Thought Leader and Influencer Interview with Pascal Bornet, Keynote Speaker, Best-selling Author and Social Media Influencer, Intelligent Automation

At a Glance

Pascal Bornet, Keynote Speaker, Best-selling Author and Social Media Influencer, Intelligent Automation

Focus Areas: AI, Generative AI, RPA, CSR

Thinkers360 In-Depth Profile & Portfolio: Pascal Bornet

Social Media: LinkedIn | X

Books: IRREPLACEABLE: The Art of Standing Out in the Age of Artificial Intelligenc e, I NTELLIGENT AUTOMATION.

Highlighted Content: How to Build Human-AI Synergy to Boost Business Intelligent Automation Newsletter #119.

Thinkers360 Leaderboards: Top 50 Global Thought Leaders and Influencers on Generative AI 2024, Top 50 B2B Thought Leaders, Analysts & Influencers You Should Work With In 2024 (North America),Top 50 Global Thought Leaders and Influencers on RPA 2023.

Thought Leader and Influencer Interview

Thinkers360 thought leader interviews profile prominent members of the Thinkers360 community who embody the power of ideas in their work. In this edition, we speak with Pascal Bornet, Keynote Speaker, Best-selling Author and Social Media Influencer, Intelligent Automation.

Pascal is a pioneer and recognized expert in Intelligent Automation (Hyperautomation). He is the author of the first reference book on Intelligent Automation. He founded and led the “Artificial Intelligent & Automation” practices for McKinsey & Company and Ernst & Young (EY).

His new book “IRREPLACEABLE: The Art of Standing Out in the Age of Artificial Intelligence ” was recently featured in Thinkers360’s ” Inspiring Reads: New Books from Thinkers360 Thought Leaders 2024“, and he is featured in Thinkers360’s annual leaderboards: Top 50 Global Thought Leaders and Influencers on Generative AI 2024, and Top 50 B2B Thought Leaders, Analysts & Influencers You Should Work With In 2024 (North America), to name just a few.

Brands, Organizations & Startups: Sign up to find and work with advisors, analysts, authors, influencers and speakers in your niche and to amplify your own executives, thought leaders, employee advocates and content among our opt-in B2B analyst and influencer community with over 100M followers on social media combined! We specialize in B2B strategic marketing, B2B analyst and influencer relations, and B2B thought leadership marketing putting your brand in front of active B2B buyers, analysts and influencers worldwide – Explore membership today!

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Thinkers360: Why are you writing this book now?

PB: I wrote this book for two main reasons:

The True Value of AI is Human-Centric

With over 25 years as an executive at McKinsey and EY, leading AI implementations worldwide, I’ve learned that successful AI projects hinge not on technology alone but on human interaction. Companies that thrive with AI always place humans at the forefront through education, communication, and effective change management. Without human insight and interaction, AI loses its essence and purpose, causing AI initiatives to fail and people to fear AI.

This realization has guided my research for the past 20 years, focusing on the intersection of AI and humans —where the greatest value can be generated for organizations and individuals alike. This book is the culmination of that work.

Adapting to Rapid Change is Crucial

The last two years have marked a noticeable acceleration in progress, propelled by technological advances. This trend is set to continue, bringing even faster changes and greater disruptions. As an expert in this field, I am dedicated to ensuring that no one is left behind in this swiftly evolving landscape. This book offers practical guidance and proven strategies to help individuals and organizations navigate and succeed in these transformative times.

The key to success in a world increasingly changing, boosted by technology, is to become IRREPLACEABLE.

Individuals are IRREPLACEABLE when:

They can handle high levels of change and disruption with calm.
They leverage AI to augment their skills and automate tasks to the maximum while mitigating risks such as data privacy, ethical concerns, distractions, and addictions.
They develop their uniquely human abilities to a whole new level, allowing them to generate more value with technology.

Companies are IRREPLACEABLE when:

They can navigate high levels of technological, market, and regulatory change, and disruption with resilience.
They leverage AI at scale to generate revenue, enhance operations, and automate processes to the maximum while building trust by effectively mitigating risks such as fairness, transparency, and privacy.
They strategically combine human abilities with technological abilities generating higher differentiation and innovation.

Thinkers360: Give us a sense for what the book is all about?

PB: In a world increasingly driven by AI, how do we ensure we remain indispensable? How do you protect your job, your business, and your children from the challenges posed by this transformative technology? And collectively, how do we protect our humanity? In IRREPLACEABLE, I offer a framework for not just surviving, but thriving in the age of AI.

Drawing on over 20 years of pioneering AI research and practical experience, I reveal the secrets to living in harmony with AI while cultivating the uniquely human qualities that no machine can replicate. I introduce the Three Competencies of the Future that we need to develop urgently to thrive in our new world.

Through engaging stories, practical strategies, and thought-provoking insights, you will discover:

Techniques to boost your career using AI―without losing your job to automation
Secrets to unlocking your uniquely human abilities―ones AI can NEVER replicate
Counterintuitive methods to raise Future-ready children
Strategies to avoid falling into the “AI Obesity” trap
Blueprints for leveraging AI to turn your company into an unstoppable force of innovation

Whether you’re an individual looking to future-proof your career, a parent aiming to raise future-proof children, or a business leader eager to steer your organization through technological disruption, IRREPLACEABLE offers a universal and comprehensive framework.

AI is not the destination; it’s the vehicle that takes us to a more human future. This book is your GPS. Embark on the journey to become IRREPLACEABLE and discover how the AI revolution is not just about technology; it’s about rediscovering the essence of what makes us human.

Thinkers360: What does it mean to be IRREPLACEABLE?

PB: “This book is a wake-up call. Our humanity is at risk, and we need to act now!”

In the context of AI’s expanding capabilities, being IRREPLACEABLE becomes a poignant subject. As AI progresses, it can mimic or surpass human abilities in tasks ranging from mundane to highly complex. This technological advancement raises existential questions: What facets of our humanity remain uniquely ours? Are there elements within us that are impervious to digitization or automation?

It is a thought-provoking notion that our humanity, our values, our jobs, or even our loved ones could, one day, be replaced by AI. To prevent this, we must recognize and cherish what is truly IRREPLACEABLE within ourselves: as individuals and as humans. There’s an urgent need to develop strategies to coexist with AI while maintaining our unique human attributes. By doing so, we ensure that as AI evolves, it serves as a complement to, rather than a replacement for, the rich and multifaceted nature of human life.

The concept of being IRREPLACEABLE is not limited to individuals; it is equally applicable to companies. For instance, a company composed of IRREPLACEABLE individuals will be more likely to thrive in an AI-driven world than one that does not.

How IRREPLACEABLE are you? Take the IRREPLACEABLE Quiz, and assess yourself in 5 minutes. And most importantly, understand how you can improve yourself.

Thinkers360: What are the Three Competencies of the Future, and why do we all urgently need to develop them?

PB: We’re standing at the edge of a massive shift with artificial intelligence, and I’m here to tell you why becoming IRREPLACEABLE isn’t just crucial – it’s urgent.

Let me introduce you to a concept I call “AI obesity.” Just like we got hooked on fast food, we’re now binging on fast creativity, fast connections, and fast decisions. We’re settling for “good enough” instead of striving for excellence, and we’re losing our jobs, our businesses, and our humanity in the process.

But here’s the thing: AI itself isn’t the problem. It’s like food – not inherently good or bad, it’s all about how you consume it. It offers great benefits but also dangers. To thrive, we need the right competencies – like juggling sharp knives skillfully.

That’s why I’ve developed a framework I call the Three Competencies of the Future. These competencies are about being AI-Ready, Human-Ready, and Change-Ready. They form a unified framework that applies to both individuals and businesses. They’re designed to help you not just survive, but thrive in this AI-driven world.

AI-Ready means having a deep understanding of AI technologies, their capabilities, and their limitations. It’s about knowing how to leverage AI to augment our own abilities and make better decisions. “AI-Ready” involves also safeguarding against its negative impacts, such as ethical issues or addictions. It’s also about having the right mindset: seeing AI as a tool to be harnessed, not a threat to be feared.
Human-Ready means cultivating the Humics, which are the uniquely human abilities that AI cannot replicate authentically: genuine creativity, social authenticity, and critical thinking. It’s about recognizing that our value lies not in our ability to process information, but in our ability to create, innovate, and connect with others on a deep, emotional level. A Human-Ready individual is self-aware, emotionally intelligent, and able to collaborate effectively with both humans and machines.
Change-Ready means having the resilience and adaptability to thrive in a world that is evolving at an increasing pace. It’s about being able to learn, unlearn, and relearn as the situation demands. A Change-Ready individual is proactive, flexible, and always looking for ways to improve. They see change as an opportunity, not a threat, and are willing to take risks and experiment with new ideas.

It is urgent for all of us to develop the Three Competencies of the Future because of the pace of change. For example, we no longer have time to retrain for new jobs before the old ones disappear. We must act now to become IRREPLACEABLE.

The Three Competencies are the foundation upon which we can build a future in which humans and machines work together in harmony to solve the greatest challenges of our time… This is what we call becoming IRREPLACEABLE!

They are metaphorically represented as a tree: the roots represent being Change-Ready, providing resilience and adaptability; the trunk symbolizes being AI-Ready, growing as high as our capabilities augmented by AI and technology; and the branches represent being Human-Ready, the ultimate form necessary for long-term success and skills adaptability.

Editor’s Note: See Pascal Bornet’s profile to work with him directly.

Thinkers360: How can companies scale their AI transformations?

PB: Based on my experience as a consultant, I’ve observed a crucial gap in how companies implement AI at scale. In 2024, while 90% of companies are still only experimenting with AI on a small scale, about 75% of knowledge workers worldwide are already using AI in their daily tasks, and 78% of these usages are through unsanctioned systems. This reveals a significant opportunity for companies to synchronize their AI strategy with the faster pace of individual adoption, and reach scale faster. Here are my strategic recommendations for Scaling AI:

Start Small, Aim Big: Begin by deploying AI in everyday tasks to enhance productivity by 10% to 20%. This initial step is not just about immediate gains but also empowers employees to use AI correctly and avoid reliance on unsanctioned programs.
Focus on Critical Functions: Once foundational use is established, expand AI applications to critical business functions such as marketing or product management. Here, AI can drive a substantial boost in efficiency, typically ranging from 30% to 50%.
Explore New Business Models: Leverage AI to develop innovative business models for a long-term competitive edge. This often requires a bold rethinking of how value is created and delivered within an industry.

Implementing the Virtuous AI Cycle to Explore New Business Models:

The virtuous AI cycle is a strategic framework that facilitates continuous improvement and value creation through AI:

Data Collection: Start by gathering a diverse set of data from clients and partners, which may include online purchases, customer feedback, and social media interactions. This broad spectrum of data helps in understanding customer preferences and buying patterns.
Data-Driven Innovation: Use AI to analyze the collected data to derive actionable insights. This can lead to service innovations and the identification of new product demands or market trends.
Value Creation and Expansion: By innovatively applying insights, companies can create more value for their clients, which in turn attracts more customers and increases market share.
Data Generation and Enrichment: More clients and increased interactions generate more data, fueling the cycle further.

Kickstarting the Cycle: The cycle begins with strategic data collection, engaging with customers through social media, and forming partnerships that can expand your data ecosystem. For example, a bank might partner with insurance or mortgage companies to share and enrich data, creating new services that enhance customer satisfaction and retention.

By following these steps and embracing the virtuous AI cycle, companies can accelerate their AI transformation, making technology a core component of their strategic operations and future growth.

Thinkers360: Can you explain why Trust is the most important asset in the age of AI, and how can companies build it?

PB: Building trust with customers and partners is pivotal in today’s data-driven business environment. Trust is not a commodity that can be bought; it is earned through consistent, transparent, and honest interactions that prioritize customer needs and privacy. Here’s how companies can establish and maintain trust:

Fairness and Bias Mitigation: Begin by integrating bias identification right at the data collection stage. Employ tools to detect biases in databases before even building models. It’s crucial to acknowledge and address one’s own biases to ensure fair treatment of all users.
Transparency through Explainability: Making AI systems transparent involves explainability, which allows users, developers, and stakeholders to understand how AI models make decisions. This is about transforming the AI ‘black box’ into a ‘glass box,’ where operations are visible and understandable. Explainability is particularly crucial in sectors like healthcare where decisions have significant impacts. Engage closely with users to determine the level of explainability they find useful and necessary.
Privacy-First Culture: Develop a culture centered on privacy by asking critical questions about data retention: “Am I entitled to keep this data? Where can I store it? How do I secure access to this sensitive information?” Companies should collect only the data necessary for AI systems to function effectively and anonymize data whenever possible to minimize privacy risks. It is also essential to educate every employee about the importance of privacy and their role in protecting it.

By focusing on these areas—fairness, transparency, and privacy—companies can build a foundation of trust that not only attracts customers and partners but also fosters long-term loyalty and engagement.

Thinkers360: How can organizations ensure that AI tools are augmenting rather than replacing human workers?

PB: For organizations to ensure that AI augments rather than replaces human workers, they need to take a human-centric approach to AI implementation. This means putting people at the heart of their AI strategy and focusing on how the technology can empower and enhance human capabilities.

One key aspect is job design. As organizations introduce AI, they need to re-imagine roles and responsibilities to focus on the uniquely human skills that AI cannot replace. This might involve redefining job descriptions to emphasize tasks that require creativity, critical thinking, emotional intelligence, and complex problem-solving.

Ultimately, the goal should be to create a symbiotic relationship between humans and AI, where each focuses on what they do best. By designing jobs and organizations around this principle, we can harness the power of AI to enhance rather than diminish human potential and value.

For example, a customer service representative’s role could evolve from handling routine inquiries (which can be automated) to managing more complex, emotionally charged situations that require empathy and judgment. An accountant might spend less time on data entry and more on interpreting insights and providing strategic advice.

Organizations also need to invest in upskilling and reskilling their workforce to prepare them for these new roles. This includes providing training not just on how to use AI tools, but also on how to develop and apply the “Humics” in a business context.

Another critical factor is to involve employees in the AI implementation process. Rather than imposing AI solutions from the top down, organizations should engage workers in identifying areas where AI can assist them and designing human-machine collaboration. This not only helps ensure that AI is augmenting in a way that benefits employees but also fosters a culture of continuous learning and adaptability.

Leadership also plays a crucial role. Leaders need to set a clear vision for how AI will augment and empower the workforce, and consistently communicate and model this perspective. They must also be proactive in addressing concerns around job security and creating a psychologically safe environment for employees to experiment, learn, and adapt.

Thinkers360: With the increasing use of AI in business, data security, privacy, and governance have become critical issues. How should companies address these concerns to maintain trust with their customers?

PB: As businesses increasingly rely on AI and data-driven decision-making, the issues of data security, privacy, and governance have indeed come to the forefront. These are not just technical challenges, but fundamental matters of trust between companies and their customers.

To address these concerns and maintain trust, companies need to take a proactive, transparent, and ethical approach to data management and AI governance. Here are some key steps they should consider:

Firstly, companies need to prioritize data security at every stage of the data lifecycle. This means implementing robust cybersecurity measures to protect against data breaches, hacks, and unauthorized access. It includes techniques like data encryption, secure authentication protocols, and regular security audits. Companies should also have clear policies and procedures in place for handling and reporting any security incidents.

Secondly, companies must be transparent about their data collection and usage practices. They should provide clear, easy-to-understand privacy policies that inform customers about what data is being collected, how it will be used, and with whom it may be shared. Customers should have control over their data, with the ability to access, update, or delete their information as needed.

In the context of AI specifically, companies should be transparent about where and how AI is being used, and what impact it may have on customers’ experiences or decisions. If an AI system is making significant decisions that affect customers, such as approving a loan or determining insurance premiums, companies should be able to explain how these decisions are made and provide avenues for customers to appeal or seek human review.

Thirdly, companies need to establish strong data governance frameworks. This involves defining clear policies and procedures for how data is collected, stored, accessed, and used within the organization. It should include guidelines for data quality, data integration, and data security, as well as defining roles and responsibilities for data management.

In the context of AI, data governance also extends to model governance. Companies should have mechanisms in place to ensure that their AI models are fair, unbiased, and aligned with ethical principles. This may involve techniques like Model explainability and fairness testing, as well as having human oversight and accountability for AI-driven decisions.

Fourthly, companies should give customers more control over their data. This includes providing easy ways for customers to opt-out of data collection, or to specify how their data can be used. Some companies are also exploring concepts like “data trusts” or “data cooperatives”, where customers can voluntarily pool their data for specific purposes in a secure and transparent manner.

Finally, building trust in the age of AI requires a fundamental shift in corporate culture and leadership. Companies need to embed principles of responsible AI and data ethics into their core values and decision-making processes. They should educate and train all employees on these principles, and hold leadership accountable for upholding them.

By taking these steps – prioritizing security, being transparent, governing data responsibly, empowering customers, and fostering an ethical culture – companies can build and maintain trust in the age of AI. It’s not just about compliance; it’s about actively demonstrating to customers that their data and their trust are valued and protected.

In an era where data is the new oil and AI is the new engine of growth, trust is the ultimate currency. Companies that can earn and keep this trust will be the ones that thrive in the AI-powered future.

Thinkers360: Bias in AI models is a significant concern. What best practices do you recommend for organizations to identify and mitigate biases in their AI systems?

PB: Bias in AI is indeed a critical issue. AI systems learn from the data they are trained on, and if that data reflects historical biases or skewed representations, those biases can become amplified and perpetuated in the AI’s decisions and outputs. This can lead to unfair, discriminatory, or even harmful outcomes, eroding trust in AI and causing real harm to individuals and society.

To identify and mitigate these biases, I recommend organizations adopt the following best practices:

Firstly, be aware of the various types of bias that can creep into AI systems. Everyone should read at least once the 188 cognitive biases that any human possesses. Go on Wikipedia and search for “cognitive biases”. As you will notice, some common ones include:

Selection bias: when the data used to train the AI is not representative of the real-world population it will be applied to.
Historical bias: when the data reflects historical societal biases, such as racial or gender discrimination.
Measurement bias: when the way data is collected or labeled introduces bias, such as using subjective or inconsistent criteria.
Algorithmic bias: when the AI model itself introduces bias, such as overfitting to certain features or magnifying small differences.

By understanding these different types of bias, organizations can be more proactive in detecting and addressing them.

Secondly, establish diverse and inclusive teams to work on AI projects. Having team members with different backgrounds, perspectives, and experiences can help identify biases that might otherwise go unnoticed. It’s also important to involve domain experts and stakeholders who understand the context in which the AI will be used.

Thirdly, conduct rigorous data audits. Before training an AI model, carefully examine the data for potential biases or skews. Check for representativeness, accuracy, and completeness. Consider techniques like stratified sampling to ensure fair representation of different groups.

Fourthly, use techniques like adversarial debiasing during the model training process. This involves intentionally trying to “fool” the model with biased data and then adjusting the model to be more resilient to these biases. There are also various algorithmic techniques for bias reduction, such as regularization, constraint optimization, and post-processing adjustments.

Fifthly, test extensively for fairness and bias. This should involve testing the model on diverse, real-world datasets and scenarios, not just the training data. Use quantitative metrics to assess fairness, such as demographic parity (ensuring the model’s decisions are independent of sensitive attributes like race or gender) and equal opportunity (ensuring the model performs equally well for different groups).

Sixthly, provide transparency and explainability for AI decisions. Use techniques like SHAP values or LIME to explain how the model is making its decisions, and make these explanations available to users or stakeholders. This transparency can help identify biases and build trust.

Seventhly, establish clear accountability and governance structures. Designate roles and responsibilities for managing bias and fairness in AI, and establish processes for regular auditing, reporting, and mitigation. Ensure there are channels for users or stakeholders to raise concerns or seek recourse if they believe they have been unfairly impacted by an AI system.

Finally, foster an organizational culture of responsible and ethical AI. Regularly train and educate all staff on AI ethics and bias mitigation. Encourage open discussion and reporting of bias concerns. Make ethical AI a core value and a key performance metric for the organization.

By adopting these best practices, organizations can proactively identify and mitigate biases in their AI systems. However, it’s important to recognize that bias elimination is an ongoing process, not a one-time fix. As AI systems evolve and are applied in new contexts, new biases may emerge. Organizations must commit to continuous monitoring, learning, and improvement.

Ultimately, addressing AI bias is not just a technical challenge, but a social and ethical imperative. It’s about ensuring that as we increasingly rely on AI to make decisions that affect people’s lives, we are doing so in a way that is fair, transparent

Thinkers360: How can businesses prepare now for the changes AI is likely to bring in the next five to ten years?

PB: To prepare for the AI-driven changes in the next decade, businesses should:

Develop an AI strategy aligned with business goals, identifying key areas for AI application and investment.
Build AI literacy across the organization, ensuring all employees understand AI basics and implications for their roles.
Invest in data infrastructure and governance, ensuring data quality, security, and ethical handling.
Experiment with AI in controlled environments, starting small and scaling successes.
Redesign jobs and processes around human-AI collaboration, focusing on augmenting rather than replacing human capabilities.
Invest heavily in employee reskilling and upskilling, focusing on developing the “Humics” – creativity, critical thinking, and emotional intelligence.
Establish cross-functional AI governance structures to manage bias, fairness, transparency, and accountability.
Engage in scenario planning to anticipate and adapt to AI’s disruptive impacts on markets, business models, and the workforce.
Collaborate with industry peers, academia, and policymakers to shape the responsible development and deployment of AI.
Cultivate an agile, learning-oriented culture that embraces change and experimentation.

The key is to approach AI not as a one-time project, but as a continuous journey of learning, adaptation, and transformation. Businesses that start now, investing in both technological and human capabilities, will be best positioned to harness AI’s potential and navigate its challenges in the years ahead.

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