How AI Is Transforming Indias Workforce for Global Competitivene

President, Global Public Policy and Government Affairs Mastercard, Vishnu R. Dusar, Co -Founder and MD, Nucleus Software, Sue Daly, Director, Tech and Innovation, Tech UK.

Thank you so much, Pragya, and a very good morning to my wonderful panelists. We have a few audience in the room, but we have a lot more online. So I’m looking forward to, you know, yeah, we can get out. You are here, Ravi, next to me. And Vishnu is just on his way. He should be here shortly. I think the theme of our panel is AI and workforce transformation. And clearly, from a, you know, India perspective, the AI is obviously creating a number of opportunities. It’s also creating a lot of anxiety amongst the youth. And I think it’s important. It’s important to decode what does AI really mean and how do we navigate these shifts that are ahead of us.

So in terms of structuring the panel, I thought we’ll try and break it into. three different segments. The first segment is clearly about what is the disruption and how are we designing for it? So try and get perspectives from each of the panelists on how are you seeing this disruption? Are we shaping this disruption or is this disruption really shaping us? So Kish, if I can start with you maybe, right? From one of the sectors that’s most hotly debated is IT services and you’re a leading company in that space. How are you seeing this change for your employees? Do you see software coding now only being done through AI tools? So what is the job of the coder if you look at it?

But how real is this disruption and how are you staying ahead of the shifts that are there?

So I think the direction of travel is indisputable. That there is disruption. There’s a lot of there’s an issue of technology capability and there’s an issue of adoption. And there’s always that technology capability leads adoption. Adoption is going to impact, is going to determine workforce displacement or disruption. But the capability, there’s no doubt that this capability that exists today, actually this capability that existed three months ago, six months ago, where there’s quite a large chunk of work that is done by the industry that could potentially be displaced or improved or in some way impacted by AI. What is it that is getting impacted is changing very rapidly. So you would ask me at the beginning of 24, right?

What services will get impacted? What services will get most impact? Out of say testing, actually I’ll put BP of India. I am saying in tech I would put testing first and I would have put software engineering last. Today I will flip that. I will say software engineering is the most. So the direction of travel I think…

So you really think software engineering is bigger disruption than testing and infra management or other stuff, right?

That is true. So I think whatever disruption we saw I thought would be there in infra. I think it is there but it is a plateau. I am not seeing leaps and bounds of change. What we saw as a potential change like a year ago and now is not so different. I think the massive difference is in software engineering.

So you know if you are a young software professional… How do you see… What does this mean for me as that young fresher out of college right now?

I’ll say opportunities for a young technically savvy person is enormous now there are things they need to think of and do differently for that opportunity to become real for them because the real value of AI is not in reducing headcount in blah functions whatever it is where it’s in BPO or some functional work that’s not the real value the real value is in being able to solve problems that you could not solve before and I think you need to arm yourself with a completely different set of skills to make that real but if you do that I think the opportunities are enormous for a young age

Thanks Kish, I’ll come back to you Ravi if I can come to you, MasterCard is very strong obviously in financial services but you have a very strong data and technology play how are you seeing this workforce disruption and for a company like yours which has a very large GCC in India what are the different kind of skill sets that you’re thinking about today

Sure, thank you very much and thanks to NASCOM great to be here on the panel with Sue and Sri Krishna so I think like I mean a lot of change right over the last two decades when I look at our industry I guess if you look at it like all the professionals in privacy, cyber security data protection, technology risk they’ve all been enablers of digital transformation right? They have, I mean, create what we enjoy today in terms of digital empowerment and the ability, let’s say, talking from a payment lens, you know, very seamless in terms of wherever in the world you are, right? All that is riding on trust, right? And there’s a lot that goes in, you know, to build that trust, right?

So now we are seeing as artificial intelligence, AI is being embedded into a kind of decision -making, public infrastructure, service delivery, right, and governance. So it’s no longer kind of a downstream compliance function as such. So I think that’s why we need, you know, the shift is in kind of the fintech disruption that came about before. I think what we are… We are seeing a bigger shift that AI is bringing in terms of the kind of skill sets, you know, that are required. So, you know, to your question on what kind of skills are required, right? I think the skills I would say is that the, what do you call, the capability for system level judgment is needed.

So what we mean by that is that are you able to, you know, take what outputs are coming? You know, from AI. And you need to have the capability to understand is the model drifting, you know, in high stakes and regulated industry like ours. It becomes essential because decisions scale very instantly and as do the systemic errors, right? And the impact of those errors if left unchecked. So I think that it’s important to have that system. level judgment. Then, interdisciplinary fluency is important because the AI challenges are not just technical, right? They are at the intersection of engineering, of regulation, of risk, you know, user behavior. So, if we have professionals who are across those domains, right, that’s important and to have that interdisciplinary approach rather than working in silos as such.

Then, it comes to need for a very continuous learning mindset because the AI systems are evolving with data, right? And the workforce needs to evolve that too. And the ability to learn from live environments, right? What’s happening to adapt models, kind of, to be able to refine the decision -making. So, that’s important. So, system -level judgment, interdisciplinary fluency. continuous learning mindset and I think last but not the least is a deep contextual awareness is needed now in a country like ours in India you know multiple languages dialects informal systems so if an AI agent is interacting with the user the question is does it understand the context and the intent and the kind of the real -life realities or is it just a language right so because the context is shaped by the whole models are being trained which means that engineers have to consciously design for it so that contextual ability and awareness is very important

so the typical engineer who was the coder as we know knew it obviously has to build a very differentiated set of skills is what you’re really talking about right so understanding interdisciplinary learning understanding context the ability to continuously learn I think that in itself is becoming a skills. So clearly, I think there’s a lot of change that is needed at a college level and school level on how, you know, even how you’re learning so that you are ready for this very, very changing world. So if I can come to you, right, how are you, you know, you represent Tech UK here, how are you seeing the AI disruption in the UK workforce? Is there anxiety?

Is there opportunities that you are seeing? And how are you as an organization, and of course, the UK government supporting this transition that’s

Well, thank you. That’s a question in a panel all in itself. It’s a real pleasure to be here. Thank you so much for the invitation to be part of the summit. Just to say to everybody, you’ve done an amazing job. So thank you. But also to this really important panel discussion. And it is absolutely a discussion that we’re having in the UK. And what I found really useful this week, if I can be slightly selfish for a moment, is that I think it’s a really important discussion that we’re be having in the UK. And I think it’s a really important discussion that we’re going to be having in the UK. And I think it’s a really important discussion that we’re going to be is to listen to the conversations that you guys are having here and the other global people that are here at the summit and to kind of compare notes.

Are we having the same conversations? Are we facing the same kind of issues? I think what I’ve just heard from my fellow panellists are some of the conversations that are happening in the UK. Yes, there is change. Yes, there is disruption happening. And to your point, absolutely, what we’re seeing is a lot of roles, not just in our industry and sector, but across industries and sectors, moving from very much admin tasks, very much cognitive tasks. Those are being increasingly automated. But then that’s freeing people up to do more problem -solving and to look at more client advisory governance and using and being able to shift those skills to look at AI governance. But also I would say client -facing as well, which goes to your point around skills.

I’ll come back to your broader question but yes it’s technical, yes it’s governance looking at other skills but it’s also those people skills, those human skills if we are shifting people, if jobs are shifting towards more of yes this automation can do the job but what’s the added value that I can provide and it’s my human skills which sounds very weird to say human skills, you know what I mean it’s that ability to interact, it’s that social, more social skills then are we teaching those as well as the technical as well as the legal, the governance as well as the software, as well as the technical skills, are we also teaching people and the young people coming through how to interact with people as well if they’re more client facing so absolutely the disruption we’re feeling it in the UK, we’re having that discussion in the UK, definitely in the industry is questioning what will my role be, where will I sit government is in the UK is focusing very squarely on this so as part of its AI opportunities action plan the UK government has created an AI skills partnership bringing together the government bodies that are looking at how do we upskill, how do we retrain, how do we get society ready for this next wave of AI that’s coming, not just the one we have now, but the one that’s coming down the line, and bringing together with companies and bodies such as TechUK and others to look at how do we do this in collaboration.

So how do we reach the wider population, and I’m not just thinking our industry here, but the wider society population with what are the training courses, what are the upskilling courses, what are the opportunities to learn and gain skills to thrive in an AI world, but then also how do we train our industry and sector for the shift that is happening as well. I think generally that task force is looking to train over one million people in AI so that we can help the greater population. be ready for working in this era. I think there is anxiety. I think there is concern. Some workers understandably worrying about displacement, worrying about if they’re at high exposure to automation, what does that mean?

How do they shift? How do they move? But I think what we are looking at is how do you turn and this is a word I’ve heard a lot about this week, how do you turn anxiety into agency? How do we encourage people to take a lead, lead, to take what they’ve learned but as you said, continuous learning, continuous upskilling because that is what you will need to thrive in this world. But I think what we’re looking at in terms of helping people do that is through restructured training, reskilling programs. It’s pathways for mid -career into new careers. One of the very interesting initiatives that the UK government introduced was around how people coming out of university that might not have an AI degree, can do a one -year conversion course to become then able to work in the AI industry.

so I think there are lots of, perhaps we’ll go into a little bit more, there are lots of different initiatives that the UK are doing which could be applicable here and vice versa, we want to learn from how you’re addressing this but I think there is anxiety but then how do you turn that into opportunity and agency

and you know one of the issues in India we keep talking with the government is that we have a very disaggregated focus right now within India, there are multiple governments multiple state governments, organisations places, organisations like NASSCOM, we’re all trying to do some part of the pie but there is no if I can use that word, whole of government or whole of country approach right, I’m saying this is how if this is such a big disruption, this is how we will go about doing it, do you see that in the UK that there is an integrated approach and then obviously every actor has their own role to play in that

I think it’s coming first of all I don’t think there’s a silver bullet, I don’t think there’s one pure answer because the moment, as you said things are moving rapidly and quickly the moment you put in a task force or initiative, it may very quickly need to shift and need to change. So I think in all of these, and AI generally having an iterative, flexible approach that can adapt and shift as technology evolves and has new developments evolve is really, really key. So I think the AI skills partnership, which we’ve signed up to with the UK government, has really kind of become a bit of a cornerstone, a bit of a nucleus of how do we retrain, how do we upskill the general population.

But then I think there’s also the conversation about how are we ensuring our schools, our education curriculum, what young people are learning in schools, how is that joined up to the AI revolution? And I think while there’s some thinking there, I think that could be more joined up. And then, yes, of course, how are we training the industry? How are we getting people leaving, as you said, the freshers leaving universities with the skills that we need as industry? TechUK is part of part of TechUK is an organisation called TechSkills, go and check them out not right now but maybe afterwards and we at TechSkills work directly with employers directly with technology companies and universities so we be that bridge between the two to make sure that industry employers can provide input into the university, the courses what they’re teaching students so that when they come out of university they have a degree, it’s called a TechSkills Gold Accreditation Degree which means employers will recognise that degree and kind of go, yes you’ve got what I need, come and work for me so there’s no one single answer to this I think it’s a number of initiatives that need to work together but at TechUK as others we’re trying really really hard to join the dots but I think the TechSkills addresses the what do employees need from universities, how do we get universities and employers employers working more closer together what role can government do and what can government do that industry can’t and vice versa what can industry do that government can’t it’s really got to be a partnership and a collaboration but there’s no one I think single initiative that will in my view that will fix this or solve this or address this

I think that’s probably a great way to think about it that there’s just so many changes that one single, there’s no single silver bullet like you said, you really got to figure out a way how you tie the different threads but let maybe a thousand flowers bloom because that’s the nature of what we’re dealing with right if you can bring it together and say here’s our coordinated approach I definitely think in the UK we could join up more these initiatives and maybe India with your scale can do that and you’ve definitely brought the world together in the summit so I’ve no doubt that you can definitely do that wonderful so Keesh if I can come back to you right again from an IT services perspective we’ve been always one of the largest employers for the engineering talent in this country now with the new skills that Ravi talked about do you see this as a way to focus will be largely on more elite top tier institutions and a large volume of students that were probably studying in tier 2, tier 3 colleges across the country and had a phenomenal career in our industry.

We are closing out opportunities for them.

I want to make a point on a previous question and then I’ll address this. While, you know, and I agree there’s no silver bullet. However, I’ll say that, you know, I live in the US. The conversation I hear about policy around AI is should we regulate, should we not regulate? Who should regulate? Should it be the state? Should it be the central government? I’m not hearing what I heard here, which is a big focus on inclusiveness. And I think while, you know, it may not have all of the… I think while, you know, it may not have all of the… I think it’s still a very material difference in approach of how government I see here is thinking about.

And actually, I heard that from the UK. I did minister there before. I heard from President Macron yesterday in the plenary session. So I think there’s a big difference in some of countries relative to at least what I’m hearing in the U .S., much more focused on how to make it work for everyone. How to make it inclusive, which I think is a huge difference. I think it will lead to a very material difference in outcomes over a period of time. Now, coming back to your question. So first, do I know all the answers? No. But here are some things, some pieces that I think are true. First. I think we’re going to have to look at the data.

I think we’re going to have to look at the data. I think we’re going to have to look at the data. I think the I’ve seen young air native talent is much better at many things than think somebody even who’s in their 30s and trying to retrain them it’s much like you know do you use Instagram I don’t actually you know but there are kids who are grown up with it right so I think it’s the same difference the digital native I think you’re going to see an air native generation and we find actually like last year the there’s like a set of people we hire from the absolute top engineering schools like IT we had them train our management team on white coding in May last year because white coding back then was brand new and they were like and guess who the kids were the best in it in the company the people who came out of college right there in the best so we had them trainer so I think this part is going to be true right whenever we think of pyramid we have to bear in mind that sometimes the best talent is the youngest one that is coming the second one that’s going to choose I think ultimately the new opportunities cleared by AI go far outlaw far greater than the number of jobs this direct could reduce now there’s going to a period where you know there is a transition period and I’m not sure exactly you know how to clear but I’m very confident that ultimately AI is going to so many more things that will need building applications building tech for and I think power I think the third is also true that for kids the problem to solve is not tech is not coding.

It’s not, you know, creating data structures or whatever it is that kids are trying to solve. I think that’s a solved problem by some of the tech, by AI. So now you’ve got to think of what problems that you want to solve, which is something else, which is where the big

So, Keech, I’m going to hold you to that where you said AI will create more jobs than it changes. So we’ll see how that plays out. But you know, one of the conversations I was having with another IT services company, and they were like this AI native talent is great, but that talent will have never learned to, you know, work without AI. And does that mean that some of your foundational and core skills will not be as solid as they were in the past because this is the world you’ve grown up with, and your dependence on these tools will be so high that does it lead to a lack of some foundational skills also, right?

Listen, And I was in the United States for a couple of years. And I was in the United States for a couple of years. And I was in the United States for a couple of years. And I was in the United States for a couple of years. And I was in the United States for a couple of years. There was a time when coding you had to do in C++, right? And then there were, the whole evolution of coding as an example has been abstracting what you need to code for to something, right? So you wouldn’t have IDEs like I don’t know how many years ago, right? But who codes without an IDE now? Nobody, right?

And that’s been true for whatever, a decade. So I think that same question will become who codes now? And I don’t think anybody will code, okay? That’ll be a solved problem. So no. Is it going to be a discipline? I think far from it. I think it’s going to become a significant advantage. I think the cost of coding is going to become zero. Cost of code is going to become zero. What that means is you can solve. any number of problems with code that you couldn’t solve for before because it is too complex or too expensive to do so. So, absolutely not. I think it’s going to be a big advantage.

Yeah, no, really fascinating. I think just on the coding point, you’re absolutely right. And I’m just thinking as a woman in tech as well, we had a big focus in the UK of getting girls into coding. Brilliant. But actually well now, why? But there is also an opportunity there but there’s also a risk. So coding, AI for coding, great. But we will need somebody to check the code. So again, it’s that shifting and that moving of skills. And then my brain went to okay, well the people that were doing the code could we reskill them into checking the code and going more into governance. But then my brain goes to, but hang on, but AI might be able to check the code quicker than a human can.

but then you get to that point of somebody then needs to check that the AI has checked the code correctly so there is, you know, you’re baking in governance and assurance in AI, humans will need to be in the loop, so how can people in the coding world be shifted in their role, shifted to help more on the governance side I did have another point, however my jet lag brain means I’ve forgotten it, so I’ll give away

But if you’ve never coded in your life how do you know what to check for?

Oh, I remembered my point that’s kind of related in a way to the gentleman from Mastercard was saying about context and the completely important context is really, really key and something that is in my brain as well is that people that work in organisations over the last couple of years, they have, you know, done junior roles, they’ve learnt the company they’ve learnt the sector, they’ve learnt the industry they’ve kind of done the grunt work you know, to learn the context and learn what’s important and what’s important and what’s important and what’s important what concerns me slightly is that people coming in using AI will not using AI but when do we give them time to learn the company, when do we give them time to learn the context, are they getting exposed to, you know when I first started in a company, I started in the basement I worked my way up but I knew my sector, I knew my industry, I knew that background I knew that context, I knew what I was checking and why so if automation takes those junior roles away, how are we teaching people, how are people getting exposed to the context and what a fintech industry needs and what it looks like if those opportunities which came through more junior roles are now no longer there, so I think there’s huge opportunities here but there’s also some rethinking we need to do as an industry and a sector of are we skilling people with the right things for what the industry needs going forward as well

Thank you so Ravi if you want to go both on the question on we have a million plus engineers graduating every year what are the jobs for them and you know obviously you’ve talked about the skills they need but will we as and you know today tech jobs are not just in the tech industry they are in every sector but what you see as the opportunity for them and secondly this whole part about right what will humans do if AI does all the coding sorry what would humans what would humans do or the engineer do if AI is going to do all the coding right so

flows, how operational controls shape risk over time and when to intervene. Then I think we have to make governance interdisciplinary and influential which is requiring fluency for people and putting things together along law, technology, ethics, operations. Like I mentioned before, privacy, AI, governance, they cannot operate only in silos. So the future readiness requires a big structural change in design, in procurement design, deployment. And we also have to close the uneven digital capability across institutions. We talked about that. If there are central agencies and large enterprises can attract talent, and large can attract talent, then we have to while smaller cannot. So that will create governance gaps and governance gaps especially where AI is expanding the most.

And those are risks that we need to make sure that we have the right solutions or the right thought process because it is around going beyond kind of elite specialization towards more of a broad -based AI digital literacy. So at MasterCard, like, you know, what we do is I think that, you know, we have spent, you know, several of our last years operationalizing responsible AI, right? And not just as a policy exercise but as a workforce and capability challenge. Now, we have a very formal established AI governance framework. We have a chief AI and data governance officer. We have a chief privacy officer, you know. And we have a privacy by design approach into everything.

ensure that AI risks are addressed before systems are built and deployed and not afterwards. And we have an AI governance team that is working horizontally across data, science, product, legal, compliance, engineering because knowing how important that integration layer is because we have and then the product and engineering leaders, you could say they are the first line stewards of risk and AI risk. They are not kind of the recipients of compliance decisions. They are the stewards up front. But that happens when you get that right integration up front. So I think that for us from a MasterCard perspective what we have learned that governing AI at scale, it’s fundamentally a workforce challenge that requires interdisciplinary skill.

and early integration is required into product design and we need governance professionals who can manage risk and not just enforce rules. So it is a privacy by design, security by design. Those are kind of core principles, but then how do you bring those things together in this evolving is important.

And I think that’s a fascinating part of this conversation, right? The whole focus on ethics, principles, trust, security, privacy by design, right? And as you think about, Ravi, going back to this large student workforce, right, that we are building for tomorrow, how do we get them to imbibe many of these principles? Obviously, when they come into your organizations, there’s structured programs that you’re running to drive this thinking. But if we had to take this back to the whole college -university ecosystem, any recommendations? Any recommendations you have on how to drive that?

I think, no, absolutely. So clearly, you know, from a corporation perspective, I think, you know, I was looking at this morning when I took a picture of that. I think when I, you know, opened the news this morning, you know, the very first thing on the TV was around, you know, the headlines were AI skills or skill gap, right? And a lot of discussion based on, you know, obviously this week of what’s happening at the, you know, as part of this summit, right? So, and I think that, you know, clearly the role that business, academia, government, right, we all have a role to play in navigating this workforce transition. I think for corporations, it’s not just enough to say you’re offering internships, right, you know, to students.

I personally feel, you know, how are we taking our, maybe, you know, chief learning officer, or other, or engineering. kind of who are at the front line, how are they working with, you know, people in academia and actually helping think through and design courses based on real world examples of, and situations that are coming, you know, then, and certainly, obviously, when people come into internships, it helps them get that exposure, take that back into their learning environment. But I think in, you know, the whole facet of curriculum and curriculum design is changing, where it needs, it should not be only restricted only to computer science majors, but I think that this is something that is required, you know, in terms of AI in every different form across a broad set of disciplines, right?

So it’s not something that we can leave it only for, you know, computer science majors, you know, per se. So I think that the, you know, in terms of priority, embedding AI governance and interdisciplinary. interdisciplinary collaboration into, you know, is one of the very first layers that we have to begin with. So that, you know, the people coming in, you know, as you talked about the engineer, they’re trained to think across the full life cycle of AI system, you know, and not just in a very silent approach, right? And that is what talked about bringing engineers, product, risk, policy, all of those, you know, together. And, you know, then I think another priority is, I know we talk about it and we have to think, focus on role redesigning and not just reskilling.

And I think that, you know, because AI is transforming tasks within jobs rather than eliminating, you know, roles entirely. So I think that the work, you know, we have to see is how do we kind of redesign roles, right? Rather than only focusing on reskilling, right? And we have to build inclusive and distributed talent pipeline. So I think here, I mean, I go back to CII, you know, and other organizations where we have worked with where you go on the field and you’re working, let us say, with MSMEs, right? And, you know, working with the last mile, understanding their challenges and their pain points and bringing that into our product design and, you know, and output that’s required.

And because that provides the context. Right. So I think that, you know, the ability to take our talent pipeline and expose them to real world and helping them contextualize, you know, is very, very important.

Thank you. Kish, if I can follow up the question with you, right? I think Ravi spoke about two themes. One is role redesign. So how are you seeing the role redesign happen from a technology services context? And secondly, I think there’s so much we hear about the changing role of the engineer. Now this whole forward deployed engineers becoming like the new buzzword in town. How are you seeing this happen in your organization?

Thank you. I mean specifically on role redesign that is absolutely true I mean just again going back to software engineering you think of a typical kind of squad that builds software may have had 7, 8, 10 people some developers some testers scrum master typical roles I think in the extreme case we are seeing down to 3 people one product owner one developer one tester and that substantial redesign of the role and the time it takes to do it is coming down from 2 weeks to 2 days so yes you won’t see value unless you are redesigning the role you won’t see real value from AI now we have been speaking a lot about capabilities right I I I think we should spend enough time on adoption.

And I think there is a pretty big gap. Actually, I think that gap is good for workforce. Because no matter what the capabilities are, I think by the time it becomes real, adopted at scale into workforce, into our enterprise customers, it’s several years. In aggregate, ultimately, I think of the impact of work, and hence workforce, is maybe in low single -digit percentages per year at most. Even 1 to 2 percent right now. Maybe they’ll expand to 2 to 3 next year. This is because of the speed of adoption and the multiple constraints in adoption. Because I don’t think AI knows context. Right? Right. Everybody’s speaking in a watered -like… But, you know, mad could mean what the word mad means for one enterprise.

It could mean the old world for Chennai and another enterprise, right? So, there are many reasons why adoption, I think, is going to be slow. But, and frankly, one of the reasons is role redesign, because it is not as simple as getting a coding toy or whatever data tool. It is an organizational redesign to make that happen.

And are you engaged in enabling all your employees to be able to use these tools, given, you know, some of the issues around governance risks that are being talked about?

Yeah, 100%. Okay. I think it is kind of… A little bit silly to tell employees that you cannot use. We’ve already got… We are already in the second generation of retaining our employees on the air. I think first generation was whatever, on Gen AI and I would say even as of Jan last year, the whole concept of agentic came in, whatever you learn till that point becomes useless. And so we are doing that second generation of training. Now, what we found is that earlier we used to mandate training. We wanted everybody to learn and we were pushing employees to learn. Suddenly we stopped it. We said, hey, it’s up to you. The truth is, if you don’t learn, you are going to be redundant.

Yeah, so it’s not for us that we learn. It’s for you. And suddenly we’re finding that the number of people who are actually getting trained is more, not less, once you stop mandating it. So, yeah, I mean, are there privacy risks with Facebook? do people use it? The answer for both is yes. So I think you’re just going to find a generation of people who think about the resources here and very differently.

So you know yesterday at the Impact Summit, the CEO of Anthropic spoke about I think what was the 100x geniuses in a data center, right? That’s the kind of intelligence at scale that will exist as these technologies really mature to a deployment and scale the deployment gap. How do you see the role of humans shifting and what is this human -AI collaboration that we are all talking about, right?

See, the thing is this, I tell my customers this. Stop chasing the shiniest object. There is always going to be advancement in technology every month, every two months, every three months. Something better will come. And in the quest to keep chasing that, actually what you’re doing is not realizing value from anything. So, for me, most enterprises can get significant value if they fully adopt systematically capabilities that existed a year ago. Certainly capabilities that existed six months ago. So, what are the relevance of data center full of geniuses for most enterprises? I think it’s zero. What problems can it solve that enterprises… I think enterprise problems are not to do with IQ. It is far more complex than a linear IQ issue.

So, I think yes, it may be true that AI can do like a thousand things that humans can’t, but it’s not relevant. So I think the real focus is not about capability, about how do you help enterprise adopt and that is the real answer to your earlier problem, earlier question. What do people do if machines do coding? Actually the problem you are trying to solve is not writing code, you are trying to always solve for some other problem. I think that’s the re -skilling that engineers and young talent need to go through. For me now, AI knowledge is like English, it’s foundational, it’s fundamental. I need to be in the business of solving for something else.

And there I think the point you have made several times in terms of engineering, engineering and interdisciplinary I think is crucial. So how many times do you go to a doctor and get frustrated? Listen, I don’t want an eye doctor. I don’t want a nose doctor. I actually want a doctor. Right? And you know, that’s true in engineering. You think about robotics. You don’t want a mechanical engineer. You don’t want a software engineer. You don’t want an AI engineer. You don’t want an electrical engineer. You want an engineer. And I think that is where our talent needs to go. Now, frankly, I think academia has a big job to do to help them get there because our courses are not designed like this right now.

They’re designed as electrical and whatever else. But I think young talent who are reorienting themselves that, hey, AI is not the skill. AI is very foundational. But I’m going to use that to solve for something more meaningful. I think we’ll just be fine on workforce.

yeah so if i can come to you right i think you’ve heard a lot about how learning has to change and uh you know whether it’s critical thinking that we’re talking about problem solving experiential use case based uh but at the same time you need access to data you need access to compute you need access to research right so how how are you think how how is uk thinking about this and you know are there examples that india can learn from from there

yeah absolutely so when we think about realizing the opportunities economic and social opportunities of ai it isn’t just about obviously skills skills is part of it but it’s it’s about it getting to use that word again the foundation’s right so in the uk particularly last year we focused a lot around um and a lot of initiatives a lot of investment has been put into getting the infrastructure right so whether that is looking at our data infrastructure um the uk government infrastructure right so whether that is looking at our data um announcing a national data library initiative to try and um announcing a national data library initiative to try and um announcing a national data library initiative to try and we have, well I was about to say we have huge data sets but you guys have massive data sets, but the data sets we have, how are we using them, how are we bringing them together, not just for public services and public sector use but potentially for industry use as well so data infrastructure absolutely, a lot of investments gone into compute infrastructure so the creation of AI growth zones so dedicated areas in the UK where perhaps we don’t have the compute infrastructure resource right now, how are we building that, part of it is also investment gone into AI, so an AI research resource, so dedicated computer resource compute power chips to allow AI researchers at that fundamental research level to do the work that they’re doing as well so absolutely a lot of focus and I think if I think about and if I reflect on the last, when 2025 in the UK yes the conversation was a lot about how do we get the foundations right, how are we getting the infrastructure right where I think and where I want the conversation to shift is to now adoption yes we’ve been talking about adoption there is already adoption happening in the UK whether it’s financial services, whether it’s in our healthcare system whether it’s transport, logistics but boy there’s so much potential completely agree and at Tech UK we’re really looking at how do we accelerate that AI adoption at pace and speed in a way that we don’t get it wrong from a governance, from an ethics, from a responsible from a regulation point of view absolutely and how do we get it right for people but how do we move quickly enough to realise the opportunity and that’s really really something that we’ll be advocating for more this year because again what can government do to help that but what can we as industry particularly the tech industry help other sectors and industries to understand how they can do that as well and that’s really our core mission of my work at Tech UK and I’m really excited about the future of Tech UK and I’m really excited about the future of Tech UK skills comes into it of course but also does public trust and confidence none of what we’re talking about here is going to really fly if people don’t trust and have confidence in using AI so there is, or having AI used about them so there’s lots of initiatives happening, compute infrastructure absolutely, access to data making sure that researchers have what they need, industry have what they need SMEs have what they need but skills is an integral part of that it’s all linked, it’s all connected but I completely agree adoption is really the key and I was at a UK, I had a reception last night, the High Commission and the Rishi Sunak, the previous Prime Minister was talking about which country will win the AI race, we’re talking about sovereignty we’re talking about the previous panel was talking about sovereignty is kind of key for India it’s key for a lot of countries and we’re looking at what does data what does tech, what does AI sovereignty mean for the UK but Ritchie Sunak’s point was like the countries that will win the race in AI are not the countries that are looking at sovereignty or looking at stack or looking at infrastructure it’s the countries that can demonstrate adoption and can win the race in adoption and that can integrate AI across all the sectors and across all your industry and your economy and definitely in the UK we’re very much tying digital AI adoption and deployment diffusion into society into our economy as a key driver of growth and productivity as well so lots going on but with that central core theme of how do we get this right as well.

I fully agree I think getting deployment right is really the opportunity or challenge for economies that are not competing for the LLMs right so I think that that’s what India has to get right because AI can help solve to Keisha’s point we necessarily the shiniest toy is not needed for the enterprise it’s needed to solve India’s deep healthcare challenges it’s needed to solve some of our agriculture related issues right and I think that’s where the whole inclusion focus and what AI can do for you it really means.

I think sometimes we have to take a step back and just realize how transformational, how exciting this technology is. I mean, many of us have been talking about this for a number of years. But where we are in terms of compute infrastructure and compute power that we never had before, in terms of the digital data and the data sets that we’ve never really had before, I don’t know, I’m feeling quite this does feel like a step change. This does feel like a different moment in time. And it’s how do we grasp that moment in time, which I think is really important. How do we help young people and everybody working in the industry to understand what grasping this opportunity means for them as well?

No, I think we’re reaching the end of our session, but I just want to get to the last session and quick comments from all of you, right? You know, what would be, Ravi, your top three priorities for business, academia, and government to successfully navigate this AI workforce transition? And, you know, what are some risks it should plan for?

Great question. I think like, you know, the priorities, I think I mentioned, you know, to you about this whole interdisciplinary collaboration, the whole, you know, aspect around redesign and so forth, right? And I think in risks, I would also see, I think like, if I go to, you know, how what we’ve been talking around AI and how it has democratized access and so forth, right? But there is also the concentration risk that we have to be aware about, right? Because kind of when we have a small set of institutions or companies or talent pools pull ahead disproportionately because they have access to better data or compute and research ecosystems, right? Then I think we have to be very deliberate in how we design for systems.

Right? Right. I think this is where. You know, India, we have a position of strength because, you know, our engineers and you talked about the million plus engineers that are, you know, we are coming from a position of strength because India has contributed to the global technology revolution. You look at all the growth of our global capability centers, you know, kind of reflect that depth of the talent pool, you know, that exists, right? And I think that we have to, as we go forward, you know, get that, you know, design aspect right, right? Because foundational digital and AI literacy into school curriculum, right? Because equitable access to tools, infrastructure, right? Hands -on exposure across geographies, right?

So, and then also we have to go beyond top tier institutions to tier two. Tier two and tier three because other. Otherwise, again, we’ll come back to a concentration risk, you know, that will exist. And, you know, because we don’t need just people who can build AI. We can, we need folks, you know, and professionals who can build with AI, who can govern AI, and who can, and know when to override AI, right? So I think that’s kind of important. So, and we have to make sure that in terms of risk, we don’t go towards over -automation, you know, without adequate human oversight. And, you know, biases need to be taken into account because it should work well for both formal workers as well as informal workers, right?

Women entrepreneurs, you know, vernacular, because, and we talked about context and the contextual aspect of it, right? So we, otherwise, we risk exclusion at scale. And, you know, to Sri’s point, we want that inclusion that you talked about. You know, we have a lot of people who have been in the industry for a long time, and we’ve had a lot of people who have been in the industry for a long time, and we’ve had a lot of people who have been in the industry for a long time, and we’ve had a lot of people who have been in the industry for a long time, and we’ve had a lot of people who have been in the industry for a long time, and we’ve had a lot of people who have been in the industry for a long time, and we’ve had a lot of people who have been in the industry for a long time, You know, and I already talked about the…

I’m sorry, we know we are ending the session, so whoever is ringing the bell, please, we’ll be there on time. Yeah, okay. So, okay, so therefore, I mean, I’ll just conclude there that, you know, it is about this transformation that we need

So I think you articulated it very well, right, the risk of concentration, the risk of exclusion, and obviously not doing it very thoughtfully, right? So I think those are very, very well articulated. So if I can come to you, right, what do you see both as from a workforce transition framework, what are our big opportunities and risks, right?

Yeah, I’m glad you could hear that bell as well. I thought it was so funny in my head. So the question in terms of priorities, so very quickly for businesses, so touching on some of the points you were making as well, embed lifelong learning we need to continuously learn we all do actually but also our organisations I think think about for businesses not just jobs and roles but tasks, what are organisations looking for people to do and I think also organisations need to think about the opportunities but the risks they need to invest in human skills along with technical skills, governance skills but for government as well we see something in the UK that we think should be prioritised and I don’t know if this will resonate with here in India but it’s interoperability of skills credentials so if I get a credential if we’re focusing on lifelong learning if I learn a skill, if I take a course, if I have a credential how is that transferable can that be recognised elsewhere because people will need to shift and people will need to move but also a national taxonomy of skills and perhaps requirements and fundamental foundational skills that we’re talking about?

Are we all talking the same language? Are we all talking about the same skills? Some priorities there, but I’ll leave it there.

So a new skills taxonomy and interoperability of skills, I think that’s going to be very important in this environment. But technology is changing so fast, right? Because what was applicable last year is now going to be applicable this year. Keech, if we can come back to you for the closing comments. How are you seeing this?

I’ll maybe just say one thing, okay? Sorry. I think inclusiveness has to be by design.

Okay, we’re just ending. We said that we’re ending. It’s just 24 seconds, right? Yeah, why didn’t you just close that, Keech?

If you look at it, internet is very inclusive. That’s because academia made something free. I think we need academia to do that for AI. that’s how it become more inclusive and I think this has to be a huge priority

Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. To welcome you all today for our session, Reimagining AI and STEM Education for India’s Next Generation. Celebrating the vision of Vixit Bharat and its grandeur, we are witnessing the AI revolution during the AI India Summit. With a young population and vibrant digital ecosystem and strong policy momentum, we are uniquely positioned to harness AI not only for the economic future,