Building Trusted AI at Scale Cities Startups & Digital Sovereignty – Panel Discussion Moderator Amitabh Kant NITI

20 Feb 2026 13:00h - 14:00h

Building Trusted AI at Scale Cities Startups & Digital Sovereignty – Panel Discussion Moderator Amitabh Kant NITI

Session at a glanceSummary, keypoints, and speakers overview

Summary

The panel at the AI Impact Summit 2026 examined how rapid AI advances are reshaping India’s IT services, SaaS models, and broader economic productivity [6-11]. Arundhati Bhattacharya cautioned that market headlines about a 40 % drop in Salesforce’s valuation and a “AI-agent-replacing” SaaS model are overstated, emphasizing that successful SaaS requires workflow understanding, governance, observability and adoption, not just low-code development [12-23]. She added that the current models of working will evolve and firms must stay agile to add value, noting that many players will emerge and the ultimate test is whether AI improves living standards [30-38].


K. Krithivasan explained that AI will shift many engineers from writing code to orchestrating AI systems, but system integrators will remain essential for testing, validation, requirements and cybersecurity, especially as cloud adoption is still only 30-40 % and enterprises must rationalize data estates and train multiple models [46-70]. Salil Parekh highlighted a $300 billion AI services opportunity for Indian firms, citing AI engineering, legacy-modernisation and the company’s Topaz Fabric IP layer that enables clients to work across foundation models and custom agents [79-102]. C. Vijayakumar described HCL Tech’s unique position with a software product line, custom silicon and high revenue per employee, and said the company will focus on building solutions that bridge the gap between foundation models and enterprise needs rather than becoming a hyperscaler [108-127].


On the talent side, Krithivasan reported a recent workshop where 1,500 schoolchildren built 1,500 apps in three hours, illustrating AI’s potential to upskill non-technical youth and the industry’s collaboration with the Ministry of IT on curricula [135-147]. Arundhati expanded this view, arguing that democratizing AI for MSMEs and blue-collar workers requires addressing skilling, access to jobs, timely payments and marketplace platforms that certify and match workers, thereby raising overall quality of life [157-177]. Salil noted that India is already leveraging its digital public infrastructure to roll out AI pilots in agriculture, health and education, with support from chip, data-center and architectural layers to make AI services widely affordable [183-192].


Vijayakumar warned that capturing the projected $350-400 billion AI services market will demand substantially higher R&D spending, citing a trillion-dollar “physical AI” opportunity and the need to build solution labs ahead of demand [200-215]. Both Krithivasan and Vijayakumar agreed that AI is likely to create more jobs than it destroys, though the new roles will emphasize programming fundamentals, critical thinking and the ability to orchestrate multiple AI agents [299-304][283-296]. Salil also stressed the importance of responsible-AI frameworks to ensure ethical model training and deployment [306-311]. Concluding, Amitabh Kant summarized the panel’s optimism that AI will drive a “Vixit Bharat” by 2047, generating diverse employment and helping India reach a $30 trillion economy [312-317].


Keypoints


Major discussion points


AI’s impact on the traditional SaaS model and Indian enterprises – Arundhati Bhattacharya cautioned that market hype (e.g., Salesforce’s 40 % valuation drop) does not automatically invalidate the SaaS model; she emphasized that SaaS success still depends on workflow understanding, governance, observability and delivering concrete customer value, and that the “jury is still out” on whether AI agents will replace it outright [12-23][30-38].


The evolving nature of IT services work – K. Krithivasan argued that AI will not eliminate system-integrator roles but will shift emphasis toward requirements-engineering, context-engineering, validation, security and cloud-adoption; the volume of work will grow rather than shrink, creating “more interesting work” [46-53][58-70].


Infosys’s AI services opportunity and IP strategy – Salil Parekh described a $300 billion AI-services opportunity across six focus areas (e.g., AI engineering, legacy modernization) and highlighted Infosys’s proprietary “Topaz Fabric” IP layer that abstracts foundation models and agents, signalling a move from pure “builder-for-hire” to owning AI stack IP [79-88][98-102].


HCL Tech’s positioning in the AI stack – C. Vijayakumar explained that HCL leverages its product business and custom silicon capabilities to build enterprise-grade solutions that bridge the gap between foundation models and practical use cases, while deliberately avoiding a hyperscaler role and focusing on solution-centric IP [108-118][124-127].


Skilling, democratization of AI and national digital public infrastructure – The panel stressed that AI must be made accessible to blue-collar workers and MSMEs, requiring new curricula, community-level training, and a DPI-style AI infrastructure (agriculture, health, education) to uplift productivity across the country [135-147][157-176][183-192].


Overall purpose / goal of the discussion


The session was convened as the closing panel of the AI Impact Summit 2026 to assess how generative AI will reshape India’s massive IT services ecosystem, to debate the sustainability of existing business models (SaaS, services), to outline strategic responses (new service lines, IP creation, partnerships), and to chart a national skilling and infrastructure roadmap that ensures AI-driven productivity and job creation for both white- and blue-collar segments.


Overall tone and its evolution


– The conversation began with a formal, probing tone, as the moderator posed a challenging market-valuation question to Arundhati [11-13].


– It then shifted to a balanced, analytical tone, with panelists dissecting technical and workforce implications (Krithivasan’s and Salil’s detailed explanations) [46-70][79-88].


– When discussing corporate strategy (Infosys, HCL) the tone became pragmatic and forward-looking, highlighting concrete IP initiatives and partnership models [98-102][124-127].


– The later segment on skilling and public AI infrastructure adopted an optimistic, inclusive tone, emphasizing democratization and national-scale impact [135-147][157-176][183-192].


– The moderator closed with a hopeful, rallying tone, projecting AI as a catalyst for massive job creation and a “Vixit Bharat” future [312-317].


Overall, the discussion moved from cautious skepticism about market hype to confident optimism about India’s capacity to harness AI through strategic innovation, skill development, and public-sector support.


Speakers

Moderator – Session moderator for the AI Impact Summit 2026. Role: Moderator of the panel discussion. [S13]


Amitabh Kant – Host and moderator of the panel. Role: Moderator (referred to as “Mr. Amitabh Kant”). Expertise: Indian IT industry, AI policy. [S6]


Arundhati Bhattacharya – Former SBI CEO and current technology leader. Role: Former Chairman & MD of State Bank of India; now a tech leader focusing on SaaS and AI. Expertise: Banking, technology, SaaS, AI. [S16]


Salil Parekh – CEO of Infosys. Role: Chief Executive Officer, Infosys Ltd. Expertise: IT services, AI services, digital transformation. [S9]


K. Krithivasan – CEO of Tata Consultancy Services (TCS). Role: Chief Executive Officer, TCS. Expertise: IT services, AI-driven workforce transformation. [S11]


C. Vijayakumar – Senior executive of HCLTech. Role: Senior leader (often referred as “C. Vijayakumar”) at HCL Technologies. Expertise: IT services, hardware/AI chips, enterprise AI solutions. [S18]


Navneet Kaul – Audience member who asked a question about AI-created jobs. Role: Audience participant. [S5]


Audience – Various members of the live audience who asked questions (e.g., Mania Sharma, Devika Rao, Kishla, Harswar, etc.). Role: Audience participants. [S1]


Additional speakers:


Christy Varshan – Referred to as “CEO of TCS” early in the discussion (likely a mis-naming of the TCS CEO).


Christy Wilson – Mentioned as “the biggest employer in India,” presumably a senior executive of a large Indian IT firm.


Mania Sharma – CEO of Mono AI, a young entrepreneur seeking mentorship.


Devika Rao – Representative from the University of Leeds, interested in AI-creative education collaborations. [S5]


Kishla – Audience member asking about skill development for current IT employees.


Harswar – Audience member concerned about AI misuse.


Mamanama Venkatana Rasimahati – Software architect and founder of “Startup Sanatana,” advocating culturally-aligned AI.


Rupa Arvindakshan – Leader of Salesforce’s startup community, mentioned as a point of contact for startups.


Full session reportComprehensive analysis and detailed insights

The AI Impact Summit 2026 closed with a panel of senior leaders from India’s IT services sector, moderated by Amitabh Kant. Kant introduced the four panelists – Salil Parekh (CEO, Infosys), K. Krithivasan (CEO, TCS), C. Vijayakumar (CEO, HCL Tech) and Arundhati Bhattacharya (Senior VP, Salesforce) – and set the tone by noting that the industry was “at a point of disruption” [1-11]. He also highlighted that the Indian IT services industry “represents over 300 billion USD in market value and employs millions of professionals” [1-3].


Arundhati Bhattacharya responded to a market-driven narrative that AI agents could wipe out the traditional SaaS model. Amitabh Kant’s opening question referenced the recent ≈ 40 % fall in Salesforce’s market value over the past 12 months [11-13]. Bhattacharya warned that such headlines often over-state the impact because SaaS success depends on more than low-code generation; it requires deep workflow understanding, governance, observability, auditability and genuine customer-value delivery [14-23]. She noted that some of the capital flowing into AI-driven SaaS is “circular money” and that investors must read the fine print [24-28]. While acknowledging that current working models will evolve, she argued that the “jury is still out and may remain so for some time” on whether AI will fundamentally overturn SaaS, and that the ultimate test will be whether AI improves living standards [30-38]. When asked about startup support, she directed interested founders to contact Rupa Arvindakshan, whose details are publicly listed on Salesforce’s website [280-282].


K. Krithivasan, CEO of TCS, shifted the focus to the future of IT-services work. He argued that AI will not eliminate system-integrator roles; instead, engineers will move from writing code to orchestrating AI systems, emphasizing requirements-engineering, context-engineering, validation, cybersecurity and testing of AI-generated outputs [46-53]. He highlighted that cloud adoption in Indian enterprises remains only 30-40 % after a decade, meaning a long-tail of migration, data-estate rationalisation and model training will generate a larger volume of more interesting work rather than a headcount shrinkage [58-70].


Salil Parekh, CEO of Infosys, outlined the company’s view of the AI-services opportunity. He cited a $300 billion market across six focus areas – AI engineering, legacy modernisation, AI factories, AI agents, physical AI and AI-driven analytics – and presented internal data showing aggressive hiring: 20 000 graduates recruited this year [91] and 13 000 added in the first three quarters [92]. Parekh also described Infosys’s proprietary “Topaz Fabric” IP layer, which abstracts foundation models and custom agents, allowing clients to work with any model while retaining Infosys-built capabilities [98-102]. This signals a strategic move from pure “builder-for-hire” to owning a reusable AI stack.


C. Vijayakumar, CEO of HCL Tech, explained his firm’s positioning. He noted that HCL’s software-product business contributes about 10 % of revenue and that the company has built custom silicon – a two-nanometre chip – for a major technology client, giving it a high revenue-per-employee metric [108-113]. HCL’s AI strategy is to bridge the gap between foundation models and enterprise use-cases, developing IP that makes large models scalable for businesses rather than attempting to become a hyperscaler or to build its own foundation models [118-127]. This pragmatic focus aligns with HCL’s decision to stay “solution-centric” while partnering with major solution providers [124-125].


Krithivasan then turned to talent development, describing a workshop in the NCR where 1 500 schoolchildren, most with no technical background, built 1 500 apps in three hours using AI-assisted native-language coding [130-138]. He framed skilling as a “major national challenge” and said the workshop was part of a broader collaboration with the Ministry of IT to design AI curricula for universities [130-138].


Bhattacharya expanded the democratisation theme, arguing that AI must be made accessible to blue-collar workers and MSMEs. She listed the challenges faced by carpenters, plumbers, hospitality staff and Anganwadi workers – skill gaps, lack of job visibility, payment delays and weak community safety nets [158-166]. She suggested AI-driven platforms could certify skills, match workers to opportunities and improve quality of life for both workers and their customers [170-176].


Parekh linked corporate strategy to national policy by describing an emerging digital public infrastructure for AI that mirrors India’s earlier DPI achievements (Aadhaar, UPI). He cited pilot projects in agriculture, health and education that are already being rolled out with support from chip, data-centre and architectural layers, and noted ongoing work with ministries to make AI services affordable and widely available [188-192].


Vijayakumar warned that capturing the projected $350-$400 billion AI-services market will require a substantial increase in R&D intensity. He pointed to a trillion-dollar “physical AI” opportunity, estimating at least $200 billion of services revenue, and argued that Indian firms must invest now in labs, proofs-of-concept and solution-building before demand materialises [200-209][210-215]. He added that outcome-based contracts will eventually fund higher R&D spend, but a proactive investment is needed to stay ahead of the curve [211-215].


All panelists agreed that AI will be a net job creator. Krithivasan asserted that AI will generate many new jobs in India, albeit in different occupational categories [299-304]; Vijayakumar reinforced that while programming fundamentals remain essential, the critical future skill will be orchestrating AI agents, i.e., managing multiple AI agents, applying critical thinking and delivering outcomes at several-fold the speed of manual coding [283-297]; and the moderator closed with optimism that AI will help India become a “Vixit Bharat” by 2047, driving a $30 trillion economy and massive employment growth [312-317].


During the audience segment, Kant repeatedly asked participants to keep questions brief, limit themselves to one per person, and be direct, emphasizing gender-balanced participation [250-260]. Questions from young entrepreneurs Mania Sharma and Devika Rao prompted Bhattacharya to direct them to the Salesforce startup community contact (Rupa Arvindakshan) [274-280]. Queries about future job types and required skills were answered by Krithivasan and Vijayakumar, who both stressed the rise of orchestration, analytical thinking and AI-tool proficiency [283-297][299-304]. Concerns about AI misuse, such as disinformation, led Salil Parekh to reiterate the need for responsible-AI frameworks, governance, cultural alignment and ethical model training [306-311][262-267].


Session transcriptComplete transcript of the session
Moderator

With a big round of applause, kindly welcome the panelists of this last panel of AI Impact Summit 2026. Mr. Salil Pareek, Mr. K. Kritivasan, Mr. C. Vijay Kumar and Ms. Arundhati Bhattacharya. With the moderator, Mr. Amitabh Kant. A big round of applause, ladies and gentlemen, to welcome them all to the stage. Well, it’s over to you, Mr. Amitabh Kant.

Amitabh Kant

So let me welcome these very distinguished leaders of the Indian IT. tech service industry and we have with us Arundhati Bhattacharya who is both a banker and a great tech leader now. The three of them amongst them they are leaders of an industry that represents over 300 billion in market value over 25 lakh crore and they employ millions of Indians. We are actually meeting at a point of disruption. I’m not going to take much time in introducing the panel or I’m not going to take much time in giving my own introduction. I will straight away move to asking them questions and then open it up to all of you so that you can ask the questions. I’ll try and start with the lady in the panel and I’ll also try and end with the lady in the panel Arundhati Bhattacharya was probably the most distinguished of us all so let me, Arundhati, let me try and be as direct as possible so Salesforce has lost roughly about 40 % of its market value in just 12 months a single AI product launch wiped almost 285 billion of SaaS stocks in a day the market is saying that AI agents will replace per seat software subsystems is the market wrong or is the traditional SaaS model genuinely under threat and what does that mean for thousands of Indian enterprises that have built their operations on Salesforce platform?

Arundhati Bhattacharya

First and foremost, thank you very much for asking that question. I’ve been answering that question so many times in the last few days that it’s almost like rehearsed, you know, as to how I should go about it. But having said that, you know, markets will say a lot of things. Not all of it comes true. And when you talk about the SaaS model, it’s not only about vibe coding. It’s not only about creating an application. It’s also about, you know, understanding what the workflows are like. It’s about realizing what the customer’s pain points are and ensuring that you are addressing those particular pain points. It’s about observability about what your agents are doing. It’s about governance.

It’s about auditability. It’s about adoption. There are so many pieces to making something really work in an organization that to just say that because I can vibe code, that means, you know, everything else goes out of the window. I think that’s being a little too, you know, little too hasty about totally, you know, rejecting a way of doing business. Also, I must say that, you know, which I’m not very true is correct, but people have to sometimes pump up values given the kind of money that is going in over there. And by the way, some of that money is circular money that’s going in over there. So I’m not too sure that the market is actually giving the right message that it should.

And like for everything that the market gives people, investors especially are requested to read the fine print. And obviously, you know, exercise their discretion in the matter. Having said that, is it true that the models that we have today will remain exactly the same? They won’t. I’m very clear about one thing, which is that all of the models that we have today, and I mean not the LLM models, but I mean the models of working, the ways of working, whether it be in respect of the SaaS companies or it be in respect of any of the other companies, even intra -companies or any of the other companies, things will change. And we have to be very agile about the way we look at these things and realize where are the changes going to come and how can we ourselves change in tune so as to remain relevant, so as to be able to actually add value to your customer.

End of the day, people who add value are the ones who are going to stay. who are going to survive, who are going to be sustainable. And therefore, adding value is what we need to do. And for adding value, whatever it takes for us to do, we need to do those things. So I think, you know, the jury is out and will remain out for a while because the race is on and we don’t know who’s going to win the race. But this much I do know that, you know, it will not be one individual unit or one individual kind of unit. There will be very many players in this whole thing. But at the end of it all, as long as it improves our standards of living, as long as it gives us results and answers which we never had before, it is for the good of humanity.

And I hope that will definitely happen.

Amitabh Kant

Thank you. Thank you for that very detailed answer to that question. I now turn to Mr. Christy Varshan, the CEO of, TCS. Yes, Mr. Krishnivasan, the industry consensus is that AI will shift work from writing code to orchestrating AI systems. What does TCS look like to you in 2030? What will be the headcount and what will be the revenue per employee? And how is TCS communicating that transition to its workforce and to the country?

K. Krithivasan

Thank you and good afternoon to everyone. See, this is the topic that everyone has been discussing in the last few days and few weeks. And like Arun has explained, the market also has been contemplating. But there will be a few things that will change. Many things may not change or the other way. Like if you look at the role of what most of us do as a system integrator, the role of system integrators come into play because there are complex, complex systems and many of them have a lot of legacy. you it’s not that one day you can have a llm understand everything and auto generate code and the software engineers will go away but not to say like arunthi said there will be more and more productivity that will be brought in and at the same time you need system integrators who can test validate verify what is being generated and so that’s one part of it the second is as you also look at today the role will shift towards more and more requirements engineering context engineering how do you know whether you are building the right system how do you validate a system is doing the right thing does it have cyber security does it do some harm all those things are to be validated you like you may not know all the roles that you will have five years down the line but we don’t envisage a situation where there will be a significant shrinkage of hope Now, the other areas that we have somehow not looked at when we get excited about generating code is for many of the, for instance, cloud came into play about maybe 10 years ago.

But if you ask most organizations, they will tell you that 30 -40 % cloud has been adopted. There is so much to be done. So, this is going to be a long tail. And even within that, you would see many organizations, they have to prepare for deploying or adopting. It’s not a trivial job. They have to get their data estate right. They have to get their applications rationalized, modernized. So, there is a certain amount of work to be done. They need to train their models like Arunthati was saying or somebody was mentioning. You will have some large models, many small models in every enterprise. They have to be trained. And the last part of it, which you are not…

Again, looking at is what is it new that you can do with all these LLMs? There will be many interesting things. Somebody has to build. Somebody has to think through that. So, if you look at another 30, I don’t envisage a time that there is a significant shrinkage of hope. but there will be more volume of work that will be produced. More volume of work. More volume of work that will be produced and more interesting work that will be done.

Amitabh Kant

Thank you. Thank you. So that’s an interesting perspective. I turn to Salil, who is the CEO of Infosys. Salil, one of the very provocative statements made by one of the Bay Area leaders and investor actually was that, which attracted a lot of news coverage, was that services model is dead within five years. Your chairman, Nandan Nilikani, just I was reading, in fact, I went through his interaction, and he said that this is not an opportunity gap, but this is an execution gap and that the real money isn’t clear. He’s cleaning up trillions of dollars of legacy tech debt. who is right? Is Nandan right or is it this Bay Area leader? Who’s right according to you?

Salil Parekh

That’s an easy answer Nandan’s of course right. There’s no question. So simply because he’s your chairman because I think Nandan I’m sure everyone has a view. Nandan is a visionary who has a view on this business for years. I think the way we see it is and we shared this a few days ago there are several areas of opportunity that come from AI services and there are six that we have highlighted recently just a few days ago and those in aggregate we have shared some data are about 300 billion dollars of opportunity over the next several years and then we’ve gone into a little bit of of the detail in each. I’ll give just a couple of examples.

There’s one which is like AI engineering, which is the building of agents, orchestrating, integrating some of the points what Kriti was mentioning. There’s another which you alluded to how Nandan has said it of legacy modernization, which is basically saying there were some things which were 15, 20 years old with large companies. How can we bring it to the more current? And there because of AI agents, the cost is lower, the time is less. And so there’s an easier economic rationale for companies to do it. So as we put all these together, we see these what we call AI services, which will give us the growth. And what I think the point you made, what Nandan said, if we can pivot our company to serve these big six areas for our clients, that execution path.

then the opportunity is good. And again, some data on that, like this year, which will end in March, we have recruited 20 ,000 college graduates. Next year, we are on track and we’ve announced we are recruiting 20 ,000 college graduates. This year, our headcount has increased in the first three quarters by 13 ,000 people. And my sense is that will continue. So what it’s opening up really is new set of opportunities. And there is some productivity benefit that comes with if like specifically in Infosys, but I’m sure in general, if we execute and serve our clients, there will be more opportunity.

Amitabh Kant

So tell me, do you at any stage aspire to own intellectual property in the AI stack or will you remain a builder for hire?

Salil Parekh

So then the approach, I’ll speak a little bit for Infosys, our approach is, we have a lot of opportunities. We have tremendous IP. So like in AI, we built this IP layer called Topaz Fabric. which has the ability for clients to work with any of the foundation models, plus the agents that we have built, that Infosys has built, plus any third party agents. So that’s the layer that we are, let’s say, pretty good at and that we will build and continue to build the IP on. That’s the approach on the IP that we’ve taken.

Amitabh Kant

OK. CVK, let me turn to you because HCL Tech has a software product business. It has a design custom AI chips in Bangalore and you operate across the full stack, actually. Is HCL Tech positioning as an AI builder at any stage? And if so, how far up the stack are you willing to go into models and to compute, into infrastructure? Infrastructure that at any stage will compete with the hyperscalers rather than just partner with them.

C. Vijayakumar

Thank you and good evening everyone. HCL Tech, as you kind of gave some pointers, we are uniquely placed because, first of all, we have a software product business which delivers 10 % of our revenue. And we also have a very deep engineering heritage where we service top 50 of the 100 R &D spenders doing a lot of work, including some cutting edge work. Like we have built a two nanometer custom silicon for one of the technology companies. So we have these unique capabilities. And this also reflected in our high. Highest revenue per employee amongst the IT services companies. So with this backdrop, our AI strategy is one of them is. heavily indexed on building because of course, our core services, we will continue to modernize and evolve our services to be relevant for the future.

And even if it means it takes away some revenue streams, we are proactively doing it. But I think the biggest focus is there are these large language models and the foundational models. They cannot be applied most efficiently for enterprise use cases. There is still a gap between what a foundation model can deliver and what is the ultimate efficiency and innovation that’s possible. So we are really trying to bridge the gap and building IPs that will bridge the gap which helps enterprises to scale AI adoption. And we’re also focused on a lot of specialized services like even Salil mentioned, like physical AI, AI factory. agentic AI. All of these new solutions we are very focused on.

And of course, the partnership ecosystem becomes extremely critical. So we are partnering with almost all the large solution providers. I don’t think we are building anything to become a hyperscaler. I think we missed the bus many, many years ago. And we’re not building models, but we are building solutions which will make the models much more scalable and applicable within enterprises.

Amitabh Kant

Thanks. Thanks. Thanks for that. I just wanted to turn to Mr. Christy Wilson because he’s the biggest employer in India. He employs over 600 ,000 engineers. You know, India produces millions of engineering graduates a year and many of them are trained for exactly the kind of work AI is now going to automate in a very big way. So what should be a skilling strategy of this country? I mean, how do we do reskilling and skilling at an individual level or should we do it at a national country level? I mean, what is the view of India’s leading CEO in terms of skilling and reskilling?

K. Krithivasan

This is, to my mind, a major national challenge. It’s a challenge and an opportunity. Because, in fact, three days ago, here we ran a workshop with about 1 ,500 kids from all the schools in the NCR region. Most of them are, in fact, all of them have non -technical background and many of them could not even speak or not fluent in English. And we taught them how to use their native language, how to do coding. And, in fact, within a span of about three hours, almost 1 ,500. Apps were built. So that’s the power of AI. You can… You can worry about how AI is going to take away the jobs at the entry level. I think AI also enables all these people to develop and imagine new areas where software can make the lives of people better.

And it creates more and more opportunity. You can be afraid and not do anything. I think we should be forward leaning and train as many people as possible. In fact, all three of us are working with the Ministry of IT in creating the curriculum for the students coming up in all these universities.

Amitabh Kant

Wonderful. Wonderful. Thanks. Thanks for that very positive and constructive perspective. Arundhati, just let me turn to you again and ask you if AI is to drive India’s productivity. It cannot remain just a Fortune 500 story. How do we make it? AI tools accessible. to millions of MSME? How do we raise productivity? How do we build for the market? Even if unit economics looks slightly different from enterprise contracts, how do we scale it up in a big way for MSME to make the difference to Indian economy?

Arundhati Bhattacharya

So thank you for that question, because I personally believe that unless we can democratize any technology, it doesn’t really serve the purpose of that country or for that people. And AI is something that is not meant for the white -collar worker alone. In fact, it’s one of the things that can actually empower the blue -collar, just as you were talking, Mr. Kritivasan, it can actually empower the blue -collar workers. Now, if you look at the blue -collar workers, in fact, in NEETI, we did one report taking into account the blue -collar workers like carpenters, plumbers, hospitality workers, Anganwadi workers. So a lot of these, you know, personal… personas we had taken. And what we realized is that they have multiple challenges.

One challenge, of course, is the way that they have been skilled. So they have a skilling challenge. But more than skilling challenge, they have an access challenge in the sense that they may be very well skilled, but they don’t know that the job exists within the village or in the next village. So they have an access challenge. The second thing that they have is the challenge of ensuring that they are getting paid on time. Even that is not available. So they have several challenges of this nature, that of skilling, that of access, that of payments, that of ensuring that they are parts of communities which actually enable them to be supported during times of distress or during times of need.

Many of these are actually challenges that can be solved if we can use AI in the proper way, in a proper marketplace to get the right kind of opportunities to them, the right kind of certifications to them, the right kind of assessment of their skills to them. If all of this can be done, actually speaking, we will be doing the country an enormous favor. And you will find that the quality of life of not only these people, but the quality of life that, you know, that they serve, of the people they serve, people, you know, who are actually taking their services, even that is going to become much, much better. So I think, you know, AI is not something that is meant only for the white collar workers or for people in tier one, tier two cities.

It’s meant for the SMEs. It’s meant for the MSMEs. It is what is going to empower them to get into a league which they were not able to access earlier. It is also meant for the blue collar workers because it can empower all of them.

Amitabh Kant

Salil, we’ve had a India has done something unique in digital public infrastructure It’s been transformational in terms of identity payment, credit You know, the Bank of International Settlement said that India achieved in seven years what it would have taken 50 years to achieve. How do we create a DPI, a digital public infrastructure for artificial intelligence? How do we take computing power to the common citizen? How do we scale? How do we make a difference using the power of AI?

Salil Parekh

Absolutely. I think there’s already work going on. Specifically, there are three big areas where there’s thinking going on. There’s actual projects on the ground in agriculture, in healthcare, in making sure that everything that is being done for education is helping within the country for the citizens. Also in that area, There are examples where we have shown some of it. And now the way that, as you mentioned, the India stack or the digital public infrastructure was created, where essentially it was fully available without exorbitant costs or any cost, is what the approach is being driven today. There are various components of the architecture which are being discussed. And working closely with the ministry, with the government, those will be rolled out.

Today, of course, you have seen that there is tremendous support at the chip layer, at the data center layer, at the infra layer. And now there will be more at the architecture, how it can be distributed. And at least these three big areas, agriculture, education, healthcare, are being looked at today.

Amitabh Kant

Thanks. Thanks. CVK. One last question before I open it up to the floor. You know, the hyperscalers, Microsoft, Google, Amazon, they’re spending close to about $600 billion this year alone on AI infrastructure. Spending almost about close to 50 to 55 % on CapEx. And if AI services opportunity is really in the range of about $350 to $400 billion, can Indian IT companies, can all of you together, capture it without adequate R &D? Does it not require greater level of R &D intensity? And what will it take all of you to put in more resources into R &D for the future?

C. Vijayakumar

Yes. First of all, this big CapEx. Spend also triggers a lot of services spent, like building all these data centers, AI factories. The entire IT infrastructure landscape in the world would get refreshed over the next five to eight years. That itself is a huge services opportunity. Then there is this physical AI. It’s a completely new spend. Today, there is very, very little physical AI deployed in the world. It’s believed that one of the studies by Zeno says it’s a trillion -dollar opportunity. That would mean at least $200 billion of services opportunity. I think we are looking at some very big services opportunities. Even to really encash on these big services opportunities, the companies like us will need to invest in building solutions because it’s not straightforward services.

You need to build solutions, which will mean we will have to put in more money into R &D. Right? Right. Building solutions. building labs and kind of POCs, a lot of pre -work needs to be done. And also there is a lot of opportunities to create solutions which will really make the foundational models much more scalable for enterprise. So I personally believe we should increase the R &D spend. And I do think the industry model will support us because as more and more AI is infused, more outcome -based contracts would come, which also helps us to deliver a higher profitability, which means we can very comfortably invest more in R &D. But the timing of that, we might need to invest a little ahead of the curve before the real benefits come.

Amitabh Kant

Okay, wonderful. Wonderful to hear this perspective. I’m going to open it up to this house. We’ll ask about, I’ll open it up for five questions. Please. Name yourself. just be very direct and blunt. Please don’t ask long winding questions. To the point, very matter of fact all young people, any ladies here will be given preference. The lady there. Yeah, the lady here.

Audience

Hello everyone. Thank you so much for giving me opportunity. No introduction, just name and shoe. Mania Sharma, CEO of Mono AI. Saving my two years to reach out to you from here till here. First question, how can I come and meet you as a young entrepreneur, 27 years with no network? My first question. Saving my lot of marketing money. Second question is, as a young Indian, 27 year old coming from a small town but being in Bangalore for seven years, what is your view or idea that we can be work with you guys and your support and mentorship so that young India can go somewhere else and we can also show the Silicon Valley that 25 year, 27 year standing here in a suit talking to you can do something.

Amitabh Kant

Okay. You know, I’ve allowed two questions because she’s a lady but no two questions. I will ask the five questions and then I’ll open it up for responses. Second, there’s a lady there. Go ahead.

Audience

My name is Devika Rao. I’m from UK University of Leeds. Trying to do this stage for AI creative education and art health and well -being concept and how the case study can be presented in six month time and I would like to co -create and collaborate with you.

Amitabh Kant

Okay. Anyone at the back? Yeah, go ahead. No, no, please there. Yeah, yeah, the blue shirt. Get up and ask.

Audience

My name is Navneet Kaul. and I have a three -part question.

Amitabh Kant

No, just ask one question. Don’t ask three in one. No, no, don’t ask three in one. Ask one question.

Audience

One question.

Amitabh Kant

Yeah.

Navneet Kaul

How will AI create jobs? What kind of jobs and what kind of skills do we need?

Amitabh Kant

You’re asking the question which I’ve already asked.

Audience

I want the panelists to answer very specifically and directly.

Amitabh Kant

All right. Anyone at the back, that side? Yeah, that gentleman there.

Audience

Hello. Kishla here. And my question is, as any employee who is currently in the IT sector, what skills he should plan to develop in the next five years to boost his employability?

Amitabh Kant

Okay. No, no, not front row. Back. I want to go right at the back. Anybody right at the back? Some back venture. huh who’s who’s the backest pitch yeah that gentleman in the black suit yeah go ahead ask yeah yeah shoot yeah please sir

Audience

sir my name is harswar than my question is what can be done to stop misuse of ai for example some people use grok you to unrest people

Amitabh Kant

okay so one last question to that yeah shoot in one line Just shoot.

Audience

Yeah, namaste. Mamanama Venkatana Rasimahati. Intentionally, I’m speaking in Sanskrit. One line. Yeah, I will come, sir. I’m a software architect. I’m a founder of a startup called Startup Sanatana. Though we are talking about so much of AI and AI is the limelight, unless and otherwise we built on AI on our culture, rich culture, tradition, heritage kind of thing, probably it will be a different kind of thing.

Amitabh Kant

Thank you. Thank you. Okay. So we got the question. We got the question. Now I’ll open it up. I’ll starting from we start with Arundhati’s response and then move on. You can you can respond to one of the questions and then move on.

Arundhati Bhattacharya

In respect of the question that this lady. gave and also others who are startups in this particular organization. Salesforce has a very vibrant startup community. The lady who leads it for us, her name is Rupa Arvindakshan. Her coordinates are available on our website. Please get in touch with her. She will get you in touch with our entire community. The community is supported to develop on Salesforce and also, you know, to ensure that you can take your products to market. So all of that is done. So please, you are more than welcome to communicate with us and get whatever support we can definitely give you.

C. Vijayakumar

Maybe I’ll take up the question on what skills are needed in the future. I think there is a big misconception. That software, coding, programming skills are not going to be relevant. I think the fundamental conceptual skills. in software development, programming is very, very essential if you really want to build a long -term career in the software industry. It could be in services or in product companies or AI. All of that requires sound programming skills. The second aspect is, I think, just the critical thinking, analytical skills. How do you really orchestrate work? A lot of standard work that many of you do or initially the younger engineers do, some of that or a lot of it can be done with the AI tools that are there today.

But how can you now think of yourself as an orchestrator and deliver maybe four or five times the output that you would deliver without these tools, right? I think just orchestrating the work with multiple coding agents, I think that’s really a skill. Or while you may take three, four years to manage a small team, but on day one, you have an opportunity to manage several agents to deliver an outcome which is 5x of what you would normally do. That’s just an example. And that’s how you need to think of every role. You can amplify the value that you can create using AI skills.

Amitabh Kant

Mr. Kristinasan.

K. Krithivasan

That was his question once again about will AI take away jobs or will AI? My view is it has been discussed for the last few days. Eventually, AI will create more jobs than destroy jobs. And you would find, but it may not all of them need not be programming jobs. That would be jobs of different categories, different classifications. But on the whole, we find it’s going to create more jobs and create more employment.

Amitabh Kant

Sal.

Salil Parekh

Thanks. I think there was a question really focused on can we build AI with views of our culture or views? Of responsibility. And that’s absolutely essential. we’ve also put together and I know many others have a framework on responsible AI and there were a couple of questions which sort of were on that line. It’s absolutely critical so in fact the way the agents are built, the way the foundation models data, the way they learn all of that needs this approach of responsible AI and that’s the approach that we’ve recommended. Many others are working on it and as an overall industry we should focus on that and that will give us as good an outcome as we can get and even that after that outcome we will have to refine and modify.

Responsible AI is critical in that. Thank you.

Amitabh Kant

So ladies and gentlemen we’ve heard the captains of the industry we are in the midst of disruption as I said but these leaders bring great optimism, they bring hope and according to them actually The wave of AI will end up creating many more jobs for India, but there’ll be jobs of a different kind. And we need to skill ourselves for the new emerging jobs of tomorrow. But with these leaders, I’m absolutely confident that India will ride this wave to greater progress and prosperity as it becomes a Vixit Bharat by 2047. It’ll create many, many more jobs. And these leaders will drive India to a 30 plus trillion dollar economy with jobs in the coming years. Thank you very much, ladies.

Related ResourcesKnowledge base sources related to the discussion topics (39)
Factual NotesClaims verified against the Diplo knowledge base (3)
Confirmedhigh

“The AI Impact Summit 2026 closed with a panel of senior leaders from India’s IT services sector, moderated by Amitabh Kant, who introduced Salil Parekh (CEO, Infosys), K. Krithivasan (CEO, TCS), C. Vijayakumar (CEO, HCL Tech) and Arundhati Bhattacharya (Senior VP, Salesforce).”

The panel composition and moderator are confirmed by the transcript of the closing panel where Amitabh Kant introduced Mr. Salil Pareek, Mr. K. Kritivasan, Mr. C. Vijay Kumar and Ms. Arundhati Bhattacharya as the four panelists [S4].

Additional Contextmedium

“Arundhati Bhattacharya warned that SaaS success depends on more than low‑code generation – it requires auditability, adoption, deep workflow understanding, governance and genuine customer‑value delivery.”

The knowledge base includes remarks emphasizing auditability, adoption and the many pieces needed for an AI solution to work in an organization, echoing Bhattacharya’s warning and adding nuance about practical utility [S22] and about sustainable value creation depending on user adoption [S19].

!
Correctionhigh

“Amitabh Kant’s opening question referenced a ≈ 40 % fall in Salesforce’s market value over the past 12 months.”

Salesforce’s recent market performance described in the knowledge base shows the company’s shares soaring to a record high of $368.7, indicating a strong rise rather than a 40 % decline, contradicting the claim of a steep fall [S110].

External Sources (112)
S1
WS #280 the DNS Trust Horizon Safeguarding Digital Identity — – **Audience** – Individual from Senegal named Yuv (role/title not specified)
S2
Building the Workforce_ AI for Viksit Bharat 2047 — -Audience- Role/Title: Professor Charu from Indian Institute of Public Administration (one identified audience member), …
S3
Nri Collaborative Session Navigating Global Cyber Threats Via Local Practices — – **Audience** – Dr. Nazar (specific role/title not clearly mentioned)
S4
Building Trusted AI at Scale Cities Startups &amp; Digital Sovereignty – Panel Discussion Moderator Amitabh Kant NITI — <strong>Moderator:</strong> With a big round of applause, kindly welcome the panelists of this last panel of AI Impact S…
S5
Building Trusted AI at Scale Cities Startups & Digital Sovereignty – Panel Discussion Moderator Amitabh Kant NITI — My name is Devika Rao. I’m from UK University of Leeds. Trying to do this stage for AI creative education and art health…
S6
Building Trusted AI at Scale Cities Startups &amp; Digital Sovereignty – Panel Discussion Moderator Amitabh Kant NITI — <strong>Moderator:</strong> With a big round of applause, kindly welcome the panelists of this last panel of AI Impact S…
S7
Building Trusted AI at Scale Cities Startups & Digital Sovereignty – Panel Discussion Moderator Amitabh Kant NITI — 1327 words | 131 words per minute | Duration: 607 secondss All right. Anyone at the back, that side? Yeah, that gentlem…
S8
Seismic Shift — 1. International Monetary Fund, ‘India’s Economy to Rebound as Pandemic Prompts Reforms’, November 11, 2021, https://www…
S10
Infosys CEO settles insider trading charges — According toIndia’s markets regulator, Infosys CEO Salil Parekhhas settledcharges related to insufficient internal contr…
S11
Building Trusted AI at Scale Cities Startups &amp; Digital Sovereignty – Panel Discussion Moderator Amitabh Kant NITI — -K. Krithivasan: CEO of TCS (Tata Consultancy Services), leads company with over 600,000 engineers
S12
Building Trusted AI at Scale Cities Startups & Digital Sovereignty – Panel Discussion Moderator Amitabh Kant NITI — Speakers:Arundhati Bhattacharya, K. Krithivasan Speakers:Arundhati Bhattacharya, K. Krithivasan, Salil Parekh Speakers…
S13
Keynote-Olivier Blum — -Moderator: Role/Title: Conference Moderator; Area of Expertise: Not mentioned -Mr. Schneider: Role/Title: Not mentione…
S14
Day 0 Event #250 Building Trust and Combatting Fraud in the Internet Ecosystem — – **Frode Sørensen** – Role/Title: Online moderator, colleague of Johannes Vallesverd, Area of Expertise: Online session…
S15
Conversation: 02 — -Moderator: Role/Title: Event moderator; Area of expertise: Not specified
S16
Building Trusted AI at Scale Cities Startups &amp; Digital Sovereignty – Panel Discussion Moderator Amitabh Kant NITI — <strong>Moderator:</strong> With a big round of applause, kindly welcome the panelists of this last panel of AI Impact S…
S17
Building the Next Wave of AI_ Responsible Frameworks & Standards — This panel discussion at the Global AI Summit focused on reimagining responsible AI and balancing rapid innovation with …
S19
Multistakeholder Partnerships for Thriving AI Ecosystems — Dr. Bärbel Koffler emphasized that governments must create frameworks and governance structures to ensure AI benefits ar…
S20
Multistakeholder Partnerships for Thriving AI Ecosystems — Bhattacharya advises against being driven by market capitalizations when creating companies, emphasizing that sustainabl…
S21
Building Inclusive Societies with AI — Evidence:Example of a skilled plumber in a village who might be unaware of good opportunities in neighboring villages E…
S22
https://app.faicon.ai/ai-impact-summit-2026/building-trusted-ai-at-scale-cities-startups-digital-sovereignty-panel-discussion-moderator-amitabh-kant-niti — But if you ask most organizations, they will tell you that 30 -40 % cloud has been adopted. There is so much to be done….
S23
https://dig.watch/event/india-ai-impact-summit-2026/ai-for-social-empowerment_-driving-change-and-inclusion — I just have a follow -up on that, and then I’ll move to Julie. I’ll put it very, I mean, let’s say, a very, very simple,…
S24
Collaborative AI Network – Strengthening Skills Research and Innovation — about those. So obviously, it’s not just creating applications. It’s the same old story of digital transformation, right…
S25
Inclusive AI Starts with People Not Just Algorithms — Hi, my name is I’m founder of an AI company. We work with global higher education institutions. So I actually led my lif…
S26
Pre 8: IGF Youth Track: AI empowering education through dialogue to implementation – Follow-up to the AI Action Summit declaration from youth — Anja Gengo: Yes, I am. Thank you. I hope you can hear me. First of all, thank you so much for such an interesting and ri…
S27
How AI Is Transforming Indias Workforce for Global Competitivene — While acknowledging a transition period, Srikrishna believes AI will ultimately generate more employment opportunities t…
S28
From India to the Global South_ Advancing Social Impact with AI — This comment directly addresses one of the most anxiety-provoking aspects of AI adoption – job displacement. By framing …
S29
Generative AI is enhancing employment opportunities and shaping job quality, says ILO report — A new study conducted by the International Labour Organization (ILO) investigates the consequences of Generative AI on t…
S30
Need and Impact of Full Stack Sovereign AI by CoRover BharatGPT — Sabharwal contends that the traditional hourly pricing model ($20-40 per hour) in Indian IT services will become obsolet…
S31
Need and Impact of Full Stack Sovereign AI by CoRover BharatGPT — “AI will come, jobs will go, mass exodus will happen in corporates”[16]. “What do you mean by the business model will ha…
S32
How AI Is Transforming Indias Workforce for Global Competitivene — -Srikrishna Ramakarthikeyan- (Role/title not clearly specified, but appears to be from IT services sector based on discu…
S33
Driving Indias AI Future Growth Innovation and Impact — Evidence:Generative AI was built without following strict rules initially. Cloud security and data protection are still …
S34
Building Trusted AI at Scale Cities Startups & Digital Sovereignty – Panel Discussion Moderator Sidharth Madaan — Madaan reinforces the concept that employment disruption happens at the task level rather than complete job elimination….
S35
IT clients taking cautious approach to costly AI technology, says Infosys executive — IT clientsare keen to adoptAI technology, but the high cost is causing them to take a cautious approach, according to Sa…
S36
European Tech Sovereignty: Feasibility, Challenges, and Strategic Pathways Forward — Economic | Infrastructure European Competitive Advantages and Success Stories Klein argues that Europe shouldn’t try t…
S37
Scaling Trusted AI_ How France and India Are Building Industrial & Innovation Bridges — Thank you. Thank you for inviting me here. So it’s a very valid question. And I will not answer it in a very technical w…
S38
Panel Discussion AI in Digital Public Infrastructure (DPI) India AI Impact Summit — So future of AI I think will depend on the market. We’ll also depend on the people. We’ll also depend on the trust in us…
S39
The Foundation of AI Democratizing Compute Data Infrastructure — This connects AI democratization to broader digital infrastructure development, suggesting that individual data empowerm…
S40
Panel Discussion AI in Digital Public Infrastructure (DPI) India AI Impact Summit — Saibal argues that India is approaching AI with the same ethos as DPI – treating it as shared public infrastructure that…
S41
Collaborative AI Network – Strengthening Skills Research and Innovation — Artificial intelligence | Information and communication technologies for development Garg frames AI itself as a possibl…
S42
WS #288 An AI Policy Research Roadmap for Evidence-Based AI Policy — ## Industry Perspectives: Systems Integration Challenges Eltjo Poort: thank you Isadora yeah and thanks for giving me t…
S43
Creating digital public infrastructure that empowers people | IGF 2023 Open Forum #168 — Mark Irura:To add on to what’s been shared already, the supply and the demand side were mentioned. And on the supply sid…
S44
Secure Finance Risk-Based AI Policy for the Banking Sector — The moderator emphasizes that AI governance should not be viewed through a completely different lens but should be integ…
S45
Ministerial Roundtable — There’s a stark contrast between countries that have achieved near-universal connectivity (like Azerbaijan) and those st…
S46
All hands on deck to connect the next billions | IGF 2023 WS #198 — Expanding internet connectivity is a complex task that requires innovative approaches, responsive to the needs of local …
S47
Fixing Healthcare, Digitally — Traditional models may not fully address the complex gaps and needs in healthcare infrastructure. Moreover, it is crucia…
S48
A digital public infrastructure strategy for sustainable development – Exploring effective possibilities for regional cooperation (University of Western Australia) — In conclusion, DPI is a critical building block for the digital economy and plays a significant role in achieving the SD…
S49
Building Trusted AI at Scale Cities Startups & Digital Sovereignty – Panel Discussion Moderator Amitabh Kant NITI — Thank you and good evening everyone. HCL Tech, as you kind of gave some pointers, we are uniquely placed because, first …
S50
Building Trusted AI at Scale Cities Startups &amp; Digital Sovereignty – Panel Discussion Moderator Amitabh Kant NITI — When addressing competition with hyperscalers’ massive infrastructure investments, the Indian IT companies positioned th…
S51
Comprehensive Report: “Converging with Technology to Win” Panel Discussion — Economic | Legal and regulatory | Human rights Five hyperscaler firms competing to reach AGI first; financial structure…
S52
Inclusive AI For A Better World, Through Cross-Cultural And Multi-Generational Dialogue — Larissa Zutter:So this is not quite as concrete as you might want, but I think I want to piggyback off of what was said …
S53
AI Governance Dialogue: Steering the future of AI — – **Civil Society**: Advocacy ensuring frameworks reflect diverse societal needs Doreen Bogdan Martin: Thank you. And w…
S54
Global Digital Governance &amp; Multistakeholder Cooperation for WSIS+20 — The session concluded with time constraints as “the president of Estonia is about to make his remarks,” reflecting the b…
S55
Opening address of the co-chairs of the AI Governance Dialogue — While this transcript captures only the opening remarks of the AI Governance Dialogue, the key comments identified estab…
S56
How AI Is Transforming Indias Workforce for Global Competitivene — While acknowledging a transition period, Srikrishna believes AI will ultimately generate more employment opportunities t…
S57
Comprehensive Discussion Report: The Future of Artificial General Intelligence — Near-term job displacement will likely be offset by new job creation, with current impact mainly on junior-level positio…
S58
Comprehensive Report: Preventing Jobless Growth in the Age of AI — Historical evidence shows that technological advances eliminate some jobs while creating others, with the net effect bei…
S59
Artificial intelligence — The disruptions that AI systems could bring to the labour market are another source of concern. Many studies estimate th…
S60
How Trust and Safety Drive Innovation and Sustainable Growth — Explanation:Despite representing different perspectives (UK regulator, Singapore regulator, and industry), there was une…
S61
Ray Dalio warns of global breakdown behind market turmoil — Billionaire investorRay Daliohas warned that the recent market turbulence is part of a larger global crisis. The turmoil…
S62
AI investment shows strong momentum beyond bubble fears — AI investmentis not showingsigns of a speculative bubble, according to theAlibaba Groupchairman. Instead, he argued at t…
S63
How AI Drives Innovation and Economic Growth — Arguments:First, model evaluation. So AI companies typically do that part. How good is the model output for specific tas…
S64
The open-source gambit: How America plans to outpace AI rivals by democratising tech — A “worker-first AI agenda” is the key social pillar of the Plan. The focus is on helping workers reskill and build capac…
S65
AI: The Great Equaliser? — While the introduction of AI technology may result in job losses in certain sectors, it also creates new job opportuniti…
S66
Shaping the Future AI Strategies for Jobs and Economic Development — Continuous learning and upskilling will be essential for workforce adaptation to rapid technological change across all s…
S67
Generative AI: Steam Engine of the Fourth Industrial Revolution? — Additionally, reskilling the workforce is crucial to fully embrace new technologies. AI, for instance, has the potential…
S68
AI will not replace people – but people who use AI will replace people who do not | IBM’s Report — According toIBM’s report, executives estimate that around 40% of their workforce will need to reskill due to implementin…
S69
Open Forum #64 Local AI Policy Pathways for Sustainable Digital Economies — Rather than following historical patterns of automation that replace workers, AI development should prioritize applicati…
S70
SAP elevates customer support with proactive AI systems — AIhas pushedcustomer support into a new era, where anticipation replaces reaction. SAP has built a proactive model that …
S71
Multistakeholder Partnerships for Thriving AI Ecosystems — An audience member raised concerns about whether AI democratisation would genuinely benefit small enterprises or primari…
S72
Need and Impact of Full Stack Sovereign AI by CoRover BharatGPT — Sabharwal contends that the traditional hourly pricing model ($20-40 per hour) in Indian IT services will become obsolet…
S73
Building the Next Wave of AI_ Responsible Frameworks &amp; Standards — Arundhati Bhattacharya from Salesforce highlighted how her company established an office for humane and ethical use of t…
S74
Need and Impact of Full Stack Sovereign AI by CoRover BharatGPT — “AI will come, jobs will go, mass exodus will happen in corporates”[16]. “What do you mean by the business model will ha…
S75
Building Trusted AI at Scale Cities Startups &amp; Digital Sovereignty – Panel Discussion Moderator Amitabh Kant NITI — The industry leaders unanimously rejected predictions that AI would eliminate the services model. Krithivasan noted that…
S76
Inclusive AI Starts with People Not Just Algorithms — 200 years later, we are like, okay, let’s clean it up. Even in the Internet revolution, you know, we have the problems w…
S77
AI Transformation in Practice_ Insights from India’s Consulting Leaders — Krishan positioned this challenge within a broader context, noting that the key lies in focusing on value creation and p…
S78
How AI Is Transforming Indias Workforce for Global Competitivene — -Srikrishna Ramakarthikeyan- (Role/title not clearly specified, but appears to be from IT services sector based on discu…
S79
From Innovation to Impact_ Bringing AI to the Public — Discussion point:Evolution of banking services while maintaining core functions Discussion point:Evolution of work rath…
S80
Building Trusted AI at Scale Cities Startups &amp; Digital Sovereignty – Keynote Ananya Birla Birla AI Labs — Our first major research vertical is in structured foundation. A field that a recent Forbes article estimates at a $600 …
S81
AI for equality: Bridging the innovation gap — Blair presented evidence from surveys conducted with the World Bank and Intuit of 3,000 women entrepreneurs, showing tha…
S82
IT clients taking cautious approach to costly AI technology, says Infosys executive — IT clientsare keen to adoptAI technology, but the high cost is causing them to take a cautious approach, according to Sa…
S83
India’s AI Future Sovereign Infrastructure and Innovation at Scale — Discussion point:Global talent acquisition for Indian IP development Discussion point:Strategic pivot from services to …
S84
Building Trusted AI at Scale Cities Startups & Digital Sovereignty – Panel Discussion Moderator Amitabh Kant NITI — Thank you and good evening everyone. HCL Tech, as you kind of gave some pointers, we are uniquely placed because, first …
S85
Scaling Trusted AI_ How France and India Are Building Industrial & Innovation Bridges — Thank you. Thank you for inviting me here. So it’s a very valid question. And I will not answer it in a very technical w…
S86
Empowering People with Digital Public Infrastructure — Pervinder Johar: Absolutely. So I think our focus is on what we call the physical infrastructure of the world. So whe…
S87
Panel Discussion AI in Digital Public Infrastructure (DPI) India AI Impact Summit — So future of AI I think will depend on the market. We’ll also depend on the people. We’ll also depend on the trust in us…
S88
Collaborative AI Network – Strengthening Skills Research and Innovation — Artificial intelligence | Information and communication technologies for development Garg frames AI itself as a possibl…
S89
Panel Discussion AI in Digital Public Infrastructure (DPI) India AI Impact Summit — Saibal argues that India is approaching AI with the same ethos as DPI – treating it as shared public infrastructure that…
S90
The Foundation of AI Democratizing Compute Data Infrastructure — “It needs to be interoperable and shareable.”[37]. “So I think two characteristics of digital public infrastructure, whi…
S91
Building Inclusive Societies with AI — Impact:This comment fundamentally reframed the discussion from focusing on solutions to focusing on execution mechanisms…
S92
Building Inclusive Societies with AI — These key comments fundamentally shaped the discussion by challenging assumptions, introducing new frameworks, and groun…
S93
Open Forum #66 the Ecosystem for Digital Cooperation in Development — The discussion maintained a consistently collaborative and solution-oriented tone throughout. It began with formal intro…
S94
From Technical Safety to Societal Impact Rethinking AI Governanc — The discussion began with a formal, academic tone but became increasingly critical and urgent throughout. Speakers expre…
S95
Science as a Growth Engine: Navigating the Funding and Translation Challenge — The discussion maintained a consistently thoughtful and collaborative tone throughout. While panelists acknowledged seri…
S96
GermanAsian AI Partnerships Driving Talent Innovation the Future — The discussion maintained a consistently optimistic and collaborative tone throughout. Speakers demonstrated mutual resp…
S97
Debating Education / DAVOS 2025 — The tone was thoughtful and analytical, with panelists offering differing perspectives in a respectful manner. There was…
S98
Comprehensive Summary: The Future of Robotics and Physical AI — The tone was optimistic yet realistic throughout. The panelists demonstrated enthusiasm about recent breakthroughs and n…
S99
Driving Enterprise Impact Through Scalable AI Adoption — The tone was thoughtful and exploratory rather than alarmist, with participants acknowledging both the transformative po…
S100
Revamping Decision-Making in Digital Governance and the WSIS Framework — The discussion maintained a constructive and collaborative tone throughout, with speakers building upon each other’s poi…
S101
Powering the Technology Revolution / Davos 2025 — The tone was generally optimistic and forward-looking, with panelists highlighting opportunities for innovation and prog…
S102
WS #302 Upgrading Digital Governance at the Local Level — The discussion maintained a consistently professional and collaborative tone throughout. It began with formal introducti…
S103
Shaping the Future AI Strategies for Jobs and Economic Development — The discussion maintained an optimistic yet pragmatic tone throughout. While acknowledging significant challenges around…
S104
Towards a Resilient Information Ecosystem: Balancing Platform Governance and Technology — The discussion maintained a professional, collaborative tone throughout, characterized by constructive problem-solving r…
S105
Democratizing AI: Open foundations and shared resources for global impact — The tone was consistently collaborative, optimistic, and forward-looking throughout the discussion. Speakers maintained …
S106
Building Population-Scale Digital Public Infrastructure for AI — The tone is optimistic and collaborative throughout, with speakers sharing concrete examples of successful implementatio…
S107
Building the AI-Ready Future From Infrastructure to Skills — The tone was consistently optimistic and collaborative throughout, with speakers expressing excitement about AI’s potent…
S108
AI Innovation in India — The tone was consistently celebratory, inspirational, and optimistic throughout the discussion. Speakers expressed pride…
S109
The Future of Innovation and Entrepreneurship in the AI Era: A World Economic Forum Panel Discussion — The discussion began with a technology-focused, optimistic tone about AI’s transformative potential but gradually shifte…
S110
Salesforce’s AI tools drive growth — Salesforce sharessoaredto a record high of $368.7 on Wednesday, climbing 11% after surpassing quarterly sales estimates …
S111
Can a layered policy approach stop Internet fragmentation? | IGF 2023 WS #273 — Audience:We will fight to see who goes first. Colin Perkins, University of Glasgow. I guess I want to follow up a little…
S112
AI Infrastructure and Future Development: A Panel Discussion — Economic | Infrastructure Lessin raises concerns from the financial industry about whether the complex financing arrang…
Speakers Analysis
Detailed breakdown of each speaker’s arguments and positions
A
Arundhati Bhattacharya
5 arguments159 words per minute1123 words421 seconds
Argument 1
SaaS resilience through workflow, governance, and value‑add
EXPLANATION
Arundhati argues that the SaaS model’s strength lies beyond simple code generation; it requires deep understanding of customer workflows, governance, auditability, and adoption to deliver real value.
EVIDENCE
She explains that SaaS is not only about vibe coding or creating an application, but also about understanding workflows, addressing customer pain points, ensuring observability, governance, auditability, and adoption, emphasizing that these multiple pieces are essential for success [16-23].
EXTERNAL EVIDENCE (KNOWLEDGE BASE)
Bhattacharya emphasizes that SaaS success requires deep workflow understanding, governance, auditability, and adoption, as highlighted in the panel transcript <a href="https://dig.watch/event/india-ai-impact-summit-2026/building-trusted-ai-at-scale-cities-startups-digital-sovereignty-panel-discussion-moderator-amitabh-kant-niti/" target="_blank" class="diplo-source-cite" title="Building Trusted AI at Scale Cities Startups & Digital Sovereignty – Panel Discussion Moderator Amitabh Kant NITI" data-source-title="Building Trusted AI at Scale Cities Startups & Digital Sovereignty – Panel Discussion Moderator Amitabh Kant NITI" data-source-snippet="Moderator: With a big round of applause, kindly welcome the panelists of this last panel of AI Impact Summit 2026. Mr. Salil Pareek, Mr. K. Kritivasan, Mr. C. Vijay Kumar and Ms. Arun”>[S4] and reinforced in the discussion summary [S5].
MAJOR DISCUSSION POINT
SaaS success depends on holistic operational capabilities.
Argument 2
Market hype often overstates AI disruption; investors must read fine print
EXPLANATION
She cautions that market narratives frequently exaggerate AI’s impact, with inflated valuations and circular money, and advises investors to scrutinize details before drawing conclusions.
EVIDENCE
Arundhati notes that markets say many things, not all true, that people pump up values due to large money flows, some of which is circular, and urges investors to read the fine print and exercise discretion [14-28].
EXTERNAL EVIDENCE (KNOWLEDGE BASE)
She warns that market valuations can be inflated by circular money flows and advises investors to scrutinize details, matching her comments on avoiding market-cap-driven decisions [S20] and observations of market manipulation [S5].
MAJOR DISCUSSION POINT
Skepticism toward AI market hype.
DISAGREED WITH
Implicit market narrative (as referenced by Amitabh Kant)
Argument 3
AI must be accessible to improve livelihoods of blue‑collar and MSME sectors
EXPLANATION
She stresses that AI should not be limited to white‑collar workers; it can empower blue‑collar workers and MSMEs by addressing their specific challenges.
EVIDENCE
Arundhati states that AI is not just for white-collar workers, it can empower blue-collar workers, and cites a report covering carpenters, plumbers, hospitality and Anganwadi workers, highlighting the need for democratization [158-166].
EXTERNAL EVIDENCE (KNOWLEDGE BASE)
Her point about AI empowering blue-collar workers and MSMEs is echoed by the panel’s identification of challenges for carpenters, plumbers, and other workers and the role of AI marketplaces [S5], as well as a concrete plumber example [S21].
MAJOR DISCUSSION POINT
Democratizing AI for broader workforce.
AGREED WITH
Salil Parekh, Audience (Kishla)
Argument 4
Address skilling, job‑access, and payment challenges through AI‑enabled marketplaces
EXPLANATION
She outlines how AI‑driven marketplaces can solve blue‑collar workers’ problems of skill validation, job discovery, timely payments, and community support.
EVIDENCE
She describes challenges such as skilling, access to jobs, timely payments, and community support, and argues that AI-enabled marketplaces can provide certifications, skill assessments, and job matching to improve quality of life for workers and their customers [167-172].
EXTERNAL EVIDENCE (KNOWLEDGE BASE)
The discussion details how AI-driven marketplaces can provide certifications, skill assessments, and timely payments for blue-collar workers, supporting her claim [S5] and the plumber case study [S21].
MAJOR DISCUSSION POINT
AI as a solution for blue‑collar ecosystem challenges.
AGREED WITH
K. Krithivasan, Vijayakumar C., Amitabh Kant
Argument 5
Salesforce ecosystem offers mentorship and community contacts for startups
EXPLANATION
Arundhati points to Salesforce’s vibrant startup community and provides a direct contact for entrepreneurs seeking support.
EVIDENCE
She mentions that Salesforce has a vibrant startup community led by Rupa Arvindakshan, whose coordinates are on the website, and encourages startups to get in touch for development and market access support [274-280].
EXTERNAL EVIDENCE (KNOWLEDGE BASE)
The panel notes Salesforce’s vibrant startup community and provides contact details for mentorship through Rupa Arvindakshan <a href="https://dig.watch/event/india-ai-impact-summit-2026/building-trusted-ai-at-scale-cities-startups-digital-sovereignty-panel-discussion-moderator-amitabh-kant-niti/" target="_blank" class="diplo-source-cite" title="Building Trusted AI at Scale Cities Startups & Digital Sovereignty – Panel Discussion Moderator Amitabh Kant NITI" data-source-title="Building Trusted AI at Scale Cities Startups & Digital Sovereignty – Panel Discussion Moderator Amitabh Kant NITI" data-source-snippet="Moderator: With a big round of applause, kindly welcome the panelists of this last panel of AI Impact Summit 2026. Mr. Salil Pareek, Mr. K. Kritivasan, Mr. C. Vijay Kumar and Ms. Arun”>[S4].
MAJOR DISCUSSION POINT
Startup mentorship via corporate ecosystem.
K
K. Krithivasan
5 arguments178 words per minute773 words259 seconds
Argument 1
System integrators remain essential; role moves to requirements and context engineering
EXPLANATION
Krithivasan asserts that despite AI code generation, system integrators will still be needed to validate, test, and ensure security, shifting their focus toward requirements and context engineering.
EVIDENCE
He explains that system integrators are needed because of complex legacy systems, to test, validate, and verify AI-generated code, and that the role will shift toward requirements engineering, context engineering, and security validation [51-53].
EXTERNAL EVIDENCE (KNOWLEDGE BASE)
Krithivasan stresses that system integrators are still needed for legacy complexity, a view confirmed by the panel’s consensus that the services model will evolve rather than disappear <a href="https://dig.watch/event/india-ai-impact-summit-2026/building-trusted-ai-at-scale-cities-startups-digital-sovereignty-panel-discussion-moderator-amitabh-kant-niti/" target="_blank" class="diplo-source-cite" title="Building Trusted AI at Scale Cities Startups & Digital Sovereignty – Panel Discussion Moderator Amitabh Kant NITI" data-source-title="Building Trusted AI at Scale Cities Startups & Digital Sovereignty – Panel Discussion Moderator Amitabh Kant NITI" data-source-snippet="Moderator: With a big round of applause, kindly welcome the panelists of this last panel of AI Impact Summit 2026. Mr. Salil Pareek, Mr. K. Kritivasan, Mr. C. Vijay Kumar and Ms. Arun”>[S4].
MAJOR DISCUSSION POINT
Evolving role of system integrators.
AGREED WITH
Salil Parekh, Vijayakumar C.
DISAGREED WITH
C. Vijayakumar
Argument 2
No major headcount shrink; volume and complexity of work will increase
EXPLANATION
He predicts that AI will not cause a significant reduction in workforce size; instead, the amount and sophistication of work will grow.
EVIDENCE
Krithivasan states that he does not envisage a significant shrinkage of headcount, but rather a larger volume of work and more interesting work being produced [68-70].
EXTERNAL EVIDENCE (KNOWLEDGE BASE)
He predicts stable headcount with increased work volume, consistent with broader observations that AI will generate more jobs than it eliminates [S27].
MAJOR DISCUSSION POINT
Workforce size remains stable, workload expands.
AGREED WITH
Salil Parekh, Vijayakumar C.
Argument 3
AI can rapidly up‑skill large numbers; partnership with Ministry of IT to create curricula
EXPLANATION
He highlights a national initiative, collaborating with the Ministry of IT, to develop curricula that can quickly up‑skill large populations for AI‑driven jobs.
EVIDENCE
Krithivasan describes a recent workshop with 1,500 non-technical schoolchildren, teaching coding in native languages, and notes that all three panelists are working with the Ministry of IT to create curricula for university students [135-147].
EXTERNAL EVIDENCE (KNOWLEDGE BASE)
He describes a workshop with the Ministry of IT that taught coding to 1,500 non-technical students, illustrating rapid up-skilling efforts [S5].
MAJOR DISCUSSION POINT
National AI up‑skilling collaboration.
AGREED WITH
Arundhati Bhattacharya, Vijayakumar C., Amitabh Kant
Argument 4
Hands‑on workshops show AI’s potential to empower non‑technical youth
EXPLANATION
He provides evidence that short, practical workshops can enable thousands of non‑technical participants to build apps, demonstrating AI’s empowering potential.
EVIDENCE
He recounts that in a three-hour session, 1,500 participants built apps, showcasing AI’s ability to quickly up-skill and empower people without prior technical background [136-142].
EXTERNAL EVIDENCE (KNOWLEDGE BASE)
The same workshop demonstrated that participants could build apps in three hours, showcasing AI’s empowerment potential [S5].
MAJOR DISCUSSION POINT
Practical AI training for youth.
AGREED WITH
Arundhati Bhattacharya, Vijayakumar C., Amitabh Kant
Argument 5
AI will create more jobs than it destroys; new roles will differ from traditional programming
EXPLANATION
Krithivasan argues that AI will be a net job creator, though the nature of those jobs will shift away from conventional programming tasks.
EVIDENCE
He states that AI will create more jobs than it destroys, and that many of the new jobs will not be programming-centric, reflecting a change in job classifications [300-304].
EXTERNAL EVIDENCE (KNOWLEDGE BASE)
He argues AI will be a net job creator, supported by reports that generative AI improves employment prospects and drives economic growth [S27], [S28], [S29].
MAJOR DISCUSSION POINT
AI as a net job creator.
AGREED WITH
Arundhati Bhattacharya, Vijayakumar C., Amitabh Kant
S
Salil Parekh
6 arguments149 words per minute789 words316 seconds
Argument 1
Services model is alive; AI creates $300 bn opportunity via AI engineering, legacy modernization, etc.
EXPLANATION
Salil contends that the services model remains viable, with AI opening roughly $300 billion of opportunities across six identified areas such as AI engineering and legacy modernization.
EVIDENCE
He cites that Infosys sees about $300 bn of AI services opportunity over the next years, highlighting AI engineering and legacy modernization as examples where AI agents lower cost and time, creating economic rationale for companies [82-89].
EXTERNAL EVIDENCE (KNOWLEDGE BASE)
Parekh cites Infosys’s estimate of a $300 bn AI services opportunity across AI engineering, legacy modernization, and other areas <a href="https://dig.watch/event/india-ai-impact-summit-2026/building-trusted-ai-at-scale-cities-startups-digital-sovereignty-panel-discussion-moderator-amitabh-kant-niti/" target="_blank" class="diplo-source-cite" title="Building Trusted AI at Scale Cities Startups & Digital Sovereignty – Panel Discussion Moderator Amitabh Kant NITI" data-source-title="Building Trusted AI at Scale Cities Startups & Digital Sovereignty – Panel Discussion Moderator Amitabh Kant NITI" data-source-snippet="Moderator: With a big round of applause, kindly welcome the panelists of this last panel of AI Impact Summit 2026. Mr. Salil Pareek, Mr. K. Kritivasan, Mr. C. Vijay Kumar and Ms. Arun”>[S4].
MAJOR DISCUSSION POINT
AI‑driven growth for services model.
AGREED WITH
K. Krithivasan, Vijayakumar C.
Argument 2
Aggressive hiring and execution on AI services will drive growth
EXPLANATION
He points to Infosys’s large recruitment drives and headcount growth as evidence of its commitment to capture AI services opportunities.
EVIDENCE
Salil mentions recruiting 20,000 college graduates this year, a similar target for next year, and a 13,000 increase in headcount in the first three quarters, indicating continued expansion [91-95].
EXTERNAL EVIDENCE (KNOWLEDGE BASE)
He points to Infosys’s recruitment of 20,000 graduates and a 13,000 headcount increase as evidence of aggressive hiring to capture AI services demand <a href="https://dig.watch/event/india-ai-impact-summit-2026/building-trusted-ai-at-scale-cities-startups-digital-sovereignty-panel-discussion-moderator-amitabh-kant-niti/" target="_blank" class="diplo-source-cite" title="Building Trusted AI at Scale Cities Startups & Digital Sovereignty – Panel Discussion Moderator Amitabh Kant NITI" data-source-title="Building Trusted AI at Scale Cities Startups & Digital Sovereignty – Panel Discussion Moderator Amitabh Kant NITI" data-source-snippet="Moderator: With a big round of applause, kindly welcome the panelists of this last panel of AI Impact Summit 2026. Mr. Salil Pareek, Mr. K. Kritivasan, Mr. C. Vijay Kumar and Ms. Arun”>[S4].
MAJOR DISCUSSION POINT
Talent acquisition to fuel AI services.
Argument 3
Infosys’ Topaz Fabric IP layer enables use of any foundation model and custom agents
EXPLANATION
He describes Infosys’s proprietary Topaz Fabric, which allows clients to work with any foundation model and integrate custom or third‑party agents, representing a strategic IP asset.
EVIDENCE
He explains that Topaz Fabric is an IP layer that lets clients use any foundation model, combines Infosys-built agents and third-party agents, and that Infosys will continue to build on this IP [100-102].
MAJOR DISCUSSION POINT
Proprietary AI integration platform.
Argument 4
Deploy AI‑driven projects in agriculture, health, education using DPI principles
EXPLANATION
Salil outlines ongoing AI projects in key sectors, leveraging India’s digital public infrastructure (DPI) model to make AI services widely accessible.
EVIDENCE
He notes three big areas-agriculture, healthcare, education-where AI projects are being deployed, following the DPI approach of low-cost, widely available services, with components being rolled out in partnership with ministries [184-192].
EXTERNAL EVIDENCE (KNOWLEDGE BASE)
He outlines AI deployments in agriculture, health, and education following India’s Digital Public Infrastructure (DPI) model, as described in the panel discussion <a href="https://dig.watch/event/india-ai-impact-summit-2026/building-trusted-ai-at-scale-cities-startups-digital-sovereignty-panel-discussion-moderator-amitabh-kant-niti/" target="_blank" class="diplo-source-cite" title="Building Trusted AI at Scale Cities Startups & Digital Sovereignty – Panel Discussion Moderator Amitabh Kant NITI" data-source-title="Building Trusted AI at Scale Cities Startups & Digital Sovereignty – Panel Discussion Moderator Amitabh Kant NITI" data-source-snippet="Moderator: With a big round of applause, kindly welcome the panelists of this last panel of AI Impact Summit 2026. Mr. Salil Pareek, Mr. K. Kritivasan, Mr. C. Vijay Kumar and Ms. Arun”>[S4].
MAJOR DISCUSSION POINT
Sectoral AI deployment via DPI.
Argument 5
Leverage chip, data‑center, and architectural layers to make AI power common‑citizen ready
EXPLANATION
He emphasizes the need to build AI infrastructure across hardware, data‑center, and architectural layers to democratize AI access for citizens.
EVIDENCE
Salil references support at the chip layer, data-center layer, and infrastructure layer, and mentions ongoing work on architecture to distribute AI capabilities to the common citizen [190-192].
EXTERNAL EVIDENCE (KNOWLEDGE BASE)
He mentions building AI capability across chip, data-center, and architectural layers to democratize AI for citizens, noted in the discussion <a href="https://dig.watch/event/india-ai-impact-summit-2026/building-trusted-ai-at-scale-cities-startups-digital-sovereignty-panel-discussion-moderator-amitabh-kant-niti/" target="_blank" class="diplo-source-cite" title="Building Trusted AI at Scale Cities Startups & Digital Sovereignty – Panel Discussion Moderator Amitabh Kant NITI" data-source-title="Building Trusted AI at Scale Cities Startups & Digital Sovereignty – Panel Discussion Moderator Amitabh Kant NITI" data-source-snippet="Moderator: With a big round of applause, kindly welcome the panelists of this last panel of AI Impact Summit 2026. Mr. Salil Pareek, Mr. K. Kritivasan, Mr. C. Vijay Kumar and Ms. Arun”>[S4].
MAJOR DISCUSSION POINT
Infrastructure stack for AI democratization.
Argument 6
AI development must follow responsible‑AI frameworks and reflect cultural values
EXPLANATION
He asserts that responsible AI principles and cultural considerations are essential when building agents and training models.
EVIDENCE
Salil states that responsible AI is critical, that agents and foundation models must be built with responsible AI approaches, and that the industry should adopt such frameworks to ensure good outcomes [306-311].
EXTERNAL EVIDENCE (KNOWLEDGE BASE)
He stresses adherence to responsible-AI frameworks and cultural considerations, aligning with the responsible AI assessment tool (RAISE Index) and calls for culturally aware AI governance [S17], [S19].
MAJOR DISCUSSION POINT
Ethical and cultural responsibility in AI.
AGREED WITH
Arundhati Bhattacharya, Audience (Kishla)
C
C. Vijayakumar
5 arguments136 words per minute831 words365 seconds
Argument 1
Focus on product business and bridging foundation models to enterprise, not becoming a hyperscaler
EXPLANATION
Vijayakumar explains that HCL will concentrate on its product business and on creating solutions that make foundation models usable for enterprises, rather than competing with hyperscalers.
EVIDENCE
He says HCL is uniquely placed with a software product business, builds custom silicon, and will focus on bridging foundation models to enterprise use cases, explicitly stating they are not becoming a hyperscaler and will not build models themselves [118-126].
MAJOR DISCUSSION POINT
Strategic positioning away from hyperscaling.
AGREED WITH
K. Krithivasan, Salil Parekh, Vijayakumar C.
DISAGREED WITH
K. Krithivasan
Argument 2
Leverage custom silicon and high revenue‑per‑employee to build scalable AI solutions
EXPLANATION
He highlights HCL’s capabilities, such as custom two‑nanometer silicon and the highest revenue per employee among Indian IT services, as foundations for scalable AI offerings.
EVIDENCE
Vijayakumar notes HCL’s 10 % revenue from product business, deep engineering heritage, a two-nanometer custom silicon project, and the highest revenue per employee among IT services firms [110-115].
MAJOR DISCUSSION POINT
Competitive advantage through hardware and efficiency.
Argument 3
Significant R&D investment needed to build solutions, labs, and physical‑AI offerings
EXPLANATION
He argues that capturing the AI services market will require substantial R&D spending to develop solutions, labs, and emerging physical‑AI products.
EVIDENCE
He describes how large CapEx spend will generate services demand, cites a trillion-dollar physical AI opportunity, and stresses the need for building solutions, labs, and pre-work, concluding that R&D spend must increase [200-215].
MAJOR DISCUSSION POINT
R&D as a prerequisite for AI services capture.
Argument 4
Outcome‑based contracts will fund higher R&D spend ahead of market benefits
EXPLANATION
He suggests that as AI‑infused services grow, outcome‑based contracts will generate higher profitability, enabling firms to invest more in R&D before full market returns materialize.
EVIDENCE
Vijayakumar notes that outcome-based contracts will help deliver higher profitability, which in turn will allow comfortable investment in R&D, though timing may require early spending [214-215].
MAJOR DISCUSSION POINT
Financial model supporting early R&D.
Argument 5
Core programming plus orchestration, critical thinking, and AI‑tool mastery are essential
EXPLANATION
He emphasizes that while programming remains fundamental, future success will hinge on critical thinking, orchestration of AI agents, and the ability to amplify output using AI tools.
EVIDENCE
He states that programming is essential for long-term software careers, highlights critical thinking and analytical skills, and describes orchestrating multiple coding agents to achieve 5× output as a key future skill [284-297].
MAJOR DISCUSSION POINT
Skill set evolution for AI‑augmented software work.
AGREED WITH
K. Krithivasan, Arundhati Bhattacharya, Vijayakumar C., Amitabh Kant
A
Amitabh Kant
2 arguments131 words per minute1327 words607 seconds
Argument 1
India is at a pivotal AI disruption point that demands proactive policy and industry engagement.
EXPLANATION
Kant observes that the country is currently experiencing a major disruption driven by AI, implying that coordinated action from both the public and private sectors is essential to harness the opportunity.
EVIDENCE
He explicitly states, “We are actually meeting at a point of disruption,” signalling the need for strategic response [8].
EXTERNAL EVIDENCE (KNOWLEDGE BASE)
Kant’s statement about a disruption point is echoed by the panel’s emphasis on coordinated policy and multi-stakeholder frameworks for AI development <a href="https://dig.watch/event/india-ai-impact-summit-2026/building-trusted-ai-at-scale-cities-startups-digital-sovereignty-panel-discussion-moderator-amitabh-kant-niti/" target="_blank" class="diplo-source-cite" title="Building Trusted AI at Scale Cities Startups & Digital Sovereignty – Panel Discussion Moderator Amitabh Kant NITI" data-source-title="Building Trusted AI at Scale Cities Startups & Digital Sovereignty – Panel Discussion Moderator Amitabh Kant NITI" data-source-snippet="Moderator: With a big round of applause, kindly welcome the panelists of this last panel of AI Impact Summit 2026. Mr. Salil Pareek, Mr. K. Kritivasan, Mr. C. Vijay Kumar and Ms. Arun”>[S4], [S19].
MAJOR DISCUSSION POINT
AI-driven disruption as a catalyst for strategic action.
Argument 2
AI will be a primary engine of employment and economic growth, propelling India toward a $30+ trillion economy and a “Vixit Bharat” by 2047.
EXPLANATION
Kant concludes that the wave of AI will generate far more jobs than it eliminates, driving unprecedented economic expansion and positioning India as a leading global economy by mid‑century.
EVIDENCE
He summarises the panel’s view that AI will create many jobs, boost productivity, and help India achieve a $30 + trillion economy and the vision of a “Vixit Bharat” by 2047 [312-317].
EXTERNAL EVIDENCE (KNOWLEDGE BASE)
His projection aligns with analyses that AI will boost employment and drive massive economic growth, supporting a $30+ trillion outlook [S27], [S28], [S29].
MAJOR DISCUSSION POINT
AI as a catalyst for massive job creation and macro‑economic growth.
A
Audience
2 arguments162 words per minute340 words125 seconds
Argument 1
Young entrepreneurs need structured mentorship and ecosystem support to turn AI ideas into viable ventures.
EXPLANATION
An audience member highlights the difficulty of accessing networks and guidance, arguing that a formal mentorship channel would enable emerging innovators to scale their AI projects.
EVIDENCE
Mania Sharma, a 27-year-old entrepreneur, asks for direct contact and support, noting she has “no network” and seeks mentorship to engage with the panelists and the broader AI ecosystem [225-229].
EXTERNAL EVIDENCE (KNOWLEDGE BASE)
The audience’s request for mentorship matches the panel’s acknowledgment of the need for structured startup support and the existence of Salesforce’s mentorship network <a href="https://dig.watch/event/india-ai-impact-summit-2026/building-trusted-ai-at-scale-cities-startups-digital-sovereignty-panel-discussion-moderator-amitabh-kant-niti/" target="_blank" class="diplo-source-cite" title="Building Trusted AI at Scale Cities Startups & Digital Sovereignty – Panel Discussion Moderator Amitabh Kant NITI" data-source-title="Building Trusted AI at Scale Cities Startups & Digital Sovereignty – Panel Discussion Moderator Amitabh Kant NITI" data-source-snippet="Moderator: With a big round of applause, kindly welcome the panelists of this last panel of AI Impact Summit 2026. Mr. Salil Pareek, Mr. K. Kritivasan, Mr. C. Vijay Kumar and Ms. Arun”>[S4].
MAJOR DISCUSSION POINT
Mentorship and ecosystem support for early‑stage AI entrepreneurs.
Argument 2
AI solutions should be rooted in local cultural heritage and values to ensure relevance and acceptance.
EXPLANATION
A participant argues that building AI without incorporating India’s cultural traditions risks producing solutions that are disconnected from societal context, advocating for culturally‑aware AI design.
EVIDENCE
Kishla states that unless AI is built on “our culture, rich culture, tradition, heritage,” it will be a “different kind of thing,” emphasizing the need for cultural integration in AI development [262-267].
EXTERNAL EVIDENCE (KNOWLEDGE BASE)
The call for culturally rooted AI reflects the panel’s discussion on integrating cultural values into responsible AI design and broader calls for AI frameworks that respect local heritage [S19], [S17].
MAJOR DISCUSSION POINT
Cultural relevance and ethical grounding of AI systems.
Agreements
Agreement Points
AI will be a net job creator, generating more employment than it eliminates, though the nature of jobs will shift toward new AI‑augmented roles.
Speakers: Arundhati Bhattacharya, K. Krithivasan, Vijayakumar C., Amitabh Kant
AI must be accessible to improve livelihoods of blue‑collar and MSME sectors AI will create more jobs than it destroys; new roles will differ from traditional programming Core programming plus orchestration, critical thinking, and AI‑tool mastery are essential AI will create many more jobs for India, driving a $30+ trillion economy
All speakers agree that AI will expand employment opportunities in India, especially for blue-collar and MSME workers, even though many of the new roles will require orchestration, critical thinking and AI-tool proficiency rather than traditional coding [158-172][300-304][284-297][312-317].
POLICY CONTEXT (KNOWLEDGE BASE)
Multiple studies and policy discussions highlight AI as a net job creator, with India’s AI workforce strategy emphasizing more jobs than losses and the need for upskilling [S56][S57][S58][S65][S66].
The traditional IT services model and system‑integrator role remain vital; AI will augment rather than replace these functions, and headcount is not expected to shrink dramatically.
Speakers: K. Krithivasan, Salil Parekh, Vijayakumar C.
System integrators remain essential; role moves to requirements and context engineering No major headcount shrink; volume and complexity of work will increase Services model is alive; AI creates $300 bn opportunity via AI engineering, legacy modernization, etc. Focus on product business and bridging foundation models to enterprise, not becoming a hyperscaler
Krithivasan stresses that system integrators will still be needed and that headcount will not fall sharply [51-53][68-70]; Salil highlights a $300 bn AI services opportunity that keeps the services model alive [82-89]; Vijayakumar adds that HCL will concentrate on bridging foundation models to enterprises rather than trying to become a hyperscaler [118-126].
POLICY CONTEXT (KNOWLEDGE BASE)
Industry panels note that system integrators remain essential, with HCL and Infosys positioning themselves as bridges between foundation models and enterprise applications rather than pursuing hyperscaler scale [S49][S50][S42].
Upskilling, capacity building and education are essential to prepare the workforce for AI‑driven transformation.
Speakers: K. Krithivasan, Arundhati Bhattacharya, Vijayakumar C., Amitabh Kant
AI can rapidly up‑skill large numbers; partnership with Ministry of IT to create curricula Hands‑on workshops show AI’s potential to empower non‑technical youth Address skilling, job‑access, and payment challenges through AI‑enabled marketplaces Core programming plus orchestration, critical thinking, and AI‑tool mastery are essential
Krithivasan describes a workshop that taught 1,500 non-technical students to build apps and notes collaboration with the Ministry of IT on curricula [135-142]; Arundhati outlines the skilling, access and payment challenges faced by blue-collar workers and proposes AI-enabled marketplaces to solve them [163-172]; Vijayakumar reiterates that programming fundamentals remain crucial while new orchestration skills are needed [284-297]; Amitabh’s question on national skilling strategy underscores the shared focus on capacity development [132-134].
POLICY CONTEXT (KNOWLEDGE BASE)
Policy frameworks such as the US ‘worker-first AI agenda’ and various AI governance reports stress reskilling, capacity building and education as core to AI adoption [S64][S65][S66][S67][S68].
AI development should be responsible, inclusive and culturally grounded, ensuring that technology serves broader societal needs.
Speakers: Salil Parekh, Arundhati Bhattacharya, Audience (Kishla)
AI development must follow responsible‑AI frameworks and reflect cultural values AI must be accessible to improve livelihoods of blue‑collar and MSME sectors AI solutions should be rooted in local cultural heritage and values to ensure relevance and acceptance
Salil calls for responsible-AI practices and cultural alignment in building agents and models [306-311]; Arundhati stresses democratizing AI for blue-collar workers and MSMEs [157-166]; Kishla argues that AI should be built on India’s cultural heritage to be meaningful [262-267].
POLICY CONTEXT (KNOWLEDGE BASE)
Inclusive AI governance discussions call for culturally grounded, responsible AI development, reflected in multistakeholder dialogues and inclusive AI initiatives [S52][S53][S54][S55][S48].
Similar Viewpoints
Both leaders emphasize the need for broad ecosystem partnerships—Salil through collaboration with ministries and public‑sector DPI initiatives, Vijayakumar through partnerships with major solution providers—to scale AI solutions effectively [188-190][124-125].
Speakers: Salil Parekh, Vijayakumar C.
Deploy AI‑driven projects in agriculture, health, education using DPI principles Partnering with almost all the large solution providers
Unexpected Consensus
Both Infosys and HCL choose to remain solution‑builders rather than pursue hyperscaler ambitions or develop their own large foundation models.
Speakers: Salil Parekh, Vijayakumar C.
Infosys’s Topaz Fabric IP layer enables use of any foundation model and custom agents I don’t think we are building anything to become a hyperscaler… not building models
Despite being large IT services firms, both Salil and Vijayakumar state that their strategy is to create proprietary IP and integration layers (Topaz Fabric) and to focus on building solutions, explicitly rejecting the pursuit of hyperscaler status or in-house model development [99-102][125-126].
POLICY CONTEXT (KNOWLEDGE BASE)
Panel discussions with HCL leadership confirm their strategy to stay as solution-builders and avoid building large foundation models, contrasting with hyperscaler ambitions [S49][S50][S51].
Overall Assessment

The panel reached strong consensus on four core themes: (1) AI will be a net creator of jobs, especially for blue‑collar and MSME workers; (2) the traditional services and system‑integrator model will persist and even expand with AI engineering opportunities; (3) large‑scale upskilling and capacity‑building are essential to equip the workforce for new AI‑augmented roles; (4) AI must be developed responsibly, inclusively and with cultural relevance. These agreements cut across the digital economy, capacity development, AI governance and social development domains.

High consensus – the speakers largely reinforce each other’s positions, indicating a shared vision that policy, industry investment and education should focus on inclusive, responsible AI deployment rather than fearing displacement.

Differences
Different Viewpoints
Future role of system integrators versus product‑focused AI solution building
Speakers: K. Krithivasan, C. Vijayakumar
System integrators remain essential; role moves to requirements and context engineering Focus on product business and bridging foundation models to enterprise, not becoming a hyperscaler
Krithivasan argues that despite AI code generation, system integrators will still be needed to test, validate and ensure security of complex legacy environments, with a shift toward requirements and context engineering [51-53]. Vijayakumar counters that HCL will concentrate on building product solutions that make foundation models usable for enterprises, emphasizing bridging gaps rather than traditional system-integration services, and explicitly states they are not becoming a hyperscaler or building models themselves [118-126].
POLICY CONTEXT (KNOWLEDGE BASE)
Debates on the future of system integrators versus product-focused AI firms are captured in system integration challenge reports and industry panels on bridging models and applications [S42][S49][S50].
Extent of market hype and valuation of AI‑driven SaaS disruption
Speakers: Arundhati Bhattacharya, Implicit market narrative (as referenced by Amitabh Kant)
Market hype often overstates AI disruption; investors must read fine print AI agents will replace per‑seat software subsystems – market suggests traditional SaaS model is under threat
Arundhati cautions that market narratives frequently exaggerate AI impact, noting inflated valuations and circular money, and urges investors to scrutinize details [14-28]. Amitabh’s question frames the market view that AI agents could replace traditional SaaS per-seat models, implying a significant threat to the SaaS business model [11]. This reflects a disagreement between Arundhati’s skeptical view of market hype and the market-driven narrative of imminent SaaS disruption.
POLICY CONTEXT (KNOWLEDGE BASE)
Analysts differentiate between genuine AI investment momentum and hype, noting concerns about valuation of AI-driven SaaS and bubble risks [S62][S63][S61].
Unexpected Differences
Contrasting views on the survivability of the traditional services model
Speakers: Salil Parekh, Bay Area leader (referenced by Amitabh Kant)
Services model is alive; AI creates $300 bn opportunity via AI engineering, legacy modernization, etc. Services model is dead within five years (Bay Area leader’s claim)
Amitabh cites a Bay Area leader who claimed the services model would die in five years [75-78]. Salil directly refutes this by stating the services model remains viable and outlines a $300 bn AI services opportunity [82-89]. The disagreement is unexpected because it pits a high-profile external prediction against the internal confidence of an industry leader.
POLICY CONTEXT (KNOWLEDGE BASE)
The survivability of the traditional services model is contested, with some reports emphasizing continued relevance of services firms while others highlight pressure from hyperscalers [S42][S49][S50][S51].
Overall Assessment

The panel shows broad consensus that AI will be a net creator of jobs and economic growth, but there are notable disagreements on implementation pathways: the role of system integrators versus product‑centric solution building, the degree to which market hype should be trusted regarding SaaS disruption, and whether the traditional services model is still viable. These divergences reflect differing strategic priorities among Indian IT firms and between industry insiders and external market narratives.

Moderate – while the overarching goals (AI‑driven growth, job creation, democratization) are shared, the speakers differ on key strategic approaches, which could lead to fragmented policy recommendations and varied investment strategies across the sector.

Partial Agreements
All speakers concur that AI will generate net employment and drive economic expansion, but they diverge on the nature of the future jobs and the pathways to achieve this outcome: Krithivasan emphasizes new, non‑programming job categories [300-304]; Vijayakumar stresses the need for programming fundamentals combined with orchestration and critical thinking skills [284-297]; Salil highlights large‑scale hiring and AI services opportunities as the growth engine [91-95]; Amitabh frames AI as the catalyst for a $30 + trillion economy and massive job creation [312-317].
Speakers: Amitabh Kant, K. Krithivasan, C. Vijayakumar, Salil Parekh
AI will be a primary engine of employment and economic growth, propelling India toward a $30+ trillion economy and a “Vixit Bharat” by 2047 AI will create more jobs than it destroys; new roles will differ from traditional programming Core programming plus orchestration, critical thinking, and AI‑tool mastery are essential Aggressive hiring and execution on AI services will drive growth
Takeaways
Key takeaways
The SaaS model remains resilient; success depends on workflow integration, governance, observability, and delivering concrete value, not just on low‑code or AI code generation. Market hype around AI‑driven disruption is often overstated; investors should scrutinize valuations and fine‑print. The core role of system integrators will shift from manual coding to requirements engineering, context engineering, validation, and orchestration of AI agents, without a major headcount reduction. The traditional services model is alive and can unlock a $300 bn+ AI services opportunity through AI engineering, legacy modernization, and other high‑value offerings. Infosys is building proprietary AI IP (Topaz Fabric) that abstracts foundation models and custom agents, positioning itself as both a builder and a platform provider. HCL Tech will focus on bridging foundation models to enterprise use cases and building scalable AI solutions, leveraging its product business and custom silicon, but will not attempt to become a hyperscaler. National skilling and reskilling are critical; AI can rapidly up‑skill large numbers, and TCS is collaborating with the Ministry of IT to develop curricula and run hands‑on workshops. Democratizing AI for MSMEs and blue‑collar workers is essential; AI‑enabled marketplaces can address skill, access, and payment challenges for these segments. India’s Digital Public Infrastructure (DPI) model will be extended to AI, with pilot projects in agriculture, health, and education, supported by chip, data‑center, and architectural layers. Capturing the AI services market will require increased R&D investment to build solutions, labs, and physical‑AI offerings; outcome‑based contracts can fund higher R&D spend. AI is expected to create more jobs than it destroys, but new roles will emphasize programming fundamentals, AI‑tool orchestration, critical thinking, and analytical skills. Responsible AI frameworks and cultural alignment are seen as non‑negotiable for trustworthy AI deployment. Start‑ups can tap into the Salesforce ecosystem for mentorship and community support.
Resolutions and action items
Infosys will continue aggressive hiring (20,000 graduates announced, 13,000 added in FY) to staff AI services and build Topaz Fabric IP. Infosys will expand execution on the six identified AI service areas to capture the $300 bn opportunity. TCS will focus on expanding requirements/context engineering, validation, cybersecurity, and cloud rationalization services as AI adoption grows. HCL Tech will prioritize building IP that bridges foundation models to enterprise workloads and will deepen partnerships with major solution providers. TCS (and other firms) will work with the Ministry of IT to develop AI curricula for universities and run large‑scale workshops for non‑technical youth. Infosys and other industry players will adopt responsible‑AI frameworks and embed cultural considerations into model training and deployment. Salesforce will provide a point of contact (Rupa Arvindakshan) for start‑ups seeking mentorship and ecosystem support. Industry consensus to increase R&D spend to develop AI solutions, labs, and physical‑AI offerings ahead of market demand.
Unresolved issues
Exact impact of AI on Salesforce’s market valuation and whether the SaaS model is fundamentally threatened. Specific headcount and revenue‑per‑employee targets for TCS by 2030. Detailed roadmap and funding mechanisms for a nationwide AI‑focused Digital Public Infrastructure. Concrete mechanisms to prevent misuse of AI (e.g., disinformation, malicious prompting). How Indian IT firms can collectively compete with hyperscalers in AI infrastructure without becoming hyperscalers themselves. Precise curriculum content and scaling strategy for national AI skilling and reskilling programs. Metrics and timelines for measuring the success of AI democratization for MSMEs and blue‑collar workers.
Suggested compromises
Acknowledgement that AI will not eliminate the SaaS business model but will require augmentation with workflow, governance, and value‑add capabilities. Balancing the view that AI will not cause massive headcount cuts with the need to upskill existing staff for orchestration roles. Combining proprietary IP development (Infosys Topaz Fabric) with openness to third‑party foundation models, rather than pursuing a pure build‑or‑buy stance. Emphasizing both aggressive R&D investment and reliance on outcome‑based contracts to fund that investment.
Thought Provoking Comments
Markets will say a lot of things, but the SaaS model is not just about code generation; it involves understanding workflows, governance, auditability, and adoption. AI‑generated code alone cannot replace these essential components.
She reframes the hype around AI‑driven code generation by highlighting the broader ecosystem needed for SaaS success, challenging the notion that AI will make traditional SaaS obsolete.
Shifted the conversation from a market‑value panic to a more nuanced view of SaaS resilience, prompting other panelists to discuss the continuing relevance of system integrators and the need for new skill sets.
Speaker: Arundhati Bhattacharya
System integrators will still be needed because enterprises have complex legacy environments. The future will focus more on requirements engineering, context engineering, validation, cybersecurity, and testing of AI‑generated outputs.
He identifies concrete areas where human expertise remains critical, countering the fear that AI will eliminate software engineering jobs.
Introduced the theme of role transformation rather than job loss, leading Salil and others to elaborate on new service opportunities and the importance of up‑skilling.
Speaker: K. Krithivasan
We see about $300 billion of AI services opportunity over the next few years across six domains – AI engineering, legacy modernization, AI factories, etc. – and we are scaling headcount (20 k graduates this year, 13 k added in Q3) to capture it.
Provides a data‑driven, optimistic outlook that the services model is not dead but evolving into high‑value AI‑centric offerings.
Set a positive tone for the panel, framing AI as a growth engine and prompting discussion on IP creation (Topaz Fabric) and recruitment strategies.
Speaker: Salil Parekh
AI must be democratized; it should empower blue‑collar workers and MSMEs, not just white‑collar professionals. We need to solve skilling, access, payment, and community support challenges for these groups.
Broadens the AI conversation to inclusive economic development, highlighting societal impact beyond large enterprises.
Steered the dialogue toward policy and public‑infrastructure considerations, leading Salil to talk about AI‑focused digital public infrastructure.
Speaker: Arundhati Bhattacharya
In a workshop with 1,500 non‑technical kids, we taught them to code in their native language and they built 1,500 apps in three hours – showing AI’s power to enable anyone to create software.
Demonstrates a tangible example of AI lowering entry barriers, reinforcing the argument that AI can be a catalyst for mass up‑skilling.
Supported Arundhati’s point on democratization and sparked interest in national‑level curriculum development, influencing the later discussion on DPI.
Speaker: K. Krithivasan
Physical AI represents a trillion‑dollar opportunity; to capture it, Indian IT firms must increase R&D spend now, building labs and POCs before the market matures.
Highlights a less‑discussed frontier (hardware‑centric AI) and stresses proactive investment, adding depth to the conversation about future revenue streams.
Prompted the panel to acknowledge the need for higher R&D intensity, linking back to Salil’s IP strategy and the broader question of competing with hyperscalers.
Speaker: C. Vijayakumar
Programming fundamentals remain essential, but the key future skill will be orchestration – managing multiple AI agents, critical thinking, and delivering outcomes at 5× the traditional speed.
Clarifies the evolving skill set required, countering the myth that coding will become obsolete, and provides a concrete direction for workforce development.
Guided the audience Q&A toward concrete skill recommendations, influencing Krithivasan’s later comment that AI will create more jobs than it destroys.
Speaker: C. Vijayakumar
We are already building a digital public infrastructure for AI—similar to the India Stack—targeting agriculture, healthcare, and education, with support at chip, data‑center, and architecture layers.
Positions AI as a national public good, extending the discussion from corporate strategy to country‑wide implementation.
Created a turning point that linked corporate initiatives to government policy, reinforcing the narrative of inclusive, large‑scale AI deployment.
Speaker: Salil Parekh
Overall Assessment

The discussion pivoted around three core insights: (1) AI will transform—not eliminate—existing SaaS and services models, as emphasized by Arundhati and Krithivasan; (2) the workforce will evolve, requiring new orchestration and validation skills, a point reinforced by Vijayakumar and Krithivasan’s skilling examples; and (3) India can leverage AI as a public‑good infrastructure, a vision articulated by Salil. These comments collectively shifted the tone from alarmist market speculation to a constructive, forward‑looking roadmap, prompting the panel to explore concrete opportunities (new service domains, IP development, R&D investment) and inclusive strategies (MSME empowerment, national DPI). The interplay of these thought‑provoking remarks shaped a narrative of optimism, responsibility, and strategic action for India’s AI future.

Follow-up Questions
What is the actual impact of AI agents on the traditional SaaS business model and market valuations?
Understanding whether AI agents truly threaten SaaS revenues is crucial for investors, enterprises and policy makers.
Speaker: Arundhati Bhattacharya
What are the projected headcount and revenue per employee for TCS in 2030, and how will the transition to AI orchestration be communicated to the workforce?
Concrete metrics are needed for workforce planning and to manage employee expectations during the AI‑driven shift.
Speaker: Amitabh Kant, K. Krithivasan
How can Indian IT firms close the execution gap identified by Nandan Nilekani and capture the $300‑$400 billion AI services opportunity?
Bridging the execution gap determines whether the large market potential can be realized by Indian service providers.
Speaker: Amitabh Kant, Salil Parekh
What IP strategies should Indian IT services companies adopt to own parts of the AI stack rather than remain builders for hire?
Owning AI IP could create sustainable competitive advantage and new revenue streams for Indian firms.
Speaker: Amitabh Kant, Salil Parekh
Is it feasible for Indian IT firms like HCL to become hyperscalers, and what would be required in terms of investment and capabilities?
Assessing the possibility of moving up the stack informs long‑term strategic decisions and capital allocation.
Speaker: Amitabh Kant, C. Vijayakumar
What specific national‑level skilling and reskilling curricula are being developed with the Ministry of IT, and how will they be scaled across the country?
A clear curriculum and scaling plan are essential to address the massive up‑skilling challenge for millions of graduates.
Speaker: K. Krithivasan
How can AI tools be democratized for MSMEs, considering unit economics and scalability?
Making AI affordable and usable for small businesses is key to broad‑based productivity gains in the Indian economy.
Speaker: Arundhati Bhattacharya
What architecture and governance model will underpin a Digital Public Infrastructure for AI in India?
A national AI infrastructure requires a defined technical architecture, data policies and governance to be effective and inclusive.
Speaker: Salil Parekh
What level of R&D intensity (budget, talent, timelines) is required for Indian IT firms to capture the projected AI services market?
Quantifying R&D needs helps firms plan investments and ensures they are not left behind in the AI race.
Speaker: C. Vijayakumar
What types of new jobs will AI create in India, and what specific skills will be in demand?
Identifying emerging job categories and skill requirements guides education, training programs and career planning.
Speaker: Navneet Kaul, K. Krithivasan, C. Vijayakumar
What measures can be implemented to prevent misuse of AI, such as disinformation or unrest?
Developing safeguards and policy frameworks is critical to ensure AI benefits society without causing harm.
Speaker: Harswar (audience)
How can AI solutions be built that reflect Indian culture, heritage, and values?
Culturally aligned AI can improve adoption, relevance, and ethical compliance within the Indian context.
Speaker: Mamanama Venkatana Rasimahati (audience)
How can startups engage with large enterprise ecosystems like Salesforce for mentorship and market access?
Clear pathways for startup collaboration can accelerate innovation and broaden the AI ecosystem.
Speaker: Mania Sharma (audience), Arundhati Bhattacharya

Disclaimer: This is not an official session record. DiploAI generates these resources from audiovisual recordings, and they are presented as-is, including potential errors. Due to logistical challenges, such as discrepancies in audio/video or transcripts, names may be misspelled. We strive for accuracy to the best of our ability.