Watch CCC’s September NXTUP⬆️ LinkedIn Live Episode
CCC’s new LinkedIn Live original content series NXTUP⬆️ premiered last month featuring our first guest, business futurist and best-selling author and influencer, Bernard Marr. Co-hosting the event were CCC’s Chief Product Officer Shivani Govil and Vice President of Ecosystems and Alliances Manju Bansal. This episode featured a lively discussion around important topics relevant to the P&C insurance industry, including digital transformation, business innovation, the role of AI, and of course, the growing complexity and autonomy of vehicles. Catch the replay (or read the transcript) below and be sure to follow us on LinkedIn for future episodes!
Shivani Govil: Good morning. Good afternoon. Good evening. Welcome and thank you for joining in to our new series. Next up. This is our inaugural session and each month our aim is to bring in guest speakers, visionaries and thought leaders to talk about different ideas and big topics related to the property and casualty insurance industry.
So quick introduction, I’m Shivani Govil and I’m the Chief Product Officer at CCC Intelligent Solutions and I’m joined by my colleague, Manju Bansal, who leads ecosystems and partnerships, and together we will be tag teaming on the series. Our aim is to provide thought provoking discussions and spark new ideas, so our first episode today is on digital transformation, which is a big topic and our guest joining us today is Bernard Marr. Welcome, Bernard!
Bernard Marr: Thank you. Thank you so much for having me. I’m really excited to be on your first episode.
Shivani: We’re so excited to have you as well, Bernard. And for those of you who are not familiar with Bernard, he is an industry leader, a visionary, a futurist, and a very well-known authority on the topic of business, digital transformation, and the impacts of digital transformation on business. He’s the author of over 20 best-selling books. He has his own column on Forbes. He has 2 million social media followers and over 1 million subscribers to his newsletter. In addition, Bernard is ranked one of the top five influencers in the world of business and transformation and the top influencer in the UK. Wow, Bernard that is so impressive.
Bernard: Thank you so much. It’s a great introduction. Yes, it’s super exciting. I feel very humbled that I am able to write content that people want to read about and learn about and just the last few months have even increased my profile. So, I now have one and a half million subscribers to my newsletters, which is amazing. And I just love it. I love my job.
Shivani: That’s great. And it’s such an honor to have you as our inaugural speaker for this series, and really looking forward to the discussion. So with that, let me hand over to Manju to get us started.
Manju Bansal: Thank you, Shivani, and Bernard a privilege indeed. Thank you for joining us today. Thank you also to our online audience that’s tuned in to listen to this broadcast. Hopefully it will be inspiring enough. Just a couple of quick housekeeping notes. The chat is now open, so for folks in the audience, please do write your questions in the chat. We will absolutely take them towards the end of this broadcast. Also, don’t just restricted to questions we would love it if you would have your comments and thoughts as well that you can share online so we can drive the discourse forward. And with that, Bernard…
Bernard: Absolutely, and another nice way to interact with the audience is for them to share where they’re joining us from as well. That’d be fantastic.
Manju: So Bernard, the first question for you. Shivani mentioned in our introduction, right a lot about “big picture” macro trends, business and technology, and this digital transformation topic seems to be front and center these days. You’ve seen the numbers, and I read an IDC study that said by next year, 2023, a full 53% of the entire world’s GDP, which is in the trillions now is going to be contributed by organizations that have digitally transformed themselves. That is incredible. How do you define digital transformation in this context and where do you see this going? And one more thing, how do you differentiate it? From all the investments in ERP, CRM mobile applications that companies have already invested in?
Bernard: Really good question. And I actually don’t like the notion of digital transformation because, for me, it feels like a one-off exercise we need to do to our systems are not quite up to date, so we need to transform ourselves digitally update our systems and hey, we have digitally transformed. What I’m seeing at the moment is that we are going through a stage of continuous accelerated innovation and new technologies coming along at a pace that we’ve never seen before. Currently, we’re talking about the fact that we are in the fourth industrial revolution. So, all previous industrial revolutions were nicely spaced out. We had steam, we had electricity, we had computers, and I would argue that this fourth industrial revolution is driven by artificial intelligence and intelligent systems.
But what we’re also seeing is huge other technologies like virtual and augmented reality, blockchain, cloud, 5G, intelligent robots, all of these crazy technologies all enhancing each other, and I can’t really see how we would say, there’s another single technology that drives another Industrial Revolution. What I see is this hyper evolution, where there are lots of technologies influencing each other so if you think about how AI now helps us to create virtual avatars and helps us to write texts and help us to compose music, and it’s so many aspects are affected by it, and then we have secondary technologies that are not necessarily seen as the digital technologies where we have gene editing and nano technology, that are also completely transforming our planet and they will become increasingly controllable by our digital world. If you think about things like 3-D printing – so, I work with some of the race teams in Formula One – and what they are doing is they will create a digital twin of a race car so a digital copy of it and then they take this digital race car through the digital printer and they will run it on a simulation on the racetrack that they are going to perform when racing on the next week and then they will optimize all the different components of the car like the various components that you put on that and then they press a button, and they will 3-D print those components.
So, what we’re seeing at the moment is this merging of the digital and the real world, you’ll be seeing something similar in gene editing where we can now digitally change and almost copy and paste components of genes to change plants to change animals and potentially change humans. So, all of these combined mean we will have faster, more crazy digital transformation happening and what this means for organizations is they have to very carefully think about how they are organized, they need to be more agile. They need to be in a position to embrace all of these technologies and run with them much faster than they ever needed to do this before.
Manju: And where does data come into this whole plan? Imagine that data would be sort of the foundation for enabling all these technologies to rapidly evolve and make an impact in the real business world? Where do you see that game playing?
Bernard: Yeah, and your assessment is absolutely right. Data is the foundation for the new Fourth Industrial Revolution. We look at some of the key technologies we’ve just talked about. Data is enabling them. We wouldn’t have AI or machine learning or extended reality or blockchain or any of these technologies and then we have other technologies that are enabling the capturing of data and the transmission of data from 5G to cloud to sensory technology, the Internet of Things, and what this means is that data is now moving right, into the center of organizations strategy. And I believe firmly that today, every single company on the planet has to be an AI company.
AI is the most powerful technology humans have ever had access to. And the soil or fuel for AI is data as well as – I like to use the word soil, because fuel is slightly outdated – and soil gives us this metaphor of actually we’re growing something from the Earth when we need to nurture our data and use it to grow something out of it and add value. And I feel lots of organizations have not really understood this yet. They haven’t really understood that data has now become the number one business asset alongside talented people, and when we talk about talented people, there’s a huge skills gap about in the area of data science, data literacy, being able to use data effectively and there’s just a lack of awareness of the importance of data and, and a real challenge for many organizations, especially in the industry that you’re involved with, and that we’re talking about here today. The whole automotive space, the insurance space.
Those organizations have been around for a long time. They have huge challenges in terms of updating some of their legacy systems and moving some of this to a more modern data architecture that lives in the cloud, where you can simply once your data is in the cloud, you can access a lot of those amazing technologies that I’ve just talked about. So, if you wanted to have artificial intelligence, or machine learning algorithms that allow you to process natural language, or that allow you to do machine vision technology to recognize images, you can simply switch these on as a service once your data is stored in these modern cloud architectures. So, a real challenge there on so many different levels.
Shivani: Thank you so much. I wanted to go back to something you said earlier about all the different technologies and the pace at which these technologies are coming out. And you know, there’s this there’s a talk that I really ascribe towards which is the change in pace and technology is so rapid today. And tomorrow. It’s even going to be even faster than what it is today.
And as you think about all this technology proliferation, you started touching upon the industry and the automotives and you know, it feels like the automotive industry is at the forefront of a lot of this technology proliferation, whether you think about autonomous vehicles as capabilities connected cars. You know, I grew up watching a cartoon called “The Jetsons,” which always sparked my imagination, and that’s not so much a sci-fi fantasy anymore. It’s you know, we’re seeing this become reality. I know you’ve written about companies like Whisk that are working on flying cars. I would love to get your thoughts on how do you see these digital technologies impacting the auto landscape? I almost wonder, is it a partnership between Detroit and Silicon Valley? Where do you see this going?
Bernard: Yeah, so I believe that the auto industry and all the industries around the insurance repair as the service providers going through one of the biggest transformations that they have ever seen, we’ve literally had very little complete change in this industry. For the last 100 years. We had four wheels with a combustion engine, and we had a chassis and now this is all being completely transformed.
One of the biggest challenges we are facing as the world is climate change, and the whole transportation sector is still a huge contributor to carbon emissions and is not sustainable. Right now. So, this is obviously a huge priority in terms of moving from combustion engine to a decarbonize transport environment where we have electricity, maybe hydrogen for ships and trucks and planes. And also, where our electricity is actually coming from more sustainable sources like wind and solar but also nuclear fusion, for example, which I believe has huge potential for our planet.
On top of this, we have all cars are becoming intelligent and they are slowly moving towards a self-driving capability. And what this means is that car companies have to be AI companies they have to be data companies, because if you think about the future value of a car, the main value will be in the technology and in the capability to use data and AI to not only deliver to not only make the car safe will be at the moment, but also completely autonomously drive in the future, but also to deliver completely new services and entertainment packages. Because if you think about this, if our cars are autonomous, that means we will do something else when we’re in the car so suddenly entertainment becomes an important part of all of this. And so, for me, this whole move towards a greener decarbonized environment, a move towards a more intelligent environment. And with this, move towards more intelligent potentially autonomous cars, what we’re also seeing a servitization of the industry, we move to in completely new models of ownership where people don’t buy cars anymore.
Our generation might be the last generation where cars are probably the second most expensive thing we will ever buy. In the future, we will just buy mobility as a service where a car, maybe a self-driving car is a component of it, but it might work public transport might involve scooters, it might involve flying cars and autonomous drones and many other aspects. So, this again, has huge implications for the industry and they at the moment, until now, the car industry has built relationships with end users and because they are the main customers, this is going to change in the future. So, and again, they need to start thinking about the difference between customers and consumers. And when I did some strategic work for Mars, for example, they distinguish between this very carefully that we have a few.
We have customers that buy our products on the shelves, and they eat the Mars bars and hopefully get lots of pleasure from it, but then we have our customers the supermarkets and others that buy or products and both of those have very different value propositions and this is something that the car industry really needs to get to grips with and this is changing.
Manju: So, I want to take you back to the AI piece so slightly, so you know the old saying that Software is eating the world. And AI is eating software. And now it’s almost become that AI and by extension of course the larger technology framework is becoming a very critical component of success. No matter the industry you’re in. In fact, we ourselves at CCC ONE have lots of predictive AI models in the cloud to advance our insurance business. But AI of course doesn’t come without its question marks, for lack of a better word, right? There’s ethics, there’s fairness, there’s potential job losses… how do you see ethics? With AI scaling up, what kind of things should organizations be thinking of as they grapple with this and start adopting and mainstreaming this technology as a critical component of what they do?
Bernard: Super interesting question, and I could talk now for the next three days on different aspects you’ve touched on. So, I think it’s sometimes important to actually understand where we are with AI at the moment and that AI sometimes is seen as the magic beasts that were able to supersede humans take over the world take off, take all our jobs. This is not what I’m saying. And there’s a really good book that I’ve read recently called “The New Breed.” It talks about intelligent machines and AI in the same way we talk about our domesticated pets. They are able to do things we can’t do. We have sniffer dogs that can smell drugs that we humans can’t. So, they have amazing capabilities and we’ve worked with them and what we now have as we’ve had AI for the last 50 years, we now have these amazing amounts of data, and we have more advanced algorithms that can use this data and this gives these AIs amazing capabilities that supersede human capabilities on so many different dimensions.
For me, a really good example is healthcare and radiology. So, we now have machines that are developed by Siemens and GE CT scanners and X ray machines that are able to not only take the images but analyze those images and interpret them so they can identify cancer cells, they can identify broken bones without any human involvement, and actually machines are so much better designed for this because if a human radiologist sits down and looks at the scan, sometimes they’re tired, they’re stressed they just had a bad day. And also as a human we have the problem that for example if I am analyzing a scan, I look for a broken bone I might not be able to find anything else because I have this focus that I have on finding what I’m looking for.
If I give this to an AI, the AI can analyze an entire CT scan. It will not only identify broken bones, but potentially lots of secondary diagnoses, so work and then EEG for me, what we have to think about is for every single job in the world at the moment, we need to think about how do we split this job between intelligent machines and humans. And this applies to the auto industry to the manufacturing side to the distribution side to the servicing side, on the insurance side, all of those different aspects. We think going back to the radiologist example, should this person really be sitting there for five or six hours a day looking at images trying to figure out whether this bone is broken or not? Or should they use this amazing human capability that we have to maybe do something that is more human where they think about okay, now I need to talk to a patient I need to explain what’s wrong with them. I need to think about a treatment plan. I need to think about how do I further the research and make even better systems for the future. So, this is these human skills like creativity and critical thinking and emotional intelligence. All of those things will become much more important in the future, and actually some of the more mundane things that we can outsource to machines we very often should because we humans have these amazing capabilities and we’re not using them. We’re almost dumbing ourselves down and if we look at, honestly, it’s our job and think about okay, which bits can I give to machine thinking? So it’s really important to understand where we are today.
The second question was around ethics and absolutely, a hugely important element of artificial intelligence. There are lots of challenges, because traditionally, we’ve just used data and fit this into an AI to learn and if we the data is, of course data from the past because we kind of made them from the future and this might be biased in lots of different ways. So, for me, if we took all the data of past presidents of the U.S. and then fit this into model and say what should the president look like? We will find another middle-aged man in the future. Now people are becoming more aware of this and saying actually there is potential buyers and maybe we need to counteract this we need to better understand what parameters we use and I mean, that’s a really interesting challenge, because we think about how AI is evolving. Now, we have what we call machine learning. Okay, so we, instead of writing a piece of computer code that says you take this bit of data, use it in this specific way for an outcome.
This is something we’ve had for 30 years. We’ve had the ability to recognize for sample handwriting on an address label, for example, or something we put into the postal system. And because we know the rule is I know what A looks like or A B and C looks like and then I can create a statistical model saying this is likely to be in a B or C and then those work because I understand the rules. Where machines have struggled in the past is where they can’t explain reports where we talk about tacit knowledge, so things that we have learned through experience. I can’t explain how I how I’m able to ride a bike, I can’t explain. I can’t just write down this is how you ride a bike and then give this to you. You read this instruction and then off you go. In the same way. We don’t understand how we are able to recognize an animal on a photograph or a member of our family on a photograph, but we are extremely good at this. And what we’ve done is we’ve learned this over the years from experience. So, we have this amazing new neural network in our head that is formed of billions of network connections and when you sit down with your children or your when you were a child with your parents, and they say this is a giraffe and this is what a tiger looks like and this is what a snake looks like. We then form these connections, these neural networks, but we can’t actually explain how we do this we now have the same with artificial intelligence. We now have blank neural networks that we feed with data and they are not able to give us an answer. But again, they don’t know how they do it. And they’ve been trained on the data that might be biased and might be running.
So, for me a really interesting example that illustrates this is when DeepMind a company that was then acquired by Google wanted to develop an algorithm to play the ancient Asian game of Go and we’ve had chess playing AI for a very long time, but Go is more complex. It’s a billion times more complex than chess, and no computing power in the world can be used to calculate all possible moves. And some of these, go grandmasters are held up in huge esteem. And they are also they see themselves as someone that plays intuitively, they can’t actually put into words why they make certain moves, they just feel right. So, the first version that DeepMind created was an algorithm that was fed by data of all human games that have ever been recorded. And this system became amazingly good at playing golf, but then they thought, “Hey, why do we limit ourselves with all the information we have about humans? How about we give the system a complete blank neural network and only the rules of the game and the end-result you are aiming for?” And then we put two neural networks against each other to play.
And these blank systems very quickly became much better than anything that has been programmed by humans because they learn from their own experience. And this is possibly the first time when AI became creative because when Lee Sedol, who was then the world gold champion was competing against AlphaGo. The system made a move that actually made Lee Sedol laugh, he thought this is crazy. I’ve never seen this before. And some of the commentators said okay, this is obviously a great example of computers not working yet, but maybe we’ll get at some point.
What became very clear halfway through the game, they said this was the winning but no human player had ever played this before. And this is the stage where we are now with AI that we can generate new content creative content, we can generate images that have never existed before we can generate books and texts that have not existed before. And this will have real implications for how we will split the work between humans and machines.
Shivani: Bernard, those examples are fascinating and it’s really interesting to hear where we’ve come with the technology and you know, I think AI is something that we’re talking a lot about and the other technologies as well that impact the industry in the different organizations today. I’m curious to get your thoughts Bernard, you know, technology is one part of it, but what other elements need to come together in an organization to be able to successfully deliver some of these transformations that we’re talking about, you know, culture, talent, appetite for risk, like what are your thoughts around some of those other elements that are equally important?
Bernard: All the ones you’ve just mentioned. So, for me, the key component of getting digital transformation, if you want to call it right, is a strategic approach to all of this. I feel that lots of organizations look at it. From a technical perspective. They say we need to read platform to the cloud. We need to implement blockchain technologies we need to experiment with Metaverse technology. Actually, this is the wrong starting point. What we need to do is we need to look at this strategically and say, “Where is this industry going? What will this mean for us as an organization? How will this change our value proposition?” And therefore how can we use these technologies to become more successful in either making, having a delivering a better service or product to our customers or driving internal efficiency.
So, those are the two things we need to look at first. Once we’ve done that, then for me the three main components that we need to look at technology. So, we then need to think what AI do we need to use what cloud systems do we need a private 5G network in our factory, whatever it might be. Then the second element is skills. We need to then really carefully think about, “Okay, what does this mean for that skill mix that we need in the future? How do we access the talent with the AI skills and the data skills and the metaverse skills and everything else we need in the future?”
And the third one is organization and culture. And when I look at all of these three, it is culture and talent that is far more difficult and far more important than technology is something I can just write a check, get someone to implement it and we are ready to go. The talent element is becoming increasingly difficult for me we’ve now talked about this war for talent for had another 15 years. For me, we are now entering a world of the war for talent on steroids where we’ve gone through COVID. Lots of people have reevaluated their careers. We’ve seen the great resignation. And there’s a real shortage of talented people. And because AI is advancing so fast, all the existing workforce, we have to think about auto manufacturers, for example.
The people that they employ, they need to retrain them, the skills they have at the moment in terms of building combustion engines will no longer be nice. And they need to make sure that they give those people an opportunity to learn and develop and develop those really human skills that we need for the future. And for me, the talent element is very closely linked to the culture and the movies as well. I just this week, I was with an insurance company, and I was telling them a story.
I’m in Dublin right now and giving a keynote here tomorrow. And I remember maybe five years ago I was giving a keynote to the top insurance leaders of it was a big dinner. And I was talking about some of these technology trends. And I felt that the industry wasn’t really ready for this yet. They were not taking it seriously. And they were all looking around talking to those who are using AI. Are you interested in the Metaverse and they will not doing it? Are you doing it? And then they’re all reassured each other saying actually it’s not really we’re not ready yet. But it’s really interesting to know that some of these things might be there in the future. And then we have companies like Amazon and Tencent and others sweeping in potentially threatening huge parts of their market. So, this is really dangerous. And I was with an insurance company a few days ago, and they said okay, for us. We keep a very close eye on technology. We know that there’s some FinTech companies we’re watching and we will simply acquire that and I then touch them. That is a nice idea in theory. What do you think those people working in a FinTech company would want to work for you? Because if you look at those two cultures and the working environment, they couldn’t be more opposed.
And I fear that lots of organizations in this whole automotive world are still very traditional. They’re very hierarchical. They and what we need is we need flat organizations. We need more agile organizations where people can be their authentic selves where you have almost gig working where people bring skills they add to project teams, and then they dissolve again and they work on different things where they can really bring their skills and their personality to work.
I don’t think many companies in the space are quite there yet. And so this idea of simply acquiring another company that you can acquire the company, but those people might believe they don’t feel they fit in so this cultural challenge is probably and particularly the people challenge to go hand in hand. The most important element is that we need to get right
Manju: Wow. Fascinating. Thank you, Bernard. We are almost at the slightly past the bottom of the hour. Let’s take a couple of audience questions if you if you wish. So first question coming in. What are the top two or three common factors that are constraining companies from adapting newer technologies more expediently? I know you referred to some of the same issues a little while ago. Is there a correlation between the company age size or any other attribute that makes them more on the ready path in the adoption of these new technologies?
Bernard: Really good question. And yes, the smaller the company and the younger the company, the more likely it is to be able to adopt new technologies because it isn’t constrained by existing legacy technology it isn’t constrained by existing legacy cultures and skills and so on. So yes, size and an age health. Having said this, I have seen lots of large organizations. So for example, I think we’re in the banking space, another industry that is being hugely disruptive at the moment.
A Spanish bank BBVA they basically their CEO stood up, I think 10 years ago and said, we are now a data and AI company. And they have been on this transformation journey really successful. So for me, in terms of the drivers and what differentiates organizations is the strategic awareness. And the senior leadership buy in into all of this followed by talent, culture, and technology. So if you if you don’t have the strategic buy in and support, it will never happen. You then need to make sure you have the right people with the right skills, you have the right culture, and then you also can update your technology. But these are the key barriers.
Manju: So it’s a much more holistic approach. It’s not a one-trick one and you’re done kind of an approach, if you will, that’ll work.
Bernard: No absolutely and I think the key takeaway from today for any anyone is if you’re taking anything away from today, innovation and transformation is only going to accelerate. They are other amazing technologies that we haven’t even touched on today. Like quantum computing and others that will accelerate all of this. So this means we need to create completely new organizations and organizational systems that allow us to operate in this new hyper evolving.
Manju: And the second question that came in on the chat, but I think you partly answered it, but I’ll ask it anyways. There’s so many advancements in technology and methods to manage and analyze data. Is there a single piece of advice you can give to companies or more specifically in our insurance business, to say look, as you want to evolve from being a traditional like the people you met in Ireland at the fancy dinner into a more AI driven company? What would you do? I know you touched upon the people part of technology, the training, etc. But is there anything from an AI perspective? Is there something else they have to be thinking?
Bernard: For me, I’ve just written a book called “Future Skills,” because after every presentation I give people are talking how “Where does this leave us?” And, and I looked at the 20 skills that we will all need to succeed in the future. And the first skill I talk about is digital intelligence. And this basically means being able to understand some of these major technology trends. And think through the implications for your own industry and your own job. Everyone can do this. I just just this week I published a video on my YouTube channel about the five biggest technology trends for 2023.
And this is a really good starting point. Do you look at those technology trends and think about cable? What were those mean for my business? On your offer person you can look at it as an individual’s I was looking for my job for my future career prospects for my skills. How are my skills aligned with all of this and how do I have gaps and then on a macro scale, company needs to do that companies need to do that they need to do exactly the same for their own business plan and their own strategy to say, “Okay, how is the world going to change? How do we adapt to this? And therefore, where are the gaps?” How we get this is all not really difficult. So, when I work with organizations on creating digital transformation plans and helping them to understand how other industries are going to be transformed.
Just engaging with this dialogue, I think is a really important first step, and then thinking okay, Metaverse is just a fad, web three, and blockchain technology and NFT’s I don’t really understand, AI is for nerds. We need to; everyone needs to engage with these because they will change every business and I think the promise and the parallel. It’s a technology has bought for the more you are aware and educated about what it can do for your business, the better off you will be to succeed in the future.
Shivani: Sure. Can we take one more question from the audience?
Bernard: Absolutely.
Shivani: And I was just thinking it fits right in Bernard with your context of predictions and looking into the future. So go ahead, Manju.
Manju: So the audience individual asks how quickly we’ll be in your perspective get to the vision of cars solely in the context of mobility as a service as opposed to the traditional ownership model of cars faster than we think.
Bernard: I… the thing is with all of these technologies, there’s always so much hype. So I started writing about self-driving cars 10 years ago and people think, okay, they’re just around the corner. And then with every technology, go through this hype cycle and get hyped up and there’s a bit of disillusionment and then the real development application start they address some of the barriers. There are a number of barriers, but if you look of how pushy organizations like Tesla are with this technology, they are this week, they will announce their next evolution of the self-driving and autonomous driving technology and that advancements are so rapid, that I believe we will see this very soon. And I have recently seen a trial of a self-driving car company operating in Delhi in India, and anyone who has ever really or driven there have been driven now, this is probably the most crazy scenario you can ever imagine yourself driving in where you have lots of scooters and bikes and pedestrians and elephants and everything in between all driving as if there were absolutely no rules. On the road. And those cars can navigate pretty well. This for me is an indication of where this is all gone.
And also, not only will we have autonomously driving cars on the roads because we already have them we already have trials going on in parts of the US where we have self-driving trucks driving on public highways. We will also have autonomously flying drones. In places like Dubai they are building an infrastructure environment at the moment where an autonomous robot picks you up and takes you home with no pilot in it. And they had a virgin flight with the Crown Prince of Dubai, I believe six years ago now. So this is this all will come and then we had a setback to COVID and other things that made it a bit more difficult, but there’s so much investment going in and so many amazing benefits for lots of organizations that will come out of this that we will see this phrase.
Shivani: Wow. All this talk about the future gives me goosebumps and David for the opportunities ahead. I think we’re on time It’s time to call it a wrap. Bernard, thank you so much for joining us. This has been a fascinating discussion. And like you said, I think we could go on for days talking about some of these topics and I wish we had that time.
Thank you to the audience for joining us today. I hope you found it interesting engaging. Send us your comments. We will be back next month with another riveting topic and another engaging guest speakers. So please stay tuned to LinkedIn where we will post the details. And Manju thank you so much for co-hosting with me and enjoy doing it together. Pleasure. Thank you Bernard, really do appreciate your time today.
Bernard: Yes! It was such fun. Thank you so much. I really enjoyed it and hopefully we can do this again soon.
Shivani: Thanks, everyone. Have a great rest of the day. Bye bye.
