3085: NTT Data - AI, Cybersecurity, and the Evolution of Hybrid Work

[00:00:03] How is technology reshaping the very fabric of every industry and indeed our daily lives?

[00:00:10] This is a topic I explore every day on this podcast and today I welcome Tanvir Khan from NTT Data.

[00:00:20] And together we're going to discuss the transformative power of generative AI across various sectors,

[00:00:25] from cybersecurity to new digital workplaces and financial services.

[00:00:31] But I know you've heard about this stuff many times, but today I want to focus on things like ROI, business value, real-world examples.

[00:00:40] And with a rapidly evolving tech and business landscape, it feels like we're facing a dual challenge,

[00:00:48] innovating to stay ahead while also ensuring advancements are both secure and sustainable.

[00:00:54] And as I said a moment ago, we get ROI from these expensive tech projects.

[00:00:59] So, generative AI is heralding significant changes, but as it redefines the boundaries of innovation, bigger questions arise too.

[00:01:09] What are the broader implications for the workforce and global industries?

[00:01:12] Well, today we're going to explore all this and how NTT Data is steering through these technological shifts,

[00:01:21] fostering large-scale experimentation and enhancing employee capabilities by harnessing AI's full potential.

[00:01:29] But enough from me. Let's get Tanvir onto the podcast now.

[00:01:33] So a massive warm welcome to the show.

[00:01:36] Can you tell everyone listening a little about who you are, Tanvir, and what you do?

[00:01:41] Thanks for having me, Neil. My name is Tanvir Khan.

[00:01:44] I'm the head of transformation for NTT Data Inc.

[00:01:49] I've been with the company for about a dozen years in different roles.

[00:01:52] I've served as the president of our healthcare business in the US.

[00:01:56] I've been a service delivery leader.

[00:01:59] I was the head of strategy and the chief digital officer.

[00:02:04] And now I am trying to work in an office that's trying to build a whole company together as a leader for transformation.

[00:02:11] If I look at who I am as a person, I like to think I am a hiker.

[00:02:18] I am a chef.

[00:02:20] I'm a bartender.

[00:02:22] Last summer I hiked up to the Everest base camp and that was beautiful.

[00:02:26] And when I have time, I like to play around the kitchen, give me fire, give me a set of random ingredients.

[00:02:33] And I like to create stuff.

[00:02:35] And that's what my passion is.

[00:02:37] Wow.

[00:02:38] I feel incredibly lazy now, man.

[00:02:39] I thought I spent a lot of plays, but that is phenomenal.

[00:02:43] I love that passion that you've managed to walk away from tech to wonder where the Wi-Fi is.

[00:02:49] We can break bread around the table with friends and family and colleagues, et cetera.

[00:02:54] At the moment, of course, in the tech industry, all anyone's talking about is AI in particular generative AI.

[00:03:01] And for a lot of business leaders, they're playing with it in their homes and think, oh, this is kind of cool.

[00:03:06] But there's also an increasing amount of conversations now about, okay, but what does this mean for my business?

[00:03:13] What does this, how can we get value from this for my business?

[00:03:16] And each day on this podcast, I try and explore different areas of how technology can impact a business in any industry.

[00:03:23] So I'm curious, how do you see generative AI shaping the future of cloud strategies?

[00:03:29] Are there any unique roles you see it playing in helping companies tackle industry specific challenges like improving security, enhancing privacy?

[00:03:39] What are you seeing here?

[00:03:40] All of the above.

[00:03:42] But the reality is, there are two ways to look at it.

[00:03:45] One is, I think there is consensus in the industry that the changes generative AI is going to bring are profound.

[00:03:54] They are going to fundamentally change how we do stuff.

[00:03:57] The second part is, what are the use cases of tomorrow?

[00:04:02] The honest answer is, nobody knows.

[00:04:05] And people who are taking positions who are, let's put it politely, guessing.

[00:04:12] So what are the cases of tomorrow are going to unfold in the next two to five years, even longer?

[00:04:20] We are in the beginning of a very long journey.

[00:04:23] Just like when we started the internet journey, the common thinking was that the transformation the internet is going to bring is doing the same things we do in the physical world.

[00:04:34] But we'll do it in cyberspace.

[00:04:36] So we will have e-commerce, the Amazon of the world.

[00:04:39] Yes, that happened.

[00:04:40] We will do online trading.

[00:04:42] We will do online banks.

[00:04:45] But the killer apps that appeared later, for example, today, what is the most predominant use of people's time where internet is the underlying technology?

[00:04:55] It is the social video, TikTok, Reels.

[00:04:59] That was in nobody's mind when they were thinking of how the internet is going to change the world.

[00:05:04] So similarly, when we took off generative AI and the use cases that will be the dominant use case a few years from now, nobody knows.

[00:05:12] But let's look at what it does and try to extrapolate it.

[00:05:16] So today you can go to a program like Midjourney or GPT-4 and say,

[00:05:21] Paint me a picture of a man wearing a purple tuxedo, holding a golden trident, riding a killer whale, fighting an army of octopus.

[00:05:29] And boom, the picture gets created, right?

[00:05:32] Instantly.

[00:05:33] It would be like that with software development in the near future.

[00:05:37] Write me a Python program for, and boom, your program is now ready.

[00:05:42] So, one developer can do the job of 10, maybe 100 people.

[00:05:47] They need to know what they are doing.

[00:05:49] They don't have to worry too much about the how.

[00:05:53] So it's going to fundamentally change things as they emerge.

[00:05:57] But what will the use cases be?

[00:05:59] Nobody knows.

[00:06:02] So, when you don't know what you do, that is an important question.

[00:06:06] And people say that in this area, there is no wait and watch.

[00:06:11] Wait and watch is death, because it is changing so rapidly that you will never be able to catch up.

[00:06:16] So now you have a dichotomy.

[00:06:19] You don't know what is going to happen, but you cannot wait.

[00:06:23] So what do you do?

[00:06:24] What you do is, you do enablement and scaling at massive scale.

[00:06:29] You experiment today.

[00:06:32] You work on proof of concepts.

[00:06:33] You try new things.

[00:06:35] You enable thousands and thousands of your employees to be able to play with it.

[00:06:40] So large scale upscaling, large scale enablement and experimentation is where we need to be today.

[00:06:48] What are the use cases?

[00:06:50] There are millions of use cases that everybody is talking of today.

[00:06:55] Some of them in the area of conversational interface to guided analytics,

[00:07:00] things like content creation, personalization, internationalization are very powerful, are here and now.

[00:07:08] But what we have to keep our eye on is the size of the price,

[00:07:11] which is the new emerging things that will come a few years from now.

[00:07:15] And we need to make sure that we are equipped to harness them as they begin to sort of manifest themselves.

[00:07:22] And if we were to take one particular area, and I think cybersecurity is another big topic right now.

[00:07:29] With the rise of cybersecurity breaches, what would you say are some of the most effective strategies that businesses can adopt today?

[00:07:37] And especially as we're a few weeks away from 2025 looking at doing things differently.

[00:07:42] And how are IT solutions, particularly AI, driving this transformation?

[00:07:48] So we need to start taking a more holistic look at cybersecurity than we traditionally have been.

[00:07:56] So let's step back.

[00:07:58] So AI offers a huge opportunity in cybersecurity.

[00:08:02] AI is better at identifying anomalies as they occur, focus on them and catch things as they are happening or beginning to happen.

[00:08:11] So AI has the ability to strengthen parameter security significantly.

[00:08:16] The not so good news is that the same AI that is available to the, let's call it the protectors, is equally available to the hackers.

[00:08:25] So it is also making the hackers immensely powerful.

[00:08:29] Not only that, it is putting power in the hands of novice hackers who might be able to build things like ransomware that they don't really know how to unlock later.

[00:08:41] So AI is equipping the guardians with power.

[00:08:46] But it is also making the bad guys immensely more powerful.

[00:08:51] So this cat and mouse game is going to play out over the next 10 years.

[00:08:56] So what should you be doing?

[00:08:58] One is continue working on strengthening the perimeter security.

[00:09:02] And there are hundreds of services and products out there.

[00:09:05] We do a great job of that.

[00:09:06] But the second is, there is going to be some sort of a breach sooner or later.

[00:09:14] When that happens, you need to be prepared for minimizing the damage that occurs.

[00:09:19] So we've been doing a lot of work in things like attribute based security, which is if somebody does get in, they should be able to get as little information as possible.

[00:09:29] Do as little damage as possible.

[00:09:31] So if somebody does get hold of documents or data, that has to be encrypted at a level that even if somebody gets it, they can't do much with it or it is not usable.

[00:09:42] So you protected yourself and somebody that's get in.

[00:09:46] The third area is, people will get in, people will have ransomware, people will lock your systems out.

[00:09:53] If you start paying ransom, you just created a new problem for society at large.

[00:10:00] So just like today, a lot of governments take up a stand of not paying ransom.

[00:10:05] Companies have to start taking that.

[00:10:07] But to be able to do that, you need to have the ability to recover.

[00:10:10] So rapid recovery is the third leg of the stool.

[00:10:14] When something happens, you already have backups.

[00:10:17] You've got systems in place.

[00:10:18] How quickly can you build your systems back up so that the ransomware hackers don't have the power over you?

[00:10:27] So the third aspect is systems and processes that allow you to have rapid recovery in case of a ransomware attack or some other type of outage.

[00:10:35] And the fourth is, if you've been able to build process for ransom recovery and rapid recovery protocols,

[00:10:43] you need to make sure that you start putting them in place even before the bad actors had a chance to strike or think of it as cyber resilience.

[00:10:50] So bring your cyber resilience protocols, put it back into your perimeter security and build that virtual cycle

[00:10:57] where you've got the ability to strengthen your perimeter.

[00:11:01] You've got hackers not being able to do anything with the information they've got access to.

[00:11:06] Rapid recovery in case of an attack and resilience that continues to strengthen the perimeter security.

[00:11:12] But between all that, you have to remember the weakest link is the user.

[00:11:18] The phishing attacks that happen, the social engineering that happens, the vulnerability is the user.

[00:11:24] So continuous training and update and testing of security is important.

[00:11:28] But yes, AI is going to make the perimeter security stronger, but it is also going to make the attackers stronger.

[00:11:36] And that cat and mouse game is going to play out, like I said, over the next decade or so.

[00:11:40] And we are, or we find ourselves now several years after the pandemic and organizations are continuing to adapt that to that hybrid work environment.

[00:11:51] I think it's something that businesses all over the world are looking at now.

[00:11:56] And we're looking at not only the future of work, but how can we improve that future of work?

[00:12:00] How can we do things differently?

[00:12:01] So I'm curious from your side of things, what do you think a modern digital workplace should look like?

[00:12:08] And how do you see Gen AI supporting collaboration and productivity in this different era of work, really, where we're not just looking at the future of work, but improving it?

[00:12:18] So the hybrid workplace is really something that's out there, which we as a society still haven't figured out.

[00:12:25] Every day you pick up the newspaper.

[00:12:28] Well, these days you don't pick up the newspaper, but let's say you pick up the news and you hear of some of the large banks asking everybody to come back to office full time.

[00:12:39] Amazon sent out a message where they want people coming back five days a week.

[00:12:44] There are companies that are asking people to stay remote permanently.

[00:12:50] And then there is all sort of combinations.

[00:12:53] So it is safe to say this hybrid workplace is here to stay.

[00:12:57] And when we start thinking of a hybrid workplace, we need to start thinking of it along three axes.

[00:13:03] But what did remote work do?

[00:13:05] Remote work allowed people time.

[00:13:09] It allowed people better work life balance.

[00:13:12] And arguably it increased productivity.

[00:13:15] What was the downside?

[00:13:17] It did impact collaboration.

[00:13:21] It did impact the creativity and problem solving.

[00:13:24] So it is clear that we need to bring in aspects of creativity, innovation, problem solving and collaboration.

[00:13:32] And balance it with the needs of people who have work life balance and driving more productivity.

[00:13:38] So the answer is not the physical space.

[00:13:41] The answer is the modern workspace.

[00:13:43] And we think of it along three axes.

[00:13:45] Think of it as user.

[00:13:47] Think of it as spaces.

[00:13:49] And think of that as the enablers.

[00:13:52] Enablers, boil it down, becomes your device and the associated software.

[00:13:57] So when you look at the user.

[00:14:00] Today, users use multiple devices, can work anytime, anywhere on any device.

[00:14:06] So providing them with support is becoming paramount in a mod of workplace.

[00:14:12] Nobody has the ability to walk across and talk to a tech and say, I can't log in.

[00:14:16] Can you come and tell me how to do that?

[00:14:17] Because you are remote.

[00:14:19] When we started supporting tens of millions of users that we support in our digital workplace services,

[00:14:26] what we realized is the transaction volumes are really exploding.

[00:14:29] Because time on task started expanding.

[00:14:32] The number of devices started expanding.

[00:14:34] And the environment was very heterogeneous because it was 10 million homes.

[00:14:38] So very clearly where the industry is moving in is your devices today are very smart devices.

[00:14:45] Your phones, your computers.

[00:14:46] Usually when you have a problem like the applications are slowing down,

[00:14:50] your computer knew of it six hours before you ever noticed it.

[00:14:54] So there are tools today that can proactively monitor and fix those things before people ever realize that.

[00:15:00] So as far as the user support is concerned,

[00:15:03] it is moving very quickly into a highly proactive, preventative, predictive model

[00:15:10] where your device will get updated, fixed, optimized before you ever know it.

[00:15:16] So user support and user experience is changing.

[00:15:19] The thinking about user experience few years ago was to try to make it seamless as painless as possible.

[00:15:27] Today's model is make it invisible so that you never ever see that interaction.

[00:15:33] So the best call in a service desk is a call that never comes.

[00:15:36] Today that's becoming a reality.

[00:15:38] The second is workspaces.

[00:15:41] And when we talk of collaborative workspaces,

[00:15:44] this is no longer a physical space, an office, a WeWorks.

[00:15:48] This is a combination of physical and virtual space.

[00:15:51] So we are now beginning of seeing the emergence of things like the metaverse.

[00:15:56] Companies are setting up their intraverse.

[00:15:58] We expect things like training and onboarding having in these virtual worlds in the next coming years.

[00:16:05] And I'm not talking five, six years.

[00:16:07] This is one to two years.

[00:16:08] Our company launched an intraverse as a common place for meetings across the 190,000 people corporation.

[00:16:15] And that is becoming the definition of space.

[00:16:19] The other thing that is growing very rapidly is digital twins.

[00:16:25] Now there are digital twins of processes, devices, but that's a topic for another day.

[00:16:29] But digital twins of spaces.

[00:16:30] So you can actually replicate a factory, a conference room and an office.

[00:16:34] And then you and I are having this meeting, but we can actually be having this meeting or working together

[00:16:39] in a virtual digital twin of the space that we are supposed to be working in.

[00:16:45] So today with things like LIDAR technologies, it is very easy to recreate a three dimensional model of any physical space.

[00:16:53] But that three dimensional model is static.

[00:16:56] In spaces, there are things that are moving.

[00:16:58] So for example, if it was a factory, we would create a three dimensional view of the building.

[00:17:03] On top of that, you layer live video of things like conveyor belts and things that are moving.

[00:17:08] On top of you, you layer on instrumentation.

[00:17:13] And now you are actually in that factory, which is a digital twin.

[00:17:18] So when you start thinking of intraverse, metaverse, digital twin and collaborative spaces,

[00:17:24] we have to start thinking of spaces as that big picture when we look at the hybrid workplace.

[00:17:29] And then you will look at devices.

[00:17:32] So we are very close to the Holy Grail of anytime, anywhere, any device access to all sorts of corporate resources.

[00:17:40] And then there are tools like Zoom that we are on, Teams, Slack.

[00:17:45] So if you put together your device and the collaboration tools, you put together spaces, both physical and virtual.

[00:17:52] And then you put together a support model, which is invisible and seamless.

[00:17:56] That is what the modern hybrid workplace is going to look like.

[00:18:00] And once you have that manifested in your environment, the debate on whether you should come home, work at home,

[00:18:07] or you come into office five days a week or a few days here, a few days there goes away because you address the underlying issues of collaboration,

[00:18:15] creativity and productivity and balanced it with work life balance.

[00:18:19] And I think that's where the hybrid work business is going.

[00:18:22] Oh, absolutely.

[00:18:23] Love that.

[00:18:24] At the very beginning of our conversation as regarding AI, we were talking about the almost in an experimental phase at the moment.

[00:18:32] And there is a lot of guesswork, a lot of people selling solutions that are looking for a problem.

[00:18:37] And many business leaders are struggling with that ROI question and what business value is this technology going to bring to me?

[00:18:44] So maybe to bring to life what we're talking about here, are you able to share any examples of maybe how generative AI has helped innovate cloud services in maybe real world applications?

[00:18:57] And how do those innovations solve business challenges while ensuring compliance and maintaining ethical standards and all that good stuff?

[00:19:06] Because I think very often we get a little bit distracted by the technology and then come to the problem later.

[00:19:12] But I'd love to hear some positive examples of what you're seeing here.

[00:19:16] So generative AI is one of those technologies that started making impact very quickly.

[00:19:21] It, while the future is bright, but even today it is adding considerable value.

[00:19:29] So we work with consumer products company, which has got an immense amount of data going back years and decades into history.

[00:19:38] They can look at every product.

[00:19:40] They can look at every store.

[00:19:42] They can look at every region, time of the day.

[00:19:45] So massive amount of data.

[00:19:47] But data by itself doesn't do anything unless you've got the ability to organize it.

[00:19:51] So they spent tens of millions of dollars over the years to build this massive tool where salespeople can actually slice and dice data and come up with their sales strategies.

[00:20:00] And after spending these tens of millions of dollars, they figured that nobody's actually using it.

[00:20:06] And that was a problem that existed for a few years because you've got data, you've got the tools, but the average person is not a data scientist.

[00:20:16] So you are a sales manager.

[00:20:19] You've got 17 stores that you manage in the southern part of Dallas.

[00:20:24] And now you've got this data lake that can help you design your campaigns.

[00:20:28] You're a sales guy.

[00:20:29] You spent 10 years on sales.

[00:20:31] You're good at relationship management.

[00:20:32] You're good at running a sales process.

[00:20:35] You are not an analytics guy.

[00:20:37] That's not what you were hired for.

[00:20:39] So you've got this massive tool that you can't.

[00:20:41] Now, Generative KI allows you to talk to your data and talk to your data.

[00:20:46] Talk to your documents are the most powerful use case that is prevalent today.

[00:20:51] So now the sales manager can go to the application and say, I've got these 17 stores in the southern part of Dallas.

[00:20:59] Mars madness is coming up.

[00:21:00] I want to design a campaign to maximize the sales of my beverages in that month.

[00:21:06] What should that campaign be?

[00:21:09] And boom, it comes up that give a discount on this product, have a coupon on so-and-so product.

[00:21:14] But the same thing that the application was always capable of doing.

[00:21:18] You're still using the core application, but the conversational interface to guided analytics is something that is making very powerful.

[00:21:26] You are a company that makes tractors.

[00:21:29] You've got techs that go and repair those tractors.

[00:21:32] A tractor has got 50,000 pages worth of manuals.

[00:21:36] And there's a light that is blinking on the left side of the screen.

[00:21:39] You don't know what that is.

[00:21:40] A tech typically lasts for a year or two and then they quit and do something else.

[00:21:47] Today, you go and say, there's a light blinking on the left side of a Kuboto model 2350 tractor.

[00:21:54] What could it be?

[00:21:56] And boom, you got your answer what that was.

[00:21:59] So, talking to your documents, talking to your data is really delivering value here and now.

[00:22:05] And the ease at which you can manifest those use cases is mind-boggling.

[00:22:11] Now, you can make those things happen in hours instead of years that it used to take in the past.

[00:22:15] So, those are the use cases which are very powerful today.

[00:22:18] But again, it's content generation, personalization, agentic workflows and interaction with data.

[00:22:26] First part of this revolution was when you ask for information and you are provided with content.

[00:22:33] The second part of the revolution is what's called agents, which essentially is the piece of software that actually does a task for you.

[00:22:41] Where we are today is we've gone from give me content and information to actually do something for me, which is agentic workflows.

[00:22:48] But the future is AI native workflows for business problems.

[00:22:52] And I think that's what we're going to start seeing very soon.

[00:22:55] I love that, especially about getting people used to the idea of talking to their documents, talking to their data.

[00:23:02] And you mentioned personalization in there as well.

[00:23:04] And I think especially in the finance world, if we look at our banking, there's a personalized banking transformation going on at the moment as well.

[00:23:12] So, on that side of things, how do you entity data with your expertise?

[00:23:17] How are you helping financial institutions deploy those personalized solutions more efficiently that your customers are demanding or their customers are demanding?

[00:23:27] So, the value that subject matter expert in a financial institution provides is their advice.

[00:23:34] So, you could be an advisor in a wealth planning division of a bank or you could be an investment banking analyst, which is making data driven decisions.

[00:23:47] Now, the expert adds value with their expertise and being able to do something with that data.

[00:23:54] Unfortunately, today, some of these experts spend something like 50 to 70% of time in data trolling.

[00:24:02] So, if you are a wealth management advisor, you've got access to millions and billions of records.

[00:24:11] Being able to identify patterns, trends, look at what are the things that are happening in the market, being able to look at the key trends,

[00:24:19] and then you make a decision on what should your wealth management strategy be.

[00:24:23] Unfortunately, the point in time where you get to a place where you've identified the key trends has consumed 70% of your bandwidth.

[00:24:31] So, if you've got 40 hours in a week, we want these people to be spending 36 hours making those decisions,

[00:24:39] providing that advice and 4 hours in organizing the data.

[00:24:43] Today is the other way around where organizing data and finding those trends just takes a huge amount of time.

[00:24:50] AI is very good at doing that.

[00:24:52] Similarly, if you are an investment banker trying to look at performance of five different companies,

[00:25:00] building those massive spreadsheets and tables takes a huge amount of time.

[00:25:04] So, let analysts be analysts.

[00:25:06] Let AI do the data trolling for you.

[00:25:08] And that's the biggest value that the banks will be finding.

[00:25:12] And sticking with the financial industry there, do you also see AI helping improve things like risk management or regulatory compliance for financial services?

[00:25:26] Do you see impacts like this on the banking industry too with this technology?

[00:25:30] I think the impact is going to be great, but it is not going to be a replacement of the experts.

[00:25:39] It is going to be the enhancement of experts.

[00:25:42] So, when you look at test management and you look at regulations existing and emerging across various jurisdictions across the world,

[00:25:51] and you need to put them all together and look at what are the common trends

[00:25:55] and what are the operational processes that are needed to comply with those.

[00:26:00] Now you've got a clear demarcation.

[00:26:02] So, AI is going to help look at current and emerging regulations and look at common trends across it.

[00:26:08] The human being is going to interpret that and say, operationally, these are the changes that we need to put in place to make sure we are compliant.

[00:26:17] So, as long as we've got that line of demarcation, I think we will make the whole solution more and more powerful.

[00:26:23] When we try to get into a space where we replace the human being and let AI do everything,

[00:26:28] all we are gaining is productivity.

[00:26:29] Yes, you save the salary of one person, but now you've gone down the rabbit hole of trying to find productivity in people replacement.

[00:26:38] This is not that.

[00:26:40] This is not a productivity tool.

[00:26:42] This is creating new value, not about saving a few dollars.

[00:26:46] And that's where the greatest value is going to be in essentially not only financial or any other industry.

[00:26:53] So, I heard a quote and I think it was from the CEO of Mayo Clinic where somebody said, will AI replace doctors?

[00:27:02] And his answer was, AI will not replace doctors, but AI will replace doctors who don't use AI.

[00:27:08] The same with banking.

[00:27:10] It will not replace bankers, but it will replace bankers who don't use AI.

[00:27:14] Oh, I love that.

[00:27:16] And such a good point to make.

[00:27:18] And sticking with the world of finance again, I think anyone looking at opening a new bank account,

[00:27:23] whether they're in the US listening or UK or in fact anywhere in the world,

[00:27:27] one of the things that's increasingly standing out is digital banks are often leading in customer satisfaction scores now.

[00:27:34] So, I'm curious, how do you envision those traditional banks that we all grew up with?

[00:27:39] How are they adapting to this new competition?

[00:27:41] I think they are not adapting too well.

[00:27:45] If they were adapting well, the new online banks would find it very difficult to break in.

[00:27:52] And part of it is generational.

[00:27:55] But what is becoming very apparent is people don't need banks.

[00:28:00] People need banking.

[00:28:01] So, there's a difference between needing banking and needing banks.

[00:28:06] And what the new generation of online banks are catering to is your need for banking.

[00:28:11] I don't walk into a bank branch almost ever now.

[00:28:16] The closest I would come to going to a bank is for whatever reason, if I need cash, which also I don't need too often.

[00:28:24] I might need to access an ATM machine somewhere.

[00:28:28] But all my banking needs are today on my computer and my phone.

[00:28:32] So, providing banking to people is what really the solution is.

[00:28:39] The experience of online banking is getting richer and richer.

[00:28:43] And that's where generative AI is going to provide a huge amount of personalization and content.

[00:28:48] But AI is going to revolutionize customer experience.

[00:28:51] I think that is a forewarn conclusion.

[00:28:54] And the little differentiation that banks had with banking was the experience of interacting with a human being.

[00:29:01] And as the online experience gets richer and richer, the virtual banks will have an edge over physical banks.

[00:29:09] And the big banks are spending billions and billions of dollars in developing their cyber and online presence.

[00:29:16] And I think that is the trend of the future.

[00:29:19] If you take brokerages, I in my life don't remember going sitting down with a broker and filling up a form to buy a share.

[00:29:27] All of us have online accounts.

[00:29:29] So, that physical broker that needs to sit down with you and use to actually sign share certificates and exchange it.

[00:29:35] Those days are gone.

[00:29:36] And it's quite possible to imagine a future of banking very similar to what online banking did to the paper and certificate brokers.

[00:29:45] Wow, I've learned so much from listening to you today.

[00:29:48] So many big takeaways, a lot of food for thought.

[00:29:51] But before I let you go, I'm going to ask you to leave one final gift to everybody listening that they can check out along with your insights.

[00:30:01] That is a book for our Amazon wishlist.

[00:30:04] I always ask my guests, I've got an Amazon wishlist.

[00:30:07] Ask my guests to maybe share a book that means something to them, inspires them, all that they just recommend.

[00:30:12] We'll get the listeners to check that out.

[00:30:14] But what book would you like to add to that list and why?

[00:30:16] I spend a lot of time in AI.

[00:30:20] And there's a book that I would refer to, which has got nothing to do with AI, but very relevant in today's world.

[00:30:26] And the book is not new.

[00:30:28] I would say a book is what 18, 20 years.

[00:30:30] This is Yuval Noah's book called Sapiens.

[00:30:33] Sapiens.

[00:30:34] And Sapiens talks about essentially the anthropological history of our species, the Homo sapiens.

[00:30:43] And it talks about how humans evolved over a period of time.

[00:30:47] One of the most important takeaways for me from the book is what the author calls the cognitive revolution.

[00:30:56] So he talks about very successful human species like the Homo erectus that lasted for 3 million years.

[00:31:02] But in 3 million years, their stone tools hardly evolved.

[00:31:06] And their only claim to fame was that they accidentally discovered and harnessed fire.

[00:31:11] But then came this animal on the African savanna about 120 years ago called the Homo sapiens.

[00:31:18] And it was a rather inconsequential animal.

[00:31:22] Nothing happened for about 50 years.

[00:31:23] Then about 70 years later, something happened.

[00:31:28] And nobody knows what that was.

[00:31:30] But the theory is that some sort of change happened where humans developed fictive language.

[00:31:36] And with that, in 70,000 years, we went from Flint Spear tools to the International Space Station.

[00:31:45] So imagine 3 million years, nothing.

[00:31:47] New species, 50,000 years, nothing.

[00:31:51] And then in 70,000 years, we are sitting and having a Zoom call virtually with each other.

[00:31:57] And this era is called the cognitive era.

[00:32:01] If you take a Homo sapiens 70,000 years ago, anatomically they are not very different from you and me.

[00:32:08] But intellectually, we are a different animal in 70,000 years.

[00:32:12] And this 70,000 years, the acceleration of intellect or the separation of intellect from anatomy is what the author calls the cognitive revolution.

[00:32:22] Now, I have my own theory which is I would like to call what the author calls cognitive revolution.

[00:32:30] I would like to call it the first cognitive revolution.

[00:32:33] Which was the separation of intelligence from anatomy.

[00:32:37] Now, we are in a stage with artificial intelligence and this took 70,000 years.

[00:32:41] So, AI will take another 10,000 years.

[00:32:43] I am not saying 10 months.

[00:32:45] But let's look at 1000 years, 10,000 years.

[00:32:48] The separation of intelligence from biological beings is the second cognitive revolution.

[00:32:55] And that in the next 70,000 years is going to fundamentally change the definition of intelligence just like the cognitive revolution does.

[00:33:04] So, Read Sapiens is a fascinating book.

[00:33:07] It puts in context what a cognitive revolution is.

[00:33:10] And if you want to appreciate what AI is doing, you need to appreciate what the human brain did over the last 70,000 years.

[00:33:18] So, love that book.

[00:33:19] Wow, man.

[00:33:21] Consider my mind blown there.

[00:33:22] I'll get that book added to the Amazon wish list.

[00:33:25] But your interpretation of it there and a few predictions for the future.

[00:33:29] Absolutely mind blowing.

[00:33:31] For anyone listening who wants to continue the conversation we started today,

[00:33:36] what's the best place for listeners to find you or your team online and ultimately just find out more information?

[00:33:42] Where would you like to point them?

[00:33:43] So, the NTT data website is always a great place to look for information.

[00:33:49] I am on LinkedIn.

[00:33:51] So, if anybody reaches out to me on LinkedIn and says I heard Neil Hughes' podcast, want to have another conversation, would love to talk to them.

[00:34:04] Awesome.

[00:34:04] I'll add links to everything there so people can find you nice and easy.

[00:34:08] We covered so much today from how businesses can protect themselves against cybersecurity breaches,

[00:34:14] what role IT solutions and AI technologies play in that transformation,

[00:34:19] what a modern digital workplace should look like,

[00:34:22] how businesses can use generative AI to innovate in cloud services and digital workplaces,

[00:34:28] and so much more about solving real problems, ensuring compliance, maintaining ethical standards, and everything in between.

[00:34:35] And not only that, you left us with a killer closure and a fantastic book to check out too.

[00:34:41] Thank you for joining me today.

[00:34:42] I really appreciate you, Tom.

[00:34:43] Thank you, Neil.

[00:34:44] I appreciate it.

[00:34:46] Well, we covered considerable ground today.

[00:34:48] We covered some of the things that we've been doing today,

[00:34:48] discussing the pivotal role of Gen.AI in shaping the future of work,

[00:34:53] to fortifying cybersecurity measures, and even revolutionising financial services.

[00:34:59] And Tanvir provided us all today with invaluable insights into how NTT data is not just adapting to,

[00:35:06] but actively shaping some of these advancements to empower businesses around the world.

[00:35:11] Where do we go from here?

[00:35:12] I mean, as we look to the future, and a world where technology continuously evolves at exponential pace,

[00:35:19] I think it prompts us all just to put the brakes on a minute, sit down and think,

[00:35:24] how can organisations further leverage AI not just to anticipate,

[00:35:28] but create and solve emerging challenges that we all see out there?

[00:35:33] But I invite you to share your thoughts.

[00:35:36] These kind of questions are much, much bigger and higher than my pay grade.

[00:35:41] So please join the conversation on how Gen.AI could continue to transform our digital and physical worlds

[00:35:48] by emailing me Twitter, LinkedIn, Instagram, just at Neil CQs.

[00:35:53] Keep those questions, comments, ratings, reviews, whatever it is, keep them all coming through.

[00:35:58] And I also cordially invite you to join me again tomorrow, where we're going to do it all again

[00:36:03] with a different guest and a different topic about how technology is transforming our lives, our work and world.

[00:36:11] If that sounds like a good deal to you, I will see you all bright and early tomorrow morning.

[00:36:16] I'll be the guy waiting in your podcast feed.

[00:36:19] But that's it for now.

[00:36:20] So I will speak with you all then.

[00:36:22] Bye for now.

[00:36:23] Bye.