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Pascal Bornet, Author of IRREPLACEABLE & Intelligent Automation – Interview Series

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Pascal Bornet is a pioneer in Intelligent Automation (IA) and the author of the best-seller book “Intelligent Automation.” He is regularly ranked as one of the top 10 global experts in Artificial Intelligence and Automation. He is a member of the Forbes Technology Council.

Bornet is also a senior executive with 20+ years of experience leading digital transformations for corporates. He is the founder and former leader of the “AI and Automation” practices at McKinsey and Ernst & Young (EY). 

He is also releasing a new book titled:  IRREPLACEABLE: The Art of Standing Out in the Age of Artificial Intelligence.

When did you first discover AI and realize how disruptive it would be?

My journey with AI began over 20 years ago when I started working on AI and automation projects at leading consulting firms. Even in those early days, I could sense the immense potential of this technology to transform businesses and society.

However, the real turning point for me was around 2015-2016, when AI started making headline news with breakthroughs like AlphaGo defeating the world champion in the complex game of Go. It was a powerful demonstration of how far AI had come and how it was starting to surpass human capabilities in certain domains.

This was also the time when I saw a significant uptick in interest from businesses across various industries wanting to explore AI. They were realizing that this wasn't just hype anymore – AI was becoming a real game-changer. Companies that had been skeptical or on the fence were now scrambling to understand and adopt the technology.

Seeing this shift in mindset and the accelerating pace of AI advancements, it became clear to me that we were on the cusp of a major disruption. AI wasn't just going to change a few processes here and there; it was going to fundamentally reshape how we work, live, and interact with one another. This realization was both exciting and sobering, and it drove me to focus my research and work on helping individuals and organizations navigate this transformation.

You are known for emphasizing how empowering AI is, but most people fear losing their jobs. What are the skills that humans need to reinforce in order to not be replaced by AI?

It's true that the specter of job losses due to AI automation is a real fear for many. However, I firmly believe that AI is ultimately empowering, not threatening, to human potential – if we approach it in the right way.

The key is to focus on cultivating and reinforcing the abilities that are uniquely human and difficult for AI to replicate. In my book, I refer to these as the “Humics” – genuine creativity, critical thinking, and social authenticity.

  • Genuine creativity is about generating original ideas, solutions, and artistic expressions that draw on our uniquely human subjective experiences, emotions, and intuition. While AI can recombine existing elements in novel ways, it lacks the authenticity of the human experience, and the human spark of imagination that leads to truly groundbreaking innovations.
  • Critical thinking involves analyzing information, questioning assumptions, and making ethical judgments based on our values and understanding of context. AI can process data and identify patterns, but it doesn't have the human capacity for discernment, skepticism, and moral reasoning.
  • Social authenticity encompasses our ability to build deep, trust-based relationships, communicate with empathy, and lead and inspire others. These interpersonal skills are rooted in our emotional intelligence and self-awareness, which AI cannot fully simulate.

By developing these Humics and learning to create synergies with AI, individuals can provide value that is distinctly human and highly prized. It's about leveraging AI to automate routine tasks, while doubling down on our humanity for high-value, creative, and interpersonal work.

Becoming irreplaceable also means being AI-ready, mastering the skills to work effectively alongside AI, and “change-ready”, developing the resilience and adaptability to thrive in a rapidly evolving world. By cultivating these three competencies, individuals can navigate the AI era with confidence and create their own irreplaceable value proposition.

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

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 strategies 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 can't replace. This might involve redefining job descriptions to emphasize tasks that require creativity, critical thinking, emotional intelligence, and complex problem-solving.

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 the 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.

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.

You’ve previously stated that service industries are the most likely to benefit from Generative AI, can you give some examples of this?

Service industries, which rely heavily on human interaction and creative problem-solving, stand to gain significantly from Generative AI. This technology, which can create new content (text, images, audio, etc.) based on patterns learned from existing data, has immense potential to augment and amplify human capabilities in service roles.

One prime example is in customer service. Generative AI can be used to create highly personalized and context-relevant responses to customer inquiries, drawing from a vast knowledge base. This could enable customer service representatives to provide faster, more accurate, and more tailored support. At the same time, the AI could handle routine queries, freeing up human agents to focus on more complex, emotionally sensitive situations that require empathy and judgment.

In creative fields like design and advertising, Generative AI could serve as a powerful ideation and brainstorming tool. For instance, a graphic designer could use AI to generate a wide variety of design elements or layouts based on a set of parameters, which they could then refine and curate based on their creative vision and understanding of the client's needs. This synergy of AI-generated ideas and human curation could lead to more innovative and impactful designs.

In education and training, Generative AI could be used to create personalized learning content and assessments adapted to each learner's needs, goals, and progress. Teachers could use AI to generate targeted practice problems, explanations, and feedback, allowing them to provide more individualized support at scale. At the same time, the AI could free teachers from routine tasks like grading, enabling them to focus on higher-value activities like mentoring, coaching, and fostering critical thinking skills.

In healthcare, Generative AI has exciting applications in areas like patient education and engagement. For example, AI could generate personalized health advice, reminders, and motivational content based on a patient's specific condition, lifestyle, and preferences. This could augment the work of healthcare professionals by reinforcing key messages, answering common questions, and keeping patients on track with their treatment plans.

The common thread across these examples is that Generative AI is not replacing the human service provider, but rather augmenting their capabilities. It's taking on the more routine, data-driven aspects of the role, allowing the human to focus on the high-touch, high-value activities that require creativity, critical thinking, and emotional intelligence.

By embracing this augmentation mindset, service industries can harness Generative AI to provide more personalized, responsive, and innovative services, ultimately enhancing the value and impact of their human workforce.

Could you share some specific examples of how AI is transforming industries like finance or healthcare?

AI is driving transformative changes across various industries, and finance and healthcare are two prime examples where the impact is particularly profound.

In finance, AI is revolutionizing the way financial institutions operate, from front-office customer service to back-office risk management. For instance, many banks now use AI-powered chatbots to handle customer inquiries, providing 24/7 support and freeing up human agents to focus on more complex issues. These chatbots can understand natural language, access account information, and even make personalized recommendations, greatly enhancing the customer experience.

AI is also transforming fraud detection and risk management in finance. Machine learning algorithms can analyze vast amounts of transaction data in real-time, identifying patterns and anomalies that might indicate fraudulent activity. This enables banks to detect and prevent fraud more effectively, reducing losses and protecting customers.

In investment and trading, AI is being used to make more informed and timely decisions. Algorithms can analyze market data, news sentiment, and social media trends to predict stock prices and optimize portfolio allocation. Some AI-driven hedge funds are even outperforming traditional funds managed by human traders.

In healthcare, AI is making significant strides in areas like diagnosis, drug discovery, and personalized medicine. For example, AI algorithms can analyze medical images like X-rays and MRIs to detect signs of diseases such as cancer, often with a level of accuracy that matches or surpasses human radiologists. This can lead to earlier detection and better patient outcomes.

AI is also accelerating drug discovery by predicting how molecules will behave and interact, reducing the time and cost of developing new medicines. In 2020, the first AI-designed drug entered clinical trials, marking a major milestone in this field.

Personalized medicine is another exciting frontier where AI is making an impact. By analyzing a patient's genetic data, lifestyle factors, and medical history, AI can predict their risk of certain diseases and recommend tailored preventive measures or treatments. This shift towards proactive, individualized care has the potential to greatly improve patient outcomes and reduce healthcare costs.

AI is also being used to enhance remote monitoring and telemedicine. Wearable devices and smartphone apps can collect health data in real-time, which AI can then analyze to detect early signs of health issues and alert healthcare providers. During the COVID-19 pandemic, AI-powered chatbots and virtual assistants played a crucial role in triaging patients, providing information, and reducing the burden on overwhelmed healthcare systems.

These are just a few examples of how AI is transforming finance and healthcare. What's important to note is that in each case, AI is not replacing human professionals but augmenting their capabilities. It's taking on the more routine, data-intensive tasks, allowing humans to focus on the complex, judgment-based aspects of their roles.

As these industries continue to adopt and integrate AI, we can expect to see even more innovative applications that enhance efficiency, accuracy, and personalization, ultimately leading to better outcomes for businesses and consumers alike. The key will be to manage this transformation in a way that empowers rather than displaces human workers, harnessing the power of human-machine collaboration.

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?

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. As I discussed in a recent webinar hosted by data protection company Clumio, with the rise of deepfakes, growing concerns around AI biases, and of course, the colossal problem of data breaches, businesses need to focus on trust now more than ever.

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. As I observed during the Clumio webinar, the winners in an AI-driven world won’t be the companies with the most complex datasets or the largest datasets, but the ones that are able to build an unshakable foundation of trust underpinning their digital ecosystems.

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?

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 about 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 resistant 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 and transparent.

Looking ahead, what do you see as the future role of AI in the workplace?

Looking ahead, I see AI fundamentally transforming the nature of work, not replacing humans, but augmenting and elevating human capabilities.

Routine, repetitive tasks will increasingly be automated, freeing humans to focus on higher-value activities requiring creativity, critical thinking, emotional intelligence, and complex problem-solving. AI will serve as a powerful tool for ideation, analysis, and decision support, enhancing human judgment and expertise.

We'll see more human-AI collaboration, with AI handling data-intensive aspects while humans provide nuanced understanding and ethical oversight. Jobs will be redesigned around this synergy, emphasizing uniquely human skills.

AI will also enable more personalized, responsive, and predictive services, from customer support to healthcare delivery. It will drive innovation, uncover new insights, and create new forms of value.

However, this transition will require significant reskilling and upskilling of the workforce. The role of education and training will be crucial in preparing people to work effectively alongside AI.

Ultimately, the future of AI in the workplace is about augmentation, not replacement. It's about creating a symbiotic relationship where humans and machines each play to their strengths, enhancing efficiency, innovation, and human potential. The organizations that master this balance will be the ones to thrive.

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

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.

In September 2024, you’re publishing your second book, IRREPLACEABLE: The Art of Standing Out in the Age of Artificial Intelligence, can you tell us more about this upcoming book and what we should expect from it?

In my upcoming book, IRREPLACEABLE: The Art of Standing Out in the Age of Artificial Intelligence, I dive deep into what it means to thrive in an era increasingly shaped by AI.

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 and cultivating the uniquely human qualities that no machine can replicate. I guide the reader on a journey to master the Three Competencies of the Future: becoming AI-Ready, Human-Ready, and Change-Ready.

Through engaging stories, practical strategies, and thought-provoking insights, IRREPLACEABLE equips you to:

  • Harness the power of AI to augment your life, work, and business
  • Protect yourself and your family from AI's potential pitfalls
  • Develop the skills that will make you indispensable in an AI-driven world
  • Transform your company into an IRREPLACEABLE business
  • Raise children who can thrive alongside AI
  • Discover your unique purpose in a world redefined by technology

Whether you're an individual looking to future-proof your career, a parent looking to raise AI-ready children, or a business leader striving to navigate technological disruption, IRREPLACEABLE is your essential guide. It's not just about adapting to change; it's about harnessing the power of AI to become the best version of yourself.

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.

Thank you for the great interview, I look forward to reading IRREPLACEABLE which is currently available for pre-order, readers may also wish to read Intelligent Automation which is available today.

Reads can also visit the Pascal bornet website to learn more.

A founding partner of unite.AI & a member of the Forbes Technology Council, Antoine is a futurist who is passionate about the future of AI & robotics.

He is also the Founder of Securities.io, a website that focuses on investing in disruptive technology.