Jun 5, 2019 | 3 min read

Conversation with Sadagopan Singam

Podcast #61: Applying Global Lessons to Digital Transformation

Sadagopan Singam is Senior Vice President Global and Business Head for Cloud Native SaaS at HCL Technologies. Our conversation explored his experiences working to implement enterprise applications around the globe, and how the different paces of adoption across regions and key lessons learned have proven relevant for subsequent waves of technology innovation.  He shares his methodical approach to breaking down the key priorities needed to effect successful digital transformation, along with the impact of regulation and governance. The conversation explores the evolutionary processes that organizations need to undergo to be able to harness the value captured in their data, then apply analysis and insights to drive not just efficiencies but whole new approaches to their business. Finally, he shares his optimism for human creativity boosted by technology to help solve the biggest problems of our era.  



Blitzscaling: The Lightning-Fast Path to Building Massively Valuable Companies - by Reid Hoffman, Chris Yeh  

21 Lessons for the 21st Centuryby  Yuval Noah Harari 


We'll notify you bi-weekly about new podcast episodes, upcoming guests, and news. You can subscribe to the podcast and if you'd like to be considered to appear on the podcast contact us.

View Transcript

Good day everyone, and welcome to another episode of our Momenta Podcasts. Today as another episode in our leadership series we have Sadagopan Singam who is Senior Vice President Global, and Business Head CloudNative SaaS at HCL Technologies. Now, a little bit of context, Sada and I have been speaking for a number of years on different trends in the industry, and I’ve found him to be one of the most thoughtful and insightful people in the industry when talking about, certainly my prior area of focus at my prior firm, where I was very much focused on cloud transformation, SaaS software, but he’s got a wide ranging area of interests and a lot of super-interesting thoughts, and again I just want to say it’s a pleasure having you join us Sada.

Pleasure to join you Ed, it’s always a pleasure talking to you Ed, I am very glad I got an invitation to be part of this running podcast series.

Absolutely. Let’s start with a bit of history, could you share a bit about what’s shaped your view of technology, and share a bit of the path that brought you to your current role at HCL.

Like many I started during the .com period, those days we were focused on creating enterprises with the latest and the greatest of the trends and technologies. I slowly moved from creating different, into focusing on the business processes which took me to focus on the customer relationship management space, as well as the supply chain space, product lifecycle management space, so those are the days when all the pioneering enterprises were being started primarily out of the Silicon Valley. My journey more or less started around the same time, the only thing that I did was, I consciously took a decision to operate on a global scale which means it’s not only managing business around the world, but being there at the right places, being there in places where business and technology really intersect and make a massive difference.

So, I spent a considerable amount of time, close to four or five years in the Asia Pacific region covering all the countries in the region from Singapore to Australia, to Japan to Korea, to China to the Middle Eastern markets. What was an eye-opener for me as somebody who moved from the US to these markets was the scale, the range and the utility value of technology was much better leveraged in these markets. For example, 20 years back the take-off of similar technology advancements in Japan and Korea, and the rate of applications that we saw in the early part of the century, I knew this is going to be the answer to the global revolution, and that’s the reason I stayed focused on technology. I always believed that there’s a lot of innovation happening in the Western world, and there’s a lot of application of both innovations happening on the Asian side of the world, and I thought if we can sort of build our experience on the insides based on these two lakes, we’ll be able to uniquely capitalize on creating the best possible value for our customers. So, I’m based in the Silicon Valley, but I have a global reach.

I joined HCL almost 9 years back to have a charge to drive the cloud business, from there I went to drive the global business consulting business, and then the portals, the account management, the enterprise front, and the front office technologies. As many of the enterprises began to adopt to a fast and very aggressive and highly-scalable way, we realized the need to have a leadership mission about how to create and deliver the right sort of fast technology and processes for better businesses for the customers, and that’s how I reached this stage in HCL where I had the charter to drive all the staff business for HCL globally across the customer base.

You made a really interesting comment about the different pace of adoption from your time in Asia, and how that applies globally. I would love to get your perspective on how the advantage of being a later mover, can provide lessons that allow more rapid adoption of newer technologies. I think of in some emerging nations, for instance in Africa where they skipped landline and went right to cellular technologies, and if you skip the big heavy on-premise client server technologies you can go right into cloud. What were maybe some of the key experiences and lessons that helped shape your global view of technology adoption?

Absolutely, you are spot-on Ed. Many of the emerging nations they skipped multiple layers of technology evolution, and they were able to join the global movement with the latest and greatest, which means that some cost is actually lower. But at the same time it also comes with a lot of difficulty in taking those technologies to customers, for example, how do they create a building solution, let’s take China Mobile, or A-Tel in India, how do you create a building solution with just some [inaudible 06:54] that all the regulated needs of the respective countries, and hundreds of millions of customers? How do we onboard 25 million customers every month? These are all challenges, some of which the world has not seen at the time in the developing world they’re trying to adopt on these new technologies.

So, I think the key difference was the Western world has been very super-sticky, their focus on customers, their focus on excellence of service to customers, their ability to be customer centric in their thinking, their ability to put the customer at the forefront of the business decision-making process. That was a great learning for the Asian countries where traditionally they’d been demanding supplicant shrine and therefore the supplier always had the upper hand. But then this mass explosion of technology and outreach happened at the scale that they’d never seen, they began to realize the need to have customer central stage, they began to understand. The key elements of success that the Western enterprises drive, they’re centered around the customer and how they’re able to bring it there.

However, the ability of a localization that the emerging nations were able to do, there’d be something that the Western nations probably had an opportunity to learn, after the fact that if you went to road load something in China or India, or even Africa, each one of the equivalent of the American provinces have a different culture, different bibulations, different language, and sometimes different mechanisms of their new collection. So, all those meant there are multiple business models that got big inside, that was the key element of the next wave of innovation, the Western setup and provided a base. So, it was a complete loop, obviously the West has a clear edge from this edge as it mostly is, you hear a lot of learning which again is fed back into Western nations’ learning, and which helps you to create new wave of innovation. I think this has been a tremendous learning process, very immersive process, but extremely full of learning and extremely full of new layers of experimentation that got big in size, providing superior levels. It’s a fascinating journey Ed, so to say.

Yes, it’s interesting you bring that up, as when you started looking at certain processes that become cloudified, or cloud enabled, or SaaS enabled, and I think now we don’t even think about the challenges involved with moving processes to the cloud. I’d love to get your sense, just looking across the enterprise today, and going back to some of the initial work you did in driving cloud forward; what are some of the business areas that you find at this point are firmly committed to cloud innovation, and others where there may still be some greenfield ahead, or even opportunities for even conversion of on premise solutions?

I think the biggest change in opinion is one of security, and the biggest pivot is around the relations. A typical example, in the early part of this decade when I used to travel in places like South Korea for example, Incheon; the Incheon airport is also a trial, and every time you take the airport bus and go to the city, you see that in those buses they are completely Wi-Fi enabled, and I’m talking about 10 years back. That time it used to be something pretty amazing to see, that it was completely Wi-Fi’d and you’re able to start work from the time land there, and on the journey,  you get to complete so many things, before you freshen up and go to the first business meeting.

But today if I were to do it again, I would first ask the question,

  • Is this secure?
  • Does it comply with all the regulations?
  • How is this data being used?
  • Who is capturing all this data?
  • What’s the chance that this data is only between me and the provider?
  • How does it change the profile that I have with the online service providers, if I begin to use these services?

There are so many questions for which we seek answers before we just say yes to connecting to public Wi-Fi. I think that is the biggest change in my opinion, one is security, and second is regulations, for example…

  • How much of a country specific data, region specific data?
  • How is the data captured?
  • How are they processed?
  • How do they retain it?
  • How is it deployed?
  • Who is the master of all this data?
  • Can I get back all the data that they held to populate?

So these types of questions and advancements that the Western companies are made today, these are becoming global examples, and I think this is advancing the state of the art to the point that you can’t have an enterprise deployment without having answers to these tough questions. I think that is the real progress if you ask me Ed, over the last few years.

And from your perspective, GDPR certainly had a lot of impact on forcing companies to take inventory of their data management, their data security processes. From your perspective, and now I believe it’s over a year since GDPR has been a formal requirement, is there a clarity of having a GDPR-type regulation at a global impact, and are you seeing GDPR being adopted as a global standard?

I have mixed views on GDPR, I am firmly on the side that data needs to be regulated, and data needs to be regulated to the point that there is a clear distinction-definition of where the ownership is, how it is processed, and somebody has to take responsibility of the data through its entire lifecycle. So, to that extent I clearly welcome GDPR, I welcome data regulations-centric issues. But I think GDPR has gone pretty rigorous in my opinion, I don’t really know whether taking the regulation to that level, is it really going to benefit the customers? And I would like to see some formal studies coming out and saying, ‘Out of GDPR these benefits, A, B, C, D, outcome’. It looks more like the power of the union disorder can bring together a number of prosperous nations, and four standards, that is what has been demonstrated. I would like to see what specific benefits GDPR has driven to, in consumers and technology companies, and to society as a whole, that is something I would love to see. But I’m firmly of the side that data cannot go unregulated, there cannot be an invasion or unregulated use of data, and there must be ownership of data throughout the develop cycle, but if GDPR is the only model, I’m not too sure I’m allowed to say what results are coming out of GDPR for this, before I take a view on that.

I’d love to get your perspective given that there’s been so much pushback, with you being based in Silicon Valley, recently there’s a lot of pushback against Google and Facebook in particular, I don’t know if you’ve had chance to read Shoshana Zuboff’s book, ‘Surveillance Capitalism’, but I think there’s quite a bit of concern about these business models, and I’d love to get your sense in terms of how these dates are playing out, particularly with the enterprise customers that you’re working with.

I think by and large the enterprise customers are very comfortable, so long as you have in situ data, so long as they’re able to conform to an existing regulation, local regulations, I think the enterprise customers are fairly happy. The only thing they want to make sure is, all these regulations are sort of big inside the softer model, so that whenever there is an update required, it happens automatically, but this surprisingly is what SaaS provides, the ability to sort of provide single instance solution around the world that actually creates a big plus-plus for the enterprise, they’re quite welcoming.

However, coming back to the larger issue of Silicon Valley versus the whole world, I think the key thing that we need to look at is, the speed at which the developments are taking place. For example, there has been a recent Facebook data breach saying some of the employers have been able to read the passwords internally, but it is never misused. Now when I looked into many of the enterprises that we work with, even from advance financial tech customers that they work with, when they reviewed their process, this has been a standard process across multiple industries.

So I think the answer to this is, the state of the art has to improve across the board, it’s not correct only to make all the Silicon Valley companies feel guilty about it, it is something that as an industry we need to come together and define what the state of the art is, define what the standards are, and if they can focus on defining those technology standards and create that body of knowledge, and say that this is the minimum bar which we expect of any enterprise operating globally, then I think it would make sense. But if all the practices are the practices that exist across industries, but only singling out Silicon Valley companies for that, that may be taking activism to other extremes. That’s what I feel Ed.

Yes, it’s interesting that you bring up standards, we’ve had a couple of conversations on our podcast about the challenges, particularly if you get outside of traditional IT, into operational technologies for instance where there really are not a lot of defined security standards, there’s FIP certifications and that sort of thing, and then of course just the challenges of managing the interplay, and multi-directional flow of data across multiple systems and clouds, its enormously challenging. I would love to get your sense if we go a little bit deeper through the conceptual side into practical implementation of data governance, what in your view are some of the most useful technological advances in recent years, as well as some of the areas that you continue to face as big challenges to address from more of a technology perspective?

I think the enterprise data lakes that is a backbone on which many of the advantages as possible, just happen. I think we need to get it right, the way we see this, if you’re able to look at your data from a monitoring standpoint, if you can score 10 out of 10, and if the everyday of the enterprise to create in faith out of that if it is less than ten, and then once you reach that level of maturity you go to optimize the data, and that’s a tougher challenge because that means you may need to have analytics to optimize the key business processes that either throw data out, or consume data, and that gets delivered to our customers, friends and employees, partners and channels. So, if you get the optimization exercise correct then we can see how these are used for monetization, for example how we leverage, how do we convert this data inside into products, into services, into markets, into channels, into audiences and partners, both too and fro? Then I think if you get 10 out of 10 in all these four different things, then I think enterprise has already evolved.

What I see typically today is, less than 20 percent of enterprises, big global enterprises, I’m talking about less than 20 percent of them already typically score 10 out of 10 in all these four phases, and after this comes the metamorphoses, essentially completely transforming your company into analytics and light and business enterprise where the data sort of feedbacks to create the best possible enabling model for driving new layers of business.

So, I think the answer to this is around if somebody is able to visualize from an enterprise standpoint, how do they monitor the data? What are the insights they can get? Are they fully satisfied with the type insight collection and application of those insights, then I’d be able to optimize the data derived, data like technologies, what are the enterprise in our data standards that we have, and how do we define governance at the enterprise level at the local business level? If they get all these right, and if they’re able to transform this to a monetization paradigm, and if they’re able to do it successfully across multiple channels, product lines, and service lines, and geographies, and business entities, then I think that is the one state that enterprises want to get into, that helps them to create the ability to use the data to keep improving the business. I think this is the prescription that they typically have, this is the framework through which we move data enterprises from left to right, and we see that as I mentioned earlier, less than 20 percent of enterprises have been able to get past the third gate here, and that is where we see a lot of opportunities today Ed.

That’s a really interesting insight about how far along enterprises are, and I think what you’ve articulated really is the broader concept of digital transformation that incorporates technology, as well as rethinking the business, rethinking monetization models. I’m interested from your perspective, this third stage, what are these obstacles that are the most difficult to overcome for organizations, to move past the initial instrumentation of their processes, and putting in the technologies to get closer to the data enlightened state?

Sure. Let me briefly unwrap the technology elements that I mentioned previously, and then go back to your question. I think the relevant set of technologies that bring the differentiation are data sciences, artificial intelligence, mission learning, data lakes, IoTs, blockchains, all have transforming abilities. Now the question is what are the hindrances, what are the limitations, what are the inhibitions? Clearly this spans out at three levels…

  1. The changes in the change management level.
  2. At the process level.
  3. At the scale levels.

So, change doesn’t change, we all know that. The old paradigm of soft is hard, and hard is soft, it holds true even today. So, given the multiplicity all these technologies have across enterprises, the fear and the possibilities also are very unrealistic in terms of visualizing. For example, somebody who is running a department is very worried about how his hierarchy, his stand of influence is going to get curtailed. Or, whether the powers that he has will they become small or become non-existent.

Imagining you touch a button, and everything will be ‘Hey Presto!’ i.e. the ability to communicate to customers at all channels, at all levels, and at real-time, and being able to collect the data, feed it back and make decisions. So, we see the expectations at both ends of the spectrum are too daunting to share. So, therefore it kind of creates a fair psychosis, an element of fair uncertainty and doubt, and that’s where change management is the most important issue here.

The second pillar on which everything requires someone is processes. For example, many times we see that the enterprises want to help processes really find the best interest of the industry are the other extreme, people want to emulate extremely diverse industries and processes, and try to bring in that level of coverage. For example an insurance company trying to get the same level of service which, let’s say, a quick service restaurant can do, and that’s a far cry because it’s going to attach multiple levels of processes, it has got to attack multiple legacy systems, and look through multiple governance and multiple levels of integration, before it throws back the results. So, therefore they need to be realistic from a process standpoint as to how much you want to stretch, and once you’ve gone to that level, how much you want to stretch more, and how do you want to continue the journey. So, defining that process means you’re delayed, and something is going to be a lot of work.

The third is skill, this is absolutely the biggest differentiator today. A small company with very little infrastructure, with very little experience in the field, can go and knock the big titans in the industry, if they get the scale element right. For example, a company like Warby Parker which was completely built out of rented technologies so to say, is they can go and give competition to the likes of Luxottica, then we talk about a small obstruct coming in completely, approving the edifice on which large enterprises are built, which is longevity, customer loyalty, as well as the brand value. So, therefore to think scale, if you want to do it now, can you do it across the globe, across all the channels, across all the service mechanisms, can it cover all the processes, and these are all big changes for some companies to even think of, and those three in my opinion of the key inhibitors towards adopting successful digital transformation in the whole inter-lifecycle journey Ed if you ask me.

That’s a great example too, and when you highlight Warby Parker, it’s kind of a vertically integrated small company, but Luxottica, I don’t know how many people appreciate that this is the company that dominates so many of the brands in eye-glass frames, I think close to 90 percent of the market is dominated because they’ve bought up the market, in a sense the mark-up is enormous so it’s incredibly profitable. They’ve got brand scale, but for a company like Warby Parker to compete, the availability of globally scalable technology resources has never been more accessible to startups. I’d love to get a bit of perspective on firms that have done it right, and if there are any lessons, or common characteristics, of firms that have really managed to become winners in the inner markets, whether they’re a start-up, or reinventing themselves.

Absolutely, like Warby Parker, we can talk about Stripe, just taking the digital payment processing market by storm. Likewise, Alwango which actually is focused the diabetes control market. So, what I see is, first of all it is vision and the ability to think big, and think aggressive, and think regardless of the entrenched competition and the entrenched big guys out there, we’ll still be able to make that mark in the marketplace, and work relentlessly every day, every hour, and these start to make all the difference. So, they’ve obviously got to take a few calls, one is they need to think global scale, or they need to sharply define the markets where they want to focus, so they don’t make any mischiefs.

Secondly, get all the technology ducts aligned extremely well, and also get the right domain knowledge, because most of the industries, all of the biggest and multi-competition in several industries are regulations. So, we need to be on the right set of regulations, we see this happening in every industry, whether it is from payroll providers, to payment processors, to consumer goods companies, to consumer in non-durable companies, to quick service reference, this is completely enormously exhaustless. And we see that there is no industry we can talk about except probably an energy industry, or grid industry, barring those, I see that across allindustries that are very promising upstarts that are really threatening the dollar value stream of the larger customers out there. I think they are actually leveraging on the ground from progressing service in a very substantial way, leveraging their ability to hyper perform for the customers, leveraging several of the AML techniques that are available mostly as APIs, and make those big distinctions.

I think their ability to be innovative with the customers, their ability to respond to customers very quickly and often in a very critical service, but in a simplistic manner, that sets them apart from all the big players who have their own ways of defining what value means to customers. I think that’s the big shift if you ask me Ed, the customer centricity ability to make things available to customers in the most simplistic form, and in a way that the customer wants, and make it available in the easiest for to the customer, that is when the small players are able to approve the established and respective fields.

That’s great insight, and you alluded to machine learning and AI, and I’d love to get your sense, you’ve been pretty deep into analytics technology and BI, but the impact on AI has certainly caused or created a lot of buzz around it, and there’s an enormous amount of investment. I’d like to get your sense of what’s changed in terms of the impact of the technology, the pace of it, and where the perception in the market is correctly aligned with reality, and where there may be some misperceptions around AI.

If you look at all the changes this AI machine learning has been able to bring inside enterprises, these are extremely-extremely impactful. I see a recommendation, for example you have all these data sets coming from the customer, from sales, from content, from different channels, you’ve got a great enterprise and a data lake set up, I talked about the possible data maturity journey an enterprise can take; once these are all in place, how do we make the best use? How do we know pump it out to the customer, and understand the responses and use those insights that you get back from the customer to improve enterprise? That’s closing the loop. And here I see the AAM techniques are really-really advance states of the art, for example the recommendation, the ability to do collaborative filtering, the ability to sort of create a more sparse data, understand the limitations of those data, and also look at the cold hard starts, and all those issues.

These are all things that clearly distinguish the company’s performance is applied correctly. Then in terms of customer support, for example, in terms of natural language processing; we knew that 10 years back only the banks used to have this wiser technology that very few enterprises used to have. Today, there is no enterprise of size and global reach that does not have multiple race of providing customer’s report. Clearly the AA of technology is helped to advance state of the art there, but more importantly where it matters enterprise internally in terms of demand prediction, in terms of price optimization, the AI and the able technologies are really offered leaps and bounds in terms of advancements for the enterprises to digest. For example, in the past the enterprise is to look at a handful of retributes, it’s like a dip-test to see how the absorption is in the market, and they used to have limited data, they used to work with a sample size of their own making that comes along, that brings its own level of Q to the data, and the forecast used to be mostly crude.

At the end of the day there are a set of old-time business planners who manually collect all the data and come to some conclusion about what’s going to more cause. But today I think you have got the ability to do simulation of data-time, you can look at a thousand attributes, you can look at unlimited data in a highly detailed forecast, and you can slice and dice the report in different ways. All this means that the enterprise has gained advantage to have a better local knowledge of demand, and that means we talking about the ability to leverage of all items, of all products, across all channels, for all promos, for all forecasts, and therefore that give us a superior level of advantage to enterprises today. So, I think this is going to be an unstoppable journey, it’s only going to get better and better and better, and the augmentation initiated on this journey they’d better not stop, they keep moving forward and forward on this because it’s not really where you start, but how you progress that will make a difference.

But in terms of the misperceptions about AI, I think it’s mostly the non-business people who ask me, there are people who are genuinely concerned about where this will lead to, for example in terms of potentially replacing humans, to being misused for all round purposes, so those are all quite valid. I don’t think promotion enterprises of scale today will have the commonsense and wisdom to know where to stop, even though it is very difficult to make an unusual call as to where to stop. But I do credit the ability of humans to make the right judgement in terms of how far to leverage them, and where to stop. So, I think clearly humongous process is being made through these technologies.

No doubt. Are there any other technologies that you’re optimistic about? I know that blockchain of course had quite a level of excitement about it last year, although in enterprise it seems like people are really focusing quite a bit on trying to build production solutions. I’d love to get your thoughts on any other adjacent technologies that you think will have a big impact for enterprises in particular, over the next several years.

The ability to create data out of non-human situations, and non-codified, non-human situations, and use the data to integrate those to your business decision processing, that actually makes a huge difference, and when you combine analytics with IOG, you’re talking about being able to create new business models that goes beyond inventory, but essentially goes towards an optimized performance across the entire extended supply chain. That’s where I think the boundary of an enterprise actually collapses if you get the right IoT done across the entire supply chain. It is the true value of any external supply team to make sure that the right parts of the supply team focus on the right things, but still as a whole the supply chain was triggered. This mission becomes reality through IoT, therefore I’m a big auditor on IoT and analytics that go hand-in-hand, along with AA and ML, and these luckily characterise the success stories of all the enterprises to come in the future Ed.

That’s great. As you look forward, I’d love to get your sense of what you’re optimistic about, and any lingering concerns that keep you up at night?

First of all, I’m a big believer in human race, and the ability of the human race to take the right calls. So, even though the ability for these technologies to sort of disrupt, to crossover a fine safe line to create destructive monsters out of that is laden there. But I do believe in the human ability to draw the line where things don’t move beyond that. However, from an opportunity standpoint, I do believe there is going to be a lot more happening along this paradigm that I’ve described, for example whether it is image processing, or wise deduction replication, or emotion control, or robotics, and ambiences, and natural language processing, and all this I think we are just scratching the surface. I think we’re going to see huge advancements in each other’s dimensions, that means there are going to be enablement’s of new technologies, and new business models, which will accelerate the progress that enterprises have in terms of adopting technologies to drive better business outcomes. I think this is going to be a very powerful journey, and in my opinion, we are in the onset of the journey Ed.

But if you ask me what are the concerns I have, as I’ve said, I do believe in human intelligence, but I do believe we are going to see a rich man being able to define new levels of beauty, and new types of brains, so to that extent these technologies can actually advance, and that is something we need to watch how it progresses and where to draw the line.

That’s super-helpful, this has been as always, a fascinating conversation. The last question I always like to ask is whether you have a good book or resource recommendation you could share for our listeners?

Absolutely, let me do two things. I do read a lot, however what I’m going to suggest just now is two books that got published the second half of last year, but I think the impact of these two books are very-very high.

The first is Blitzscaling, Reid Hoffman, an amazing book. We’re living in an age where we think, and abstract, and dislodge the existing giants. How to do that, I have to say, a book which has been able to codify all these things into one particular body of knowledge, and that’s where like Reid Hoffman’s ability to bring those together in a very structured way, for example the type of technically talks about the growth factors, then he talks about the obstacles, he talks about the business models patterns, he talks about the principles, the stages, it’s amazing transitions, all these are amazing ideas that they are able to codify into one body of knowledge.

Therefore, I would recommend that Blizscaling is something that every enterprise leader, whether he/she is inside a large enterprise, or an upcoming startup, they should definitely read and read. I’ve read the book at least six or seven times, and every time it opens new levels of thinking inside my mind.

The second book I would recommend is Yuval Noah Harari, 21 lessons for the 21stCentury. If you look at though in this small conversation, I alluded to the fact that the human brain knows when to stop, and how to leverage the technology progress, and more importantly where it can stop. But I think Yuval Harari’s book is probably the best that they have seen, in terms of what possibilities could exist on developments around infotech, and how we know humans can prudentially get replaces by advances here, and how the rich will be able to enhance the brains and body. It’s a very startling thought to think the next generation are going to see the rich people define how they look, how they think, that’s going to completelyturn the equation upside down, and how we need to be able to stop that type of regression, it’s not a progression, a regression happening.

It opens up lots of information around this. So, I think Yuval Harari, all of his work has been great, but this one particular thing stands out in my opinion. These are the two things I would love to recommend for deep reading, if you ask me.

That’s great. Those are both terrific recommendations. I wasn’t familiar with the latest from Reid Hoffman, and of course he’s always brilliant and insightful.

This has been as is always the case, a fascinating and insightful conversation. We’ve been speaking Sadagopan Singam who is a senior advice president global, and Business Head of CloudNative SaaS at HCL Technologies, and again this is Ed Maguire Insights Partner, at Moments Partners, and another episode of our Leadership Series podcasts. Thank you once again for joining us.

Thank you for inviting me Ed.