A Fireside Chat with Fred Laluyaux and Pascal Bornet about the vision and impact of intelligent automation.
All right. Well, good morning everyone. Good afternoon. Thanks for joining us for that new episode of Cognitive Automation Fireside Chat. My guest today is Pascal Bornet. Pascal, Hello? How are you?
Hi, good. Thank you.
Super happy to have you. So Pascal you're a global expert and a pioneer in the field of, what do you call? Intelligent automation and hyperautomation. Quick intro, built a long career in the field starting with EY and then building the practice for AI at McKinsey in Asia. So global experience, 20 years of experience in the field. And you've just published a book, which is incredibly interesting. When I started working on this topic of intelligent automation or cognitive automation, there was no books. You build one of the, you published one of the first one and I think it's going to be a very important one in our industry. Intelligent Automation: Learn how to harvest AI to boost business and make our world more human, which is a very interesting topic. You have been speaking around the world, you are members of the Forbes Technology Council. You've got more than 300,000 followers on LinkedIn, Twitter. You're very active on social media and talking about these topics. So welcome, super happy to have you. Where am I catching you today?
I'm currently in France.
All right, okay. Not in Asia anymore. Okay.
Not in Asia anymore, no.
Excellent, excellent. So welcome again. So look, let's dive in and I think what we can do this morning is really go into two different part of this chat. First one will be around the what is it and why is it relevant, talking about intelligent automation. And then leveraging the research that you've done with your co-authors and of course your experience. And then we'll also go into the how, and getting your advice and your take on how to deploy it efficiently, how to deploy it effectively. Is that all right with you?
So let's dive in, you can talk about intelligent automation, IA. Everybody talks about AI and you talk about IA. A little bit of a contrarian approach here but tell us what your definition of intelligent automation is.
Okay. So Let's go straight into it. It's one of the most recent and impactful trends in the broad field of artificial intelligence. My co-author and I, supported by the insight of more than 200 experts have tried to define this. And we've defined it as a combination of methods and technologies involving people, organizations and technologies like machine learning, local platforms, robotic process automation and more. I think you've, I mean, probably most of you have heard about the same concepts but using different terms like hyper automation, cognitive automation. Basically all those words are synonyms for us. Intelligent automation is aimed at automating end-to-end business processes in a computerized environment, okay? We are not talking about physical robots or physical automation, but really all the automation that is performed on computers. And what it does, it delivers business outcomes on behalf of the employees, working hands in hands with them to deliver faster, better, cheaper services and improving significantly employee and customer experiences.
So let's take an example, cognitive automation can support the automation of most work activities in a procure-to-pay process. So procure-to-pay process is the end-to-end process that any company has, basically, that helps a company to buy goods or services through a vendor and finally pays this vendor when the vendor has procured what we bought. And going through this process from procure-to-pay, so we have the selection of vendors. Selection of vendors is typically an activity that is done using machine learning. Then you have sending orders to those vendors, typically this can be automated leveraging workflows, workflow platforms. Then you have the reception and the processing of those invoice, is from those vendors. And this can be done using natural language processing and finally, you have to pay vendors and this is a very rule-based activity that can be done by using robotic process automation, okay? Key business impacts of intelligent automation include the increase of the bottom line of companies by 20 to 60%. So it's very significant. And as I said before, customer and employee experience improvements are in the center of intelligent automation.
So you're describing a very hardcore enterprise process and how the different flavors of AI and cognitive technologies and automation technologies can come in. When you talk about your book, you talk about learn how to harvest AI to boost business and make our world more human. I didn't hear the, "Make the world more human," in what you've just described. So that's why I wanted to chat with you because I want to dive a little bit deeper into that. So why this book and why this book on how to make the world more human with what you call intelligent automation?
So let me answer first and straight to the question. Why can we make our world more human with intelligent automation? So intelligent automation could help us to build a more human society because it involves a more engaging definition of work. Definition of work that we involve the time to refocus on what matters the most for all of us in our lives. Which is taking care of ourselves, taking care of the others, taking care of the planet, love family, okay? So thanks to technology, we would be able to refocus on those fundamental values, okay? I'll explain throughout the different questions you will have, I be able to explain more in detail.
And is this what drove you and what motivated you to actually write this book? Because books on AI, there are plenty of them but this one is focused a little bit more on the impact. Of course, across the multiple facets of our life, whether it's the economy but you actually brought the human angle early on in the book, which I thought was super interesting. Was it your main motivation to write the book?
No, many motivations to write it, but definitely giving a purpose to what I'm doing on a day-to-day basis is extremely important. And this helps to understand why we are waking up in the morning and so it's extremely important. The why is extremely important. So getting into why I wrote the book. And basically, it's not I who wrote the book, so I-
Yes, correct. It's a teamwork, two coauthors. One is lan Barkin, a successful entrepreneur in the field of intelligent automation, he built the first pure-play consulting company in the field five years ago. And the second coauthor is Jochen Wirtz, an academic, Vice Dean at the University of Singapore and professor of service marketing. So quite a good mix of backgrounds.
You got the entrepreneur, you got the consultant and then you've got the academic. Okay, that's good. Good combo.
Exactly. And the book is basically, it hasn't been an easy game. It's the outcome of 18 months of work, the research work, writing, it includes surveys across more than 200 intelligent automation experts, okay? Because this book is the first one on this topic. Even though we have our own experience, we have our own thoughts, we thought it was very important to make it as collective as possible. We don't own the truth and it's really when there is a new field coming in like this, when you have more thoughts and more opinions, we've been able to build something that made most sense in our views.
Yeah. I mean, of course I had a chance to read the book early on, a little bit before it was published and give you my take and my take was like, "Wow, you've nailed it." I mean, you've really covered everything from the how, the why, the how, is a very easy read, still fun, you put a little bit of AI humor in a book here and there. And yeah, I mean, if everybody interested in the topics, so basically you read this because I think it's going to be one of the fundamental books in that field. So, I mean, super well done and basically worth the effort and I believe the book is selling pretty well, right?
Yeah, definitely we're making progress. I mean, it released three months ago and been Amazon best seller since then.
Yeah. I think there's a little bit more than luckiness. There's obviously a lot of hard work and a lot of passion and obviously [intelligent automation], I'm going to have to get used to it, is a passion of yours. I think you and I have had many discussions on, I would call it cognitive automation, you call it intelligent automation. But it doesn't matter, it's very similar. So right now the [crosstalk 00:11:04] go ahead.
One thing, I forgot to answer your question, right? Why did I write this book? And I think it's very important because it's really what brought me the energy to wait for 18 months and work hard besides my normal work, doing it. And this key point is passion. I'm extremely passionate by all the contents we've brought in this book, which is basically those different technologies that companies can use but that human can benefit from. And I'm extremely convinced that intelligent automation helps to make our world more human. And that's really driving my energy on a day-to-day basis and that drove my energy to build this book.
Yeah. And your energy and your passion is contagious in the book, for sure. And as I said, it's a very good read because you don't try to, you don't take, you take the topic very seriously but your tone is a little light and I think it's important. But talking about your passion and the impact, right? Right now, the World Economic Forum is happening virtually this year. And when you go through the agenda, there are tons of sessions on, of course, AI tech for good, better world, the fourth industrial revolution. So we've kind of anchored [intelligent automation] for you and your passion, try to give us the reverse perspective of [intelligent automation] for the world and how it connects to Industry 4.0, right? How do you build that connection? What do you see the impact of [intelligent automation] will be on Industry 4.0?
I think it's really in the middle of it.
While the Industry 4.0 involves all types of automation, both the physical robots and the information-based automation, intelligence automation focuses only on the later one, okay? Only on the information, digital-based, computerized-based automation. That we call also the automation of knowledge work. We demonstrate in the book that today knowledge work, so basically the work that is done by people like us and most of you listening to us. People who are producing value by using their brain and most of them using a computer, 80% of the global workforce are basically knowledge workers, okay? And when you think of it, you can imagine what will be the impact of automation throughout this workforce hence the necessity to understand how to well automate and also anticipate what can be the impacts of this on companies, but also on labor force and humans in general.
There is a, you remind me of a comment that professor Fuller from Harvard Business School made when we talked at our Cognitive Automation Summit. And he talked about the impact of these technology on competition for talent and basically saying that historically you start in a field, you're in publishing or you're life sciences or in manufacturing of some sort, you kind of tend to stay in that industry. But with these new technologies and these new approaches to data, the competition is going across silos now and could create a hyperinflation on some talents and a deflation on others. So I think the change is coming and is going to be quite significant. Where do you see companies today in their journey toward intelligent automation? The book is full of good examples, but two questions, the level of adoption. What is your perspective on the level of adoptions? Broadly on this automation of knowledge work, right? And the second part is how advanced is it, right?
So Ray Wang, a good friend and lead analyst for Constellation Research recently published just a few months ago, a paper on Cognitive Apps Help Drive the Future of Autonomous Enterprises. And even I've been discussing this topic for several years now and he has a scale, right? Going from one basic automation to human-directed, to machine intervention, to fully autonomous, to human optional. Literally at that point, you don't really need the humans, machine are basically self-filling and writing on their own. So I really have two questions. First one is, the depth of the adoption, right? And then breadth of the adoption. Maybe you start with the breadth, how wide is it leveraged? And then we can go into some example of how deep it is leveraged.
Got it. So we get, regarding the adoption rate of intelligence automation, according to Deloitte. Deloitte made a survey, they make this survey every two years, I think. The last one was in November last year and they found out that 50%, 50, so five zero. 50% of the companies around the world have adopted intelligent automation in a way or another in their processes. And they say the rate is expected to increase to more than 70% in the next two years. They even say that if this continues, [intelligent automation] will have achieved near universal adoption in the next five years. I went back into the previous surveys and it seems the adoption rate is not going as fast as expected a few years ago, okay? But it's still good to know that 50% of the companies today have already started their journey.
And I think that it might progress faster than we expect in the next few years, especially due to the context of the COVID and the mandatory remote work and mandatory remote everything that we are doing. That involves that any company in the world that wants to survive today needs to be a minimum digitized and a minimum automated, just only to be able to sell their products, to get the cash from that clients. That we used to see [intelligent automation] being a factor of competitiveness between companies. I mean meaning that companies that have implemented it were able to gain market share by selling cheaper and better products compared to their competitors. But nowadays it's not a matter of competitiveness anymore, it's a factor of survival.
So, same Joe Fuller, Professor Fuller in the same little chat, just thinking about another one of his quotes there. He said, "We're moving from fear of missing out to certainty of missing out. We're moving from FOMO to COMO." And I just love that quote, because I think it just nails it. We're talking to, I would echo what you just said. We're talking to a lot of executives around the world who just know that they have to do something. They hired a chief digital officer, they took care of the CX, right? The customer experience, now I can engage with my customer or my consumers and then what? And it's tackling the problem of this big pyramid, where decisions are slowly efficient. But when you say 50% of the companies will have deployed somehow some form of intelligent automation. Do you see that more as what's literally known as the RPA, which is the automation of tasks through bots that are not that complicated or do you see going toward the higher level of the decision-making that requires more complexity, projections predictions, as well as automation? Where do you see the market moving?
For more about "moving from FOMO to COMO", read or listen to Cognitive Automation Redefines Agility at the Cognitive Automation Summit.
I see it in two main things. One is, as you said, the low hanging fruit of rule-based repetitive processes. And on the other side the machine learning, AI use cases that hype to increase the loyalty of clients or improve a supply chain, okay? Both of them very often from my experience, even within the same company very isolated and yes, very isolated, not connected. There is no synergy between both.
So that's kind of the segue to the second part of my question, right? From basic automation to human optional, give us some examples of what you've seen and where you think the market is.
Well, to finish on that point. So the 50% are the companies that have either, usually either started RP or started their machine learning or both, okay? Those are the business that I see on the ground. And back to your question from your friend, Ray Wang about the five levels of cognitive automation. So from my experience, I believe that no companies has reached to their level, beyond the level three. You remember level three being the-
Machine intervention, which is helping in the process.
Correct. I think to go beyond, on top of the critical success factors that companies need to get right and we talk about it in the book and I think we'll come back on that later. A key enabler would be a technology that would be more intelligent, that especially would require less efforts and risks when implemented.
Good so we found an area where we can disagree a little bit, I like it. For me, it's clear that word fully autonomous and I'll ask your perspective on... I'm not even going to go into our field but, because that would be biased but I am happy to discuss that. But more about what do you make of automated training?
Let me finish that point, I'm not sure you got my point. So we to make it clear, today you need an army of consultants or experts to identify use cases, to implement them. What I'm dreaming of and what I think we need in order to go beyond the level three is a technology that is accessible to any company, for example, on the web. Using a software as a service model that would enable, for example, to plug on the company's data, ingest this data instantly, identify the opportunities for improvement and finally implement the actions to solve them automatically. So that, in my view is the necessary lever, technology lever that we need to go to four and five.
So you're introducing a very interesting nuance here. It's fully autonomous, doesn't mean just can run fully autonomously, you're also saying should be deployed autonomously.
And that I would completely agree with you. Today we're not there in the deployment, it still requires human intervention to deploy it. Very good point. However, can we have now some of those intelligence skills run autonomously, controlled by the humans, monitored by the humans? I think the answer is yes and it's been proven in several fields. The human optional, which is the level five that Ray calls out, where eventually the system runs in humans, don't cut that out of the picture completely, I don't believe we're there yet. So almost a little bit of a scary thought. But yeah, I think it's a very good distinction around the level of automation that is required to deploy vis-a-vis run. Very good point. So thanks for that, that's very good insight.
So the next question, kind of moving a little bit and that's a good segue to go into the how, right? Based on your experience and your research, because you really marry both, 20 years of experience as a practitioner with EY and McKinsey and then the research that you've done with your co-author for 18 months. So what did you catch as the most challenging things? The biggest challenge of when adopting intelligent automation?
I would summarize in one word, which is scaling.
Scaling. Today, it's easy to succeed at implementing a PoC, a proof of concept or pilots, but when is about scaling the transformation within the organization across several departments, across functions, across divisions, it becomes very complex. I told you, 50% of the companies have started their journey into intelligent automation, this is according to Deloitte. Nevertheless, only 15%, so one five this time, 15% have been able to implement it in more than three functions or divisions and this is according to McKinsey.
And do you think the scaling is a factor of technology? Of people? What is preventing the scale today?
A combination of all this, combination of all this. And we've done our research also on that point and we've identified five main initiatives that successful companies have been able, have implemented. And it starts with two very fundamental ones, very simple ones, I would say. The first one is, always put people in the center of an [intelligent automation] transformation. So [intelligent automation] is built by people for people. I used to say without people, there is no [intelligent automation], but without [intelligent automation] there are still people, okay? So, here change management, communication, capability building is key, okay? It's critical. The second fundamental is about management support and sponsorship. And it's about also the vision, the strategy and how we document it and design it and how we agree on it, realistically. So building a roadmap, building a business case and having management to drive it, to own it, is critical. I've never seen a successful project with management not being at least supportive, without being extremely supportive or sponsor, okay?
So can I pause you here for a second?
Because I feel like what you're describing, I've heard for the last 25 years in my career for any project, anything involving IT, people, management.
And there is something that I've personally never experienced before. And I think, I did a few years with ACP, I think in the early days of ERPs it was a similar approach where these were board decisions, they were senior executive, CEO level conversations, right? Executive committee conversations, if we don't deploy any ERPs, we can't expand, we can't go global and we're losing ground vis-a-vis the competition who's going to be able to scale much better. And there's been this series of new investments in technology that we've all been part of and it's only now that I see again, that level of engagement. So when you say the management support, the executive support, we are engaging with the CXO, the CEOs, the board of the companies and this is not a nice to have conversation. This is a, back to Joe for his comment, this is a certainty of missing out. We've been talking about the movements in organizations, company disappearing and appearing faster than ever and that is accelerating. Do you agree with that? Do you see that, that when you say management, you're talking about truly senior executives?
Yes, it's really critical. I mean and more and more, I completely agree with you because of the complexity of the environment, because of the fast pace of those changes. I mean, and when you see the rapidity of those changes, we've never seen that before. So it creates more hostility, more risks and the need to be, to have a management closer to the ground.
I also believe that comfortable companies, companies have been sitting on a healthy business that didn't really, haven't been fully disrupted, tend to think that they're going to be happy. The companies that in my experience have truly embraced that change are the ones who had a near-death experience, are the ones were challenged to their core. And where again, certainty of, if we don't do something radical, it's not going to work. You look at functions like providing, [crosstalk 00:30:13] go ahead.
The same for people, I think people. People who have-
The same what?
I think people who have seen the worst are known to know what is happiness and have the capacity to take more perspective on life.
So as you're going on to people, maybe we move to that question around people in two dimensions, right? There's the employee and then there is the customer, right? So maybe you can talk about what, and I loved your book with that idea of how [intelligent automation] makes the world a better place. So we'll make the world a better place. So talk a little bit about, and you've touched on that earlier about how AI improves the employee's experience, employee's life.
Yes. I wanted to finish on the five points because I only covered two for now. So in case someone is following us very closely, when they watch us, we need, I'll give the three other components.
Sure, go ahead.
Yes, so we discussed the more common critical success factors, which are people first and then management support. I think the third one, I mean, the other ones are more specific I would say, to [intelligent automation] and to technology and digital in general. The third point is about combining [intelligent automation] technologies to create synergies and basically be able to automate complex end-to-end processes. Remember the example I gave you for purchase too. It's only by combining different technologies that you are able to automate such a long process. And we've seen so many companies limiting their impact and scale because they've only been using RPA or machine learning or computer vision, okay?
The fourth point is about democratizing [intelligent automation] by using technologies like local platforms, local technologies, user-friendly technologies that require limited skills for people to build [intelligent automation] applications and those technologies make [intelligent automation] accessible to most business users. As a result, two key results to do this technology. The first one is, accelerate the transformation speed. Indeed, when you have more people implementing, then collectively we go faster than only a single center of excellence, okay? But most importantly, I think it's driving ownership and acceptance of technology.
When people own, I mean are capable of changing their day-to-day work by automating their own transactional work that they don't like to be able to focus on what they like. When they have the hands, the control, the capacity of changing that, the ownership of the transformation is very different. And this is how I've seen companies being able to change the mindsets, change the future of the company towards more digital and automation. And finally, I think there is more and more, an opportunity to leverage technology to help implement faster and better technology, okay? So it's about automating the automation, I think you told me that already a few times. And I think this is particularly, especially if we think about the five levels you gave me before going from three to four in my view is really about leveraging that point. So today the transformations are too manual, too human intensive. If technology can help us, for example, process discovery, process mining, data discovery. We have already quite a lot of technologies that can help to automate the automations, automate the ability of automations.
Yeah, no, thanks for that. Sorry, if I was, I wasn't trying to rush you but you have a lot of depth on those and every of these questions. So I'm glad you're sharing your knowledge and experience. On your fourth point, do you see, we've been talking about the citizen developer, right? For many, many years and it's been overused by technology companies. Do you think that this is finally with [intelligent automation] coming to a reality? And maybe that's kind of the segue into the question of improving employee's experience. I mean, if you're an employee, how does AI improve or [intelligent automation] improves your experience, but you kind of, how do you make sure that you contribute to that deployment as well? And the thought that comes to my mind is I think we're going to get to a point where the citizen developer will become a reality, but that's just, we'd love to get your thoughts on that.
Exactly. And it's really progressing, not as fast as we would like but it's progressing well in getting the technology closer to people. And we talk about this in the book. When we talk about this, we talk about the symbiosis of people and technology. So the closer we are to technology, the more technology can help us, okay? The more it gives to us and you can think of your mobile phone and how much it gives you every second or every minutes in your day. And on the other side, on the other sense, technology also getting closer to us, understands us better and can help us to use it better. And you can think here of suggestions, algorithms, you can think of many technologies that are helping us to have an easier use of the technology.
So I really believe that the point number four, which is democratizing intelligent automation is currently helps, is progressing a lot, thanks to the progressing technologies. And now to your question, how does [intelligent automation] improve an employee's experience? So that's a point but I like to start with the fact. Facts that according to a Gallup research, okay? Gallup is a very famous company in the field of research in human resources, especially. 85% of the employees worldwide are not fulfilled by their work, so they think it's too manual, too repetitive, too tedious. And [intelligent automation] solves the large part of this issue by freeing up employees from those repetitive transactional activities, for example, keying in invoices in an accounting software for eight hours during the day, okay? This is something that, I mean, nobody can lack it's not, and I wouldn't even think of it for my worst enemy.
[Intelligent automation] helps to refocus those people on more value add activities but also activities that are more exciting, for example, the one involving insights, creativity, relationship. And thirdly, it helps to augment them. So transforming them into super humans, able to generate insights from the millions of data in two seconds. So for example, identifying a tumor on an x-ray in just a few seconds. And according to our research that we present in the book, about 30% of the current scope of work tasks that are performed by people, can be augmented. The rest can be either automated or eliminated. For those that are eliminated, think of all those unproductive meetings or emails that we have everyday.
I don't know what you're talking about.
And here again, technology can help us, I'm explaining the book for this last point. How can technology help us to diagnose ourselves? To basically understand from observing what we are doing during the day, helping us to be closer to the leading practices. For example, never organize a meeting with more than four people. That's a rule to help ensure that at the end of this meeting, you got your agenda reached, you got your decisions taken and you haven't [crosstalk 00:39:36].
But that's not [intelligent automation], that's good practices, right?
Yes, but [intelligent automation] helps to observe what you're doing and will tell you if invite five people, you might not invite five people, think of these rules.
Good, that's fair point, that's fair point. This is super interesting but when you're talking, I'm hearing we're getting almost to a religious discussion or a philosophical discussion around, yeah, but not everybody will be able to adapt to the new set of, to the work of tomorrow. What do you answer to that? "Okay, well, I don't have to do the repetitive task anymore but I'm not super educated and I don't have access to all this knowledge. So what do I do?" I think I know what your answer will be but I'd to hear it.
First of all, there's a place for everyone. There has always been and there will always be. But there is a critical point that you mentioned is that our skills, the skills of today are different from the skills that will be required tomorrow, especially because a lot of those skills will be taken by technology. And I think, and we explain this in the book, one of the key party that we need to implement soon in this journey is understanding what are the skills that make us different from technology? What are the skills that technology will never take? And as we've seen from the examples before, what technology is taking today is really the task that nobody wants to do. I mean, so it's really about, so to answer your point, it's really about identifying those skills that make us different from technology and choosing of all of those skills, which ones are the closest to our personality, to our passion.
We've identified those skills, we've done a prediction of what can be those skills in the future. And we've identified creativity as one of them, we've identified adaptability as another one of them. We've identified critical thinking, we've identified learning how to learn, so you can see all those capabilities are, I would say, soft capabilities. It's not technical, it's not, so I think the critical skills or the skills that are highly paid today might not be the skills that will be highly paid in the future, because of the [crosstalk 00:42:27].
It was very interesting talk, maybe it was three, four years ago by Mark Andreessen about, after they'd done their prediction of the evolution of work in general and basically saying, "Look, you had a lot of our energy went into working the fields and then we brought machinery and then the workforce went into the factories and then we brought machinery and then workforce went into the offices and now, we're bringing machinery." And there is always something else coming up, there'll be more people taking, spending their time and making a living in helping others, the elderly, the sick and so on and so forth. The arts, there are so many other fields that will benefit from needing as a society, less people to run a business, run a supply chain, run a manufacturing site and so on and so forth.
Pascal, I'm going to move a little further along because you and I can say maybe we do a part two and a part three of this conversation, but I want to be mindful of the time of the folks listening to us today. And probably go to the question that is the most thought provoking, right? Is how does AI save lives and we would talk, closing the discussion on saving lives and making a better world. Can you share your thoughts a little bit on that?
Yes. So the first part of the book explains basically why our world needs more intelligent automation. We need more intelligent automation to boost companies' efficiency, to boost customer experience, to boost employee experience but also to save lives and to save money, okay? And saving life is, we've done our calculation. Did you know that 56 millions of people are dying every year? That's the amount of people on earth, so it's going to be from 56 to 58, but roughly this amount every year. And going through the reason of death of those people, why those people die, we've been able to identify that [intelligent automation] has the potential to save about 10 million of lives every year. And the key use cases of intelligent automation, helping reaching that goal are clinical trials, supporting the clinical trials, disease diagnosis and thirdly, avoiding medical errors.
Medical errors today are a lot, I mean, contribute to deaths. And on top of this, especially in developing countries it can enable remote diagnosis. Our world has a shortage of about, of more than four million physicians, globally, okay? Being able to use an application on a phone, think of the equivalent of Babylon, for example, that is well used in the UK, would help to support a lot of people, especially in those developing countries that don't have access to a doctor. And I like to give this example, did you know that today in our world more than 1.5 people, 1.5 million people die every year from diarrhea? 1.5 million people die everywhere every year from diarrhea. So that's a preventable death that is easy to manage, but when you don't have doctors, people don't know how to do.
And so having the capacity to have an app, for example, that helps remote diagnosis supported by AI would help to solve this kind of issue. I like us with another example, which is Tissue Analytics. Tissue Analytics is an app that everyone can use that instantly diagnoses chronic wounds, burns or skin conditions just by taking the photo from a smartphone, okay? Again, here it's a great, great impact to lives.
Amazing. And I think you do an incredible job in the book in building that bigger impact picture. I mean, maybe it's my prison, where I spent a lot of my time, care and energy in improving performance and levering the best technologies to looking at one problem, right? Which is a supply chain, finance, customer experience. You brought a very different perspective to the story and you should show how ubiquitous the impact of this new field of science is. So thank you very much for that. Any last thoughts before we wrap up? I think we're getting to the end of our session today.
I think we've got all the [crosstalk 00:47:56] we've covered a lot already. Yeah, read the book. I mean, something very important as well is the profits from the book will be distributed, donated to charities in charge of helping people to get used to new technologies, especially people in need.
Very nice, very nice.
So the reason why we've written this book is really to share a message, to convey a message to a maximum of people around the world and help us donate to those charities and bring this further.
Well, look, it's been a real pleasure and privilege to spend this time with you and to get to know you over the last few months. Thank you for the book. Thank you for your research, thank you for your years as a practitioner and really trying to bring that broader perspective to those buzzwords. I think you're kind of putting the buzzwords away and going much deeper, so much appreciated. And then I'll flash on the screen where people can follow you. I know, as I mentioned, you got hundreds of thousands of followers and for all the good reasons. Pascal, thank you so much. Enjoy the rest of your day, wherever you are. I think you said you were in France right now and we'll stay in touch. Thank you.
Thank you very much, Fred.
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