operational costs, allowing Amazon to maintain its competitive edge in the e‐commerce market. By leveraging AI, Amazon continuously improves its operational efficiency, setting a high bar for logistics and supply chain management.
Customer service is another area where an AI‐first approach can transform business operations. Traditional chatbots often provide scripted responses that can frustrate customers more than they help. However, AI‐powered systems, such as those used by Apple's Siri or Google Assistant, engage in more sophisticated interactions. They learn from each interaction to provide more accurate and personalized responses. This capability goes beyond mere automation, creating new opportunities for meaningful and engaging customer experiences.
Embracing an AI‐first strategy means more than just adopting new technologies; it's about fundamentally rethinking how your organization operates and competes. It involves a strategic shift toward leveraging AI as a driver of innovation, efficiency, and customer engagement.
But not the only driver. Humans are critically important. We will still want handcrafted items from the farmer's market. The goal is to ensure that AI becomes a key component of your business strategy with your teams. Humans bring unique characteristics that will always be part of the narrative of a company's success.
As we navigate the AI‐first era, the most successful organizations will be those that can integrate AI into the crucial facets of their operations while balancing it with the invaluable contributions of human insight and creativity. By doing so, they will not only stay competitive but also lead the way into the future of business and technology.
Examples of AI‐First Success
Organizations that excel with AI‐first strategies tend to share common traits: they will integrate AI deeply into their operations, enabling it to drive innovation and efficiency across multiple facets of their business. (I say will as most companies today have not matured enough to integrate AI deeply into operations.) They will prioritize AI as a key component of their decision‐making processes, enabling them to anticipate and respond to market demands more swiftly and accurately. (See Appendix A for a simple AI marketecture.)
Moreover, these organizations see AI not merely as a set of tools but as a transformative force that fundamentally reshapes how they deliver value to their customers. By fostering a culture that embraces continuous learning and experimentation with AI, these leaders position themselves to not only keep pace with technological advancements but to also set the pace in their respective industries.
Healthcare:
In the healthcare sector, SimBioSys developed TumorScope, a simulation engine that uses diagnostic data to create virtual replicas of individual tumors. It predicts responses to therapy, assisting physicians in personalized treatment planning.
Retail:
Retailers like Zara use AI to optimize their inventory management and predict fashion trends. Their AI systems analyze customer behavior, social media trends, and sales data to forecast demand for different products, ensuring that they stock the right items at the right time. This AI‐driven approach helps Zara stay ahead in the fast‐paced world of fashion retail.
Financial services:
In financial services, companies like JP Morgan Chase use AI for fraud detection and risk management. Their AI systems analyze transaction data in real‐time to identify unusual patterns that might indicate fraudulent activity. By leveraging AI, JP Morgan enhances its ability to protect customer assets and maintain trust.
These examples illustrate the transformative potential of AI when it is placed at the heart of a company's strategy. (Note: We will go deeper into these examples as well later in the book ‐ these were to get your interested in the stories too!) Whether in health care, retail, or financial services, AI‐first organizations are redefining their industries and setting new standards for innovation and customer engagement.
What AI‐First Does Not Look Like
As we venture into the AI‐first world, it's just as crucial to understand what this strategy is not. Missteps in adopting AI can be as instructive as success stories, providing a clear picture of pitfalls to avoid.
Being AI‐first is more than just integrating AI tools or launching AI projects; it's about embedding AI into the fabric of your organization to drive meaningful change. Meaningful change occurs over time. The best AI projects start small and then grow with success! It's not merely about keeping up with trends or experimenting with AI in isolated pockets. Instead, it's about making AI a core component of your operational and strategic vision, ensuring it complements and enhances human capabilities rather than sidelining them.
Let's explore a few examples of what AI‐first does not look like and how these common pitfalls can hinder the true potential of an AI‐driven approach.
Superficial AI adoption:
Simply adopting AI for the sake of following a trend does not constitute an AI‐first strategy. For instance, installing chatbots on your website without a clear plan for how they will enhance customer experience or integrate with other systems is a superficial approach. AI‐first is not about having AI for the sake of AI; it's about making it a central part of how your business operates and delivers value.
Isolated AI projects:
AI should not be limited to isolated projects within your organization. If AI initiatives are confined to one department or a single‐use case, your organization is missing out on the broader benefits of an AI‐first strategy. AI should permeate various aspects of the business, from supply chain and logistics to customer service and product development. Companies that keep AI confined to the lab rather than integrating it across business units will struggle to see the full transformative potential of AI.
Ignoring the human element:
An AI‐first strategy that overlooks the importance of human intuition, creativity, and empathy is incomplete. AI is a powerful tool, but it works best when combined with human insight. Organizations must focus on how AI can augment human capabilities rather than replace them. For instance, in health care, AI can analyze medical images faster than humans, but the final diagnosis and treatment plan should still involve human judgment and patient interaction.
Lack of Trust:
AI First does not move forward without the trust of those using the tool. In the latest Stanford University study, ChatGPT demonstrated an impressive 92% accuracy in medical diagnoses, yet integrating it into doctors’ workflows didn’t significantly improve diagnostic accuracy. The issue wasn’t AI’s capability but rather a trust gap, as many physicians hesitated to fully rely on the tool. Additionally, the study highlighted that insufficient training in AI collaboration left this powerful tool underutilized, limiting its potential impact. For AI to truly transform industries, we must focus on building trust, providing comprehensive training, and fostering seamless integration between humans and machines to unlock its full potential.
These points illustrate common missteps that can derail an AI‐first strategy. Avoiding these pitfalls is essential for leveraging AI's full potential and driving significant, sustained value in your organization.
Timing: Navigating the AI Adoption Curve
In today's fast‐paced digital landscape, timing is everything when it comes to adopting new technologies, especially AI. According to recent data from McKinsey, 65% of respondents report that their organizations are regularly using generative AI in their business operations.2
In the IBM Global AI Adoption Index by IBM, 42% of companies are actively exploring how to integrate AI into their processes, and over 50% plan to incorporate AI technologies. This means that a staggering 77% of companies are either using or considering the use of AI.3 And different industries are progressing at different rates. Per IBM research, 63% of Energy CEOs surveyed expect to realize value from generative AI and automation.4
The takeaway? You and your company need to figure out the first project to take in the AI world. Make that small project successful, and then move forward. Move at the right pace for your business to stay competitive. The top barriers preventing deployment include limited AI skills and expertise (33%), too much data complexity (25%), and ethical concerns (23%).5
Given this momentum, businesses should not hesitate to begin their AI journey with the right project. The rapid acceleration of AI adoption signals that those who delay might find themselves playing catch‐up in a landscape that is increasingly driven by data and machine learning. However, this does not mean rushing in blindly.
Instead, organizations should adopt a strategic approach, starting with small, manageable AI projects that align with their core business goals. These early initiatives can serve as proof of concepts, helping to build internal support and demonstrate the tangible benefits of AI. As these projects gain traction, businesses can scale their AI efforts more broadly across the organization.
Speed is important, but so is prudence. Moving too quickly without a solid strategy can lead to wasted resources and failed projects. It's essential to strike a balance between urgency and thoughtful planning. Leaders should prioritize building a strong foundation, which includes investing in AI education and training for their workforce, establishing robust data management practices, and creating a flexible technology infrastructure that can support future AI initiatives.
AI‐first leaders also need to think through causal AI. It's not just a buzzword. Causal AI is about evolving your AI strategy beyond predictive models to systems that truly understand cause and effect. Start by educating yourself and your team on the principles of causal inference. This isn't just about upgrading your tech stack—it's about shifting your entire approach to problem‐solving. Causal AI enables you to ask why questions and explore interventions in ways traditional AI can't. It's the difference between knowing a correlation exists and understanding how to influence outcomes.
To implement this shift, begin by identifying key business challenges where understanding causal relationships could drive significant value. This might be in areas like customer behavior, operational efficiency, or product development. Start experimenting with causal models alongside your existing AI systems, looking for opportunities to enhance decision‐making by incorporating causal insights.
Foster a culture of causal thinking across your organization, encouraging teams to move beyond what questions to why and how questions when analyzing data and making decisions. Invest in tools and talent that can bridge the gap between data science and domain expertise because causal AI requires a deep understanding of both the technical aspects and the business context. Be patient and iterative—implementing causal AI is a journey, not a quick fix. Start with small, high‐impact projects and scale as you learn.
For example, in marketing, causal AI can help determine the optimal mix of tactics, spending, and offers to maximize return on investment. For customer retention, it can pinpoint the root causes of churn in specific markets, allowing for targeted interventions. In manufacturing, causal AI can uncover the complex relationships between inventory management and product failures, leading to more efficient processes. Financial institutions can use it to model how various Federal Reserve rate cut strategies might affect market indices. Even in human resources, causal AI can reveal how different staffing models affect revenue across retail locations. These examples showcase the versatility and power of causal thinking in driving strategic decision‐making across diverse business functions.
By taking these steps, organizations can accelerate their AI adoption while minimizing risks, ensuring they are well positioned to harness the transformative power of AI in the years ahead.
Do You Need a Chief AI Officer?
The question of whether you need a chief AI officer (CAIO) depends largely on your organization's size, complexity, and AI maturity. In many leading companies, a CAIO or equivalent role is crucial for orchestrating AI initiatives across the enterprise. This role focuses on ensuring that AI is aligned with the company's strategic objectives, overseeing AI talent development, and fostering a culture that embraces AI‐driven innovation.
However, having a CAIO is not the only way to achieve an AI‐first strategy. Smaller organizations or those in the early stages of AI adoption might find it more practical to embed AI responsibilities within existing leadership roles or create cross‐functional AI teams. The key is to ensure that AI is not siloed but integrated across all business functions.
Despite all the buzz about AI transforming the world, its influence in the boardroom has been limited until recently. Not long ago, the idea of appointing a chief metaverse officer or CAIO would have seemed absurd, yet the CAIO role is now rapidly becoming one of the most sought‐after positions in the C‐suite.
Did you see that Genius Group Limited “appointed” an avatar as their CAIO? This intriguing move raises an interesting question: what is the role of AI in corporate leadership? This decision by Genius Group blurs the traditional lines between human and machine roles in an organization, raising questions about decision‐making processes, ethics, and the human touch in leadership.
Although some might view the appointment of a virtual AI officer as a publicity stunt, it underscores a deeper integration of AI into strategic roles. Companies need to discern between leveraging AI for genuine operational enhancement versus using it as a marketing gimmick. As AI takes on more responsible positions, companies must navigate regulatory requirements and ethical considerations. There is a need for clear guidelines on accountability, especially when AI‐made decisions could have significant consequences.
This move by Genius Group highlights the necessity for organizations to balance the innovative potential of AI with practical and ethical implications. Whether this step proves to be a pioneering success or a cautionary tale, it certainly sets the stage for a broader discussion on the evolving role of AI in leadership.
These new C‐level roles, which didn't exist just a few years ago, are being filled not only by cutting‐edge start‐ups but also by more established enterprises. According to Foundry's AI Priorities Study 2023, 11% of midsize to large organizations have already appointed a CAIO, and an additional 21% are actively looking to fill this position (see Figure 1.1).6
Merely appointing a new C‐suite member doesn't guarantee that you'll address the challenges or seize the opportunities presented by AI. The tougher (but ultimately more rewarding) task is to understand how these roles fit into your organization's core strategy, what their work involves, and how new technologies transform the dynamics and responsibilities within the boardroom. This might require a new, dedicated CxO (chief x officer), or it could be more effective to adapt an existing role. Instead of hastily filling a new boardroom position, companies should pause and carefully determine their objectives and the best person to achieve them.
FIGURE 1.1 Key roles added to support generative AI.
Source: Foundry.
At first glance, creating new “chief” positions seems logical. Who better to lead and oversee AI transformations than someone with a dedicated place at the top?
But consider this: if your organization is an insurance firm, deeply affected by AI, what value is there in appointing a data scientist to the C‐suite if they don't understand the difference between an underwriter and a broker?
The key takeaway is don't be blinded by those who only understand AI. Remember, you know your business best, and that's what matters most.
There's a strong case that the best person to manage AI initiatives might already be in the boardroom. For instance, it could be the chief marketing officer (CMO) if AI is expected to have a significant, transformative impact there. Similarly, it might be the chief technology officer (CTO), given their comprehensive understanding of both tech and business priorities. It could even be the CEO.
Recent history shows us that new job titles are often integrated into other, more traditional C‐suite roles. Consider the chief cloud officer role, essential during the transition to cloud computing but eventually becoming part of the CTO's or chief information officer's responsibilities. The same could happen with the CAIO and CMO, despite efforts like the CAIO Summit to make them indispensable.
However, having a dedicated CxO might still be the best option. Lan Guan, CAIO at Accenture, a leading multinational IT company with over 730,000 employees, supports this. She believes every business should have a CAIO. “I believe this is a technology for everyone,” she said. “But 90% of the clients out there are still grappling with this technology.”7 Internally, Guan said she's helping Accenture's workforce develop AI skills and attract talent that is already well versed in the technology.
No matter how advanced, technology is always a tool best applied to clearly defined and well‐understood problems. It serves wider organizational goals rather than becoming the master. That's why the best individuals to oversee AI and initiatives might already be in charge.
The New C‐Suite
I know from personal experience just how impactful an effective CAIO can be in transforming a business. When Accenture recently introduced its new CAIO, it brought a wave of innovation and efficiency that reshaped their strategies and operations. The integration of AI at such a high level in the organization demonstrated the profound potential of AI to drive growth and adapt to rapidly changing environments.
However, true success hinges not just on the role itself, but on how well enterprises grasp the shifts in traditional boardroom dynamics that these roles bring about. Because new technologies have an almost limitless range of applications affecting numerous business operations, the person leading AI initiatives needs to be a true Renaissance individual. They must build teams with diverse technical and vertical skills and adeptly navigate the intricate organizational politics that come with a broad scope of responsibilities. For instance, will this individual oversee both internal investments and external partnerships? How much authority will they have over budget allocation and expenditures? Who will report to them, and to whom will they report? How will this alter the existing chain of command?
These are just some of the critical questions to ponder when integrating new technology into existing corporate structures. They are also vital considerations when deciding who should be in charge.
In the wake of executive order on AI, federal agencies are now required to appoint CAIOs to promote AI and manage its risks and rewards.8 This trend is likely to spread to the corporate world, but the role's responsibilities in business will be far more diverse and challenging than in government.
Cathy Hackl, often referred to as the “godmother of the spatial computing,” vividly illustrates the complexity of these emerging roles. “Every day is different, and you never know what challenges are going to land in your inbox,” she explains. “The only thing that never really changes is that my role requires me to be an evangelist and educator. I spend a lot of my time traveling, presenting, and speaking—both at industry events and internally at my company.”9
She further elaborates on the breadth of the role: “I work across practically every line of business, so it's a big challenge to be thoroughly on top of the detail about each department. I need to ensure I understand what their goals are, and they need to know how new technology can help accomplish them. But there's also a lot of roll‐your‐sleeves‐up, down‐and‐dirty work with the technology itself, whether it's designing virtual components or shepherding implementation projects.”10
However, the challenges extend beyond technical expertise. Ethical considerations are paramount, particularly in ensuring AI integration is transparent, honest, and free from unintended biases. As the recipient of the Women Leaders in Data and AI's Executive Champion Award, I'm acutely aware of the importance of ethics in this field.11
The role must balance driving innovation with predicting and mitigating unforeseen risks, managing change, and safeguarding against discrimination. This complexity suggests that organizations might need to consider roles like chief ethics officers alongside their AI leadership.
As we navigate this technological revolution, it's crucial not to rush into creating new positions without careful consideration. The key is to focus on what you want to achieve rather than hastily naming roles. Take the time to think strategically about how AI and related technologies can best serve your organization's goals and values.
Preparing for Disruption
The rapid advancement of AI means that disruption is not a question of if, but when. Businesses must be agile and forward thinking to navigate these changes. Change management becomes essential, not just in adopting new technologies but in helping people adapt to them.
A new report from YouGov and the Reuters Institute for the Study of Journalism reveals that only 7% of people in the United States use generative AI daily.12 Interestingly, most people have only used AI once or twice. This finding sparked some thoughts and questions for me. Although AI's potential is vast, its current use highlights the gap between early adopters and the general population.
This gap is why I’ve started a project called AI for Everyone to ensure all have access to AI’s power. I recently ran workshops for Vets, Dry Cleaners, and Dentists to educate them on AI’s value. More is needed in this space.
As AI continues to evolve, it's crucial to recognize and address the concerns of those who fear that their jobs might be at risk. The goal is to balance the incredible potential of AI with the irreplaceable value of human creativity and empathy. In the AI‐first era, leaders need to be both visionaries and pragmatists. They must create environments where AI can thrive and where teams are empowered to leverage its capabilities. This means fostering a culture of continuous learning and innovation, where experimentation is encouraged, and failure is seen as a stepping stone to success.
The Human Element: Balancing AI with Empathy
Although AI offers remarkable capabilities, it's crucial to balance these with the irreplaceable value of human intuition and creativity. AI can process and analyze data at scales and speeds beyond human capability, but it lacks the understanding and emotional intelligence that humans bring to the table.
In an AI‐first world, the role of humans is to interpret AI‐driven insights, make complex decisions, and build relationships. For example, in customer service, AI can handle routine inquiries and provide recommendations, but human agents are needed to resolve complex issues and empathize with customers.
Leaders must recognize that AI is a complement to human abilities, not a replacement. They should focus on fostering a collaborative environment where AI and human expertise work together to achieve greater outcomes. This involves training employees to understand and effectively use AI tools while emphasizing the importance of their unique skills and judgment.
Embracing the AI‐First Future
Embracing an AI‐first philosophy is about more than just adopting new technologies—it's about transforming your mindset and approach to business. It involves prioritizing AI in your strategy, integrating it into your operations, and preparing for the disruptions it will bring. By doing so, businesses can not only stay competitive in an AI‐driven world but also unlock new opportunities for growth and innovation.
Success in the AI‐First era demands more than adopting technology—it requires visionary leadership, strategic integration, and a balance between innovation and human creativity. To begin this journey, focus on three core principles:
Start Small, Scale Strategically:
Launch pilot projects that showcase AI’s potential while embedding ethical practices and leveraging high‐quality data. Use these successes to integrate AI across departments for greater impact.
Empower Teams with Skills and Collaboration:
Train your workforce to partner with AI, fostering a culture of creativity, continuous learning, and ethical innovation. Human intuition combined with AI’s capabilities will define success.
Innovate and Adapt Continuously:
Regularly refine your AI initiatives, staying curious and proactive. Balance automation with empathy to ensure AI aligns with your organization’s values and drives meaningful change.
As we move forward, the key to success will be balancing the power of AI with the irreplaceable value of human intuition and creativity. Organizations that can effectively harness the potential of AI while maintaining their human touch will be best positioned to thrive in the future. Whether you are a leader, employee, or entrepreneur, now is the time to embrace AI‐first thinking and prepare for the exciting changes ahead.
What will your AI‐First move be? Start small, think big, and lead boldly.
Notes
1.
Kelly, K. (2016, December 13).“How AI can bring on a second industrial revolution.” TED Talk.
https://ted2sub.org/talks/kevin_kelly_how_ai_can_bring_on_a_second_industrial_revolution
.
2.
Singla, A., Sukharevsky, A., Yee, L., et al. (2024, May 30). “The state of AI in early 2024: Gen AI adoption spikes and starts to generate value.” McKinsey & Company.
https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai
.
3.
IBM. (2024, January 10). “IBM Global AI Adoption Index.”
https://newsroom.ibm.com/2024-01-10-Data-Suggests-Growth-in-Enterprise-Adoption-of-AI-is-Due-to-Widespread-Deployment-by-Early-Adopters
.
4.
Foundry. (2023). “AI Priorities Study 2023.”
https://foundryco.com/tools-for-marketers/research-ai-priorities
.
5.
IBM. (2024, January 10).
6.
Foundry. (2023).
7.
Sweeney, E. (2024, May 31). “Accenture has had a chief AI officer for about 9 months—here's her advice to other companies thinking about adding the role.”
Business Insider
.
https://www.businessinsider.com/chief-artificial-intelligence-officer-job-skills-required-2024-5#:~:text=%22I%20believe%20this%20is%20a,well%2Dversed%20in%20the%20technology
.
8.
Federal Register
. (2023, November 1). “Safe, secure, and trustworthy development and use of artificial intelligence.”
https://www.federalregister.gov/documents/2023/11/01/2023-24283/safe-secure-and-trustworthy-development-and-use-of-artificial-intelligence
.
9.
Carter, S. (2024, April 17). “The C‐suite's hottest new job—The chief AI officer.”
Forbes
.
https://www.forbes.com/sites/digital-assets/2024/04/17/the-c-suites-hottest-new-jobthe-chief-ai-officer
.
10.
Carter. (2024, April 17).
11.
ACCESSWIRE. (2023, November 10). “WLDA names unstoppable COO Sandy Carter ‘executive champion’ of the year.”
https://www.accesswire.com/802196/wlda-names-unstoppable-coo-sandy-carter-executive-champion-of-the-year
.
12.
The Decoder. (n.d.). “Few people use generative AI daily despite ChatGPT hype, study finds.”
https://the-decoder.com/few-people-use-generative-ai-daily-despite-chatgpt-hype-study-finds
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CHAPTER 2Exponential Baby!
The term exponential
