Building Trusted AI at Scale Cities Startups & Digital Sovereignty – Panel Discussion Moderator Sidharth Madaan

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

Session at a glance

Summary

This panel discussion examined the transformative impact of artificial intelligence on the future of work, exploring both opportunities and challenges in the current transition period. The panelists agreed that we are entering an unprecedented era of workplace disruption with no clear playbook, particularly affecting knowledge workers over the next five to ten years. Deepak Bagla emphasized the psychological preparation needed for potential job displacement, drawing parallels to how bank tellers disappeared despite once being considered the most secure banking job.

Radhicka Kapoor provided a more nuanced perspective, citing research showing that while most jobs will be exposed to AI automation, only 3-4% globally face complete displacement. She explained that jobs are bundles of tasks, with some tasks being automated while others remain human-centric, creating opportunities for productivity enhancement rather than wholesale job elimination. Sanjeev Bikhchandani reinforced this optimism by noting that despite widespread concerns, actual hiring through Naukri has not declined, and he advocated for individuals to learn multiple AI platforms quarterly to remain employable.

In healthcare, Prashant Warier illustrated how AI will likely augment rather than replace medical professionals, particularly in regions with severe shortages of specialists. The discussion highlighted that regulatory frameworks and liability concerns will maintain human oversight in critical decisions. The panelists noted that current AI conversations primarily affect only 10% of India’s workforce, with 45% still in agriculture and 95% in small enterprises. Success by 2030 was defined as achieving inclusive AI adoption, net job creation, GDP growth, and ensuring benefits reach informal sectors and small businesses.

Keypoints

Major Discussion Points:

– Uncertainty and Timeline of AI Disruption: All panelists emphasized that there’s no clear playbook for AI’s impact on work, with the next 5-10 years expected to bring significant disruption. However, they stressed the importance of psychological preparation for job transitions and the need for nuanced understanding rather than doomsday predictions.

– Task-Based Job Transformation vs. Complete Job Loss: The discussion highlighted research showing that while most jobs will be exposed to AI automation, only 3-4% globally face complete displacement. Instead, jobs consist of bundles of tasks – some will be automated while others will be enhanced, requiring workers to adapt and become more productive rather than being entirely replaced.

– Sector-Specific Impacts and Applications: Healthcare was used as a key example, showing how AI will augment rather than replace professionals due to regulatory requirements, liability concerns, and capacity shortages. The focus is on AI supporting doctors in decision-making, note-taking, and diagnosis rather than replacing them entirely.

– Skills and Education System Disruption: The traditional education model is being challenged, with questions about the value of formal degrees when AI can provide answers instantly. The emphasis is shifting toward practical AI literacy – learning to use multiple AI platforms rather than traditional credentials.

– Inclusive AI Transition for India’s Diverse Workforce: A critical point raised was that current AI discussions only address 10% of India’s workforce, missing the 45% in agriculture and 95% in small enterprises. Success requires ensuring AI benefits reach the informal sector, MSMEs, and agricultural workers, not just white-collar knowledge workers.

Overall Purpose:

The discussion aimed to examine how businesses and policymakers should navigate the AI-driven transformation of work, moving beyond fear-based narratives to develop practical strategies for managing the transition while ensuring inclusive growth across all sectors of the economy.

Overall Tone:

The tone began with acknowledgment of uncertainty and potential disruption but evolved into a more optimistic and pragmatic outlook. While panelists recognized legitimate concerns about job displacement, they consistently emphasized adaptation, upskilling, and the potential for AI to augment rather than replace human work. The conversation maintained a balanced perspective throughout, avoiding both excessive pessimism and unrealistic optimism, ultimately focusing on actionable strategies for individuals and policymakers.

Speakers

Speakers from the provided list:

– Sidharth Madaan: Moderator of the panel discussion

– Deepak Bagla: Works with Aatil Tinkering Lab, has banking background (joined banking in 1986), focuses on AI and innovation at school level

– Radhicka Kapoor: Researcher who has conducted recent research on AI’s impact on jobs and workforce

– Sanjeev Bikhchandani: Associated with Naukri (job portal), has insights into hiring trends and job market dynamics

– Prashant Warier: Works in healthcare sector, specifically in radiology AI space, operates in medical technology and AI applications in healthcare

Additional speakers:

None identified in the transcript.

Full session report

This comprehensive panel discussion examined the transformative impact of artificial intelligence on the future of work, bringing together diverse perspectives from policy, business, healthcare, and research to address one of the most pressing questions of our time: how should businesses and policymakers navigate the AI-driven transformation of employment?

The Unprecedented Nature of AI Disruption

The discussion opened with a sobering acknowledgement of uncertainty. Deepak Bagla provided a powerful historical analogy that set the tone for the entire conversation. He recalled how bank tellers were once considered the most secure job in banking, yet became the first to disappear when digitisation arrived. This example underscored a critical reality: we are entering an era with no playbook, where traditional assumptions about job security may prove entirely wrong.

All panellists agreed that the next five to ten years will bring unprecedented disruption, particularly affecting knowledge workers and white-collar jobs. However, they diverged significantly on the scale and timeline of this disruption. Bagla emphasised the psychological preparation needed for widespread job displacement, suggesting that the ability to cope with unemployment may become one of the most important skills for the coming decade. His perspective reflected a more cautious, preparation-focused approach to the transition.

Educational System Disruption and Workforce Entry Points

One of the most thought-provoking aspects of the discussion emerged from Bagla’s observation about educational disruption. He recounted a conversation with an Ivy League professor whose students were questioning the value of expensive tuition fees when AI could provide answers instantly. This challenge to traditional education models has profound implications for how we think about human capital development.

Bagla suggested that age barriers to workforce entry might disappear, with potentially 13-year-olds ready for task-oriented work. He specifically mentioned the “Aatil Tinkering Lab” and his team member Dipali working on AI and tinkering at the school level, illustrating how young people are already engaging with these technologies.

Sanjeev Bikhchandani provided important counterbalance, noting that business fundamentally involves managing people, building relationships, and leading teams—capabilities that require maturity and experience. While technical skills might be acquired at young ages, the human elements of work remain age-dependent.

Evidence-Based Analysis vs. Doomsday Predictions

Radhicka Kapoor provided a crucial counterbalance to doomsday predictions by introducing concrete research data from international organisations including the International Labour Organisation (ILO) and International Monetary Fund (IMF). Her analysis, drawing from ILO studies conducted late last year, revealed that whilst most jobs will indeed be exposed to AI automation, the reality is far more nuanced than wholesale displacement scenarios suggest.

The research she cited showed that only 3-4% of jobs globally face complete displacement as a global average, with this figure being lower in low and middle-income countries and higher (close to 6%) in high-income countries. More significantly, approximately 20% of jobs will experience partial automation of tasks, creating opportunities for productivity enhancement rather than elimination. This distinction between job exposure and job displacement proved fundamental to reframing the discussion.

Kapoor’s framework of jobs as “bundles of tasks” became central to the conversation. She explained that whilst some tasks within occupations will be automated, others will remain human-centric, freeing up time for workers to engage in new activities and enhance their productivity. This productivity gain, she argued, would create a virtuous cycle of increased wages, reduced prices, boosted demand, and ultimately more job creation and investment.

Current Market Reality and Individual Adaptation Strategies

Bikhchandani brought a practical business perspective to the discussion, noting that despite widespread concerns about AI’s impact, actual hiring through Naukri.com has not declined. This observation provided important grounding in current market realities, even as he acknowledged uncertainty about future developments.

Bikhchandani’s most memorable contribution was his concrete advice for individual adaptation: learn to use three AI platforms every quarter, accumulating knowledge of twelve platforms within a year. He supported this recommendation with a personal anecdote from 1989, when his PC literacy made him indispensable in a marketing department where there were only “two PCs in the marketing department” and senior colleagues lacked these skills. His phrase “if you don’t do AI, AI will be done to you” encapsulated the imperative for proactive engagement with new technology.

However, Bikhchandani also provided important nuance regarding the persistence of traditional credentials. Despite rhetoric about skills-based hiring, he argued that institutional credentials continue to serve as valuable filters, representing not just knowledge but also demonstrated commitment, work ethic, and intellectual capability.

Sector-Specific Transformations: Healthcare as a Case Study

Prashant Warier’s analysis of healthcare provided concrete insights into how AI will transform specific sectors. He identified three key factors that will shape AI’s impact in healthcare: capacity constraints, regulatory requirements, and liability structures.

In healthcare, particularly in the Global South, severe shortages of specialists mean that AI will address unmet demand rather than displace workers. Warier cited stark statistics: India has one radiologist for every hundred thousand people compared to one radiologist for 10,000 people in the United States, whilst Kenya has the same number of radiologists as a single hospital in Gurgaon. These capacity gaps mean AI will augment healthcare workers’ capabilities, enabling them to serve more patients and make better decisions.

The regulatory environment will also slow AI adoption in healthcare. All AI applications require clearance from bodies like the FDA in the United States or CDSCO in India, creating barriers to rapid deployment. Most importantly, the liability structure ensures that doctors retain ultimate responsibility for clinical decisions, positioning AI as a supportive tool rather than a replacement.

Warier outlined specific applications where AI is already proving valuable, particularly in primary care where three components can be automated: understanding patient symptoms, recommending appropriate tests, and supporting diagnosis and treatment planning. He also highlighted automated note-taking during patient consultations and integration of data from electronic medical records and imaging systems. These applications enhance productivity without replacing the fundamental doctor-patient relationship.

The Informal Sector Challenge: India’s Missing 90%

Perhaps the most significant intervention in the discussion came from Kapoor’s observation that the entire conversation was addressing only 10% of India’s workforce. With 45% of workers still in agriculture, 55% self-employed, and 95% working in enterprises with fewer than ten employees, the focus on white-collar knowledge work missed the vast majority of India’s labour force.

This insight fundamentally challenged the scope of the discussion and highlighted a critical blind spot in AI and future of work conversations. For the informal sector, the risk is not excessive automation but rather missing out entirely on AI’s productivity benefits. Agricultural workers, micro-enterprises, and informal sector employees need different types of support: financial assistance for AI adoption, digital infrastructure including broadband access, and policies that help them participate in the AI-driven economy.

Kapoor emphasised that success in AI transition requires thinking beyond skilling and reskilling to encompass industrial policy, macroeconomic policy, trade policies, and social protection systems. She specifically mentioned India’s Code on Social Security, which extends social protection to platform workers, as an example of policy innovation. The challenge is ensuring that AI benefits reach all segments of the workforce, not just those in formal sector knowledge work.

Policy Frameworks and Labour Market Evolution

The discussion highlighted significant gaps between current policy frameworks and the evolving nature of work. Traditional labour laws, designed for standard employer-employee relationships, struggle to address the proliferation of non-standard employment arrangements, including platform work and gig economy jobs.

Kapoor noted ongoing efforts at the ILO to develop new conventions and recommendations for bringing decent work to the platform economy, with India playing a leading role through initiatives like the Code on Social Security. However, much work remains to be done in updating regulatory frameworks to match the reality of modern employment relationships.

The panellists agreed that successful AI transition requires collaboration between different stakeholders. As Bagla emphasised: “when everyone works together the government the society the people the academia i think that joining the dots is absolutely core” to achieving positive outcomes.

Vision for Success in 2030

The panel concluded with rapid-fire visions of success by 2030, revealing both shared aspirations and different priorities. Bagla emphasised India’s potential as the biggest beneficiary of AI’s “delta multiplier effect” through collaborative approaches.

Warier focused on macroeconomic outcomes, envisioning “the world’s GDP growing at 10 or more by 2030” through AI adoption.

Bikhchandani defined success simply as net job creation—more jobs created than lost through the AI transition. This metric-focused approach reflected his business background and emphasis on measurable outcomes.

Kapoor provided the most comprehensive vision: an inclusive AI transition featuring better, more productive jobs where agricultural and MSME sectors benefit from the transformation without leaving informal sector workers behind. Her vision encompassed both productivity gains and equity concerns.

Conclusion and Implications

This discussion revealed the complexity and nuance required to understand AI’s impact on work. Moving beyond simplistic narratives of either technological utopia or employment displacement, the panellists constructed a framework for thinking about AI as primarily augmenting rather than replacing human capabilities.

Key insights emerged around the importance of evidence-based analysis over speculation, the need for sector-specific approaches rather than one-size-fits-all solutions, and the critical importance of inclusive policies that address all segments of the workforce. The conversation highlighted that developing countries, particularly India, might actually benefit more from AI than developed nations due to capacity gaps, unmet demand, and opportunities for leapfrogging traditional development stages.

The discussion demonstrated that whilst we may lack a playbook for the AI transition, we can develop frameworks for thinking about adaptation, augmentation, and inclusive development. The emphasis on collaboration, continuous learning, and evidence-based policy-making provides a foundation for managing this unprecedented transformation while ensuring that the benefits of AI reach all segments of society, not just the formal sector knowledge workers who typically dominate these conversations.

Session transcript

Sidharth Madaan

Thank you. We’re at a very defining moment in the history of work. On one end, we’re seeing new possibilities, new productivity unlocks, new jobs being created. And on the other, there’s a lot of growing anxiety around what would it mean and the kind of disruption it will bring to work, especially the knowledge work, the white collar jobs, as they say. Let me start with Mr. Bagla. How should businesses and policymakers think about this transition?

Deepak Bagla

It’s very interesting. First, I don’t think any of us have any answers. We will try. The fundamental point, you know, and I remember when I joined banking and I take you back to 1986, we went for training and the first thing we were told that the only job which will never change. And is stable and safe in the banking world is that of the teller. You have to go get your. The first job to go when digitization happened was the teller. Because you started taking it out of the machine. Now the challenge which remains for all of us is that we are entering into an era where there’s no playbook. What is it which it is going to move into?

So we’ve got to put it into time spans if I look at it. What is going to happen in the next 5 years, 10 years and then after that no one knows. I think next 5 years is going to be one of the toughest times of disruption. How many of you have ever been laid off? Excellent. You’re the only one ready for the next 10 years. That is the most important thing going forward. And I think one of the things which we are trying to do at the Aatil Tinkering Lag, because I have a team here, Dipali is here and with her she is the one who is putting it. At the school level. we are trying to bring AI and tinkering.

The idea of innovation that you… And what I’ve also started seeing as a trend from there that many of them may not be looking at going to a very formal education system, but getting into a job profile there and then. And it’s more task -oriented. So I’ll start off with this, and I know we’ll go on with the questions. But let me end here. But as I see it, I think that disruption in the next five years and 10 -year period will be a lot for all of us to learn psychologically on how can we be without a job when we are asked. That’s the first most important point. And then tend to see what is it which we can pick up to take on next, because that’s where we all talk about will be that reskilling piece coming in.

Sidharth Madaan

Radhika, you have done the research recently on this. Let me ask the same question to you. But let me add, are we overestimating near -term job loss? Are we overestimating the long -term transformation which it’s bringing?

Radhicka Kapoor

somewhat yes first let’s let me also somewhat endorse what Mr. Bagla said I think that this is there’s immense uncertainty and we really do need to have more granular and more nuanced understanding of what this transition actually entails because you know different segments of the population different segments of the workforce are going to be impacted differentially by this transition now there is this narrative of this doomsday prediction and we’re all going to lose our jobs and we’ve got to be psychologically prepared for losing our jobs I think yes it is indeed the case that most of our jobs are going to be exposed to automation and to gen AI but it doesn’t mean that our jobs are going to be destroyed or that they’re going to be completely dispased because if you go and look at the academic literature and a lot of the research the IMF that the managing director was spoke in the session before at the ILO we know that an occupation essentially entails many different tasks it’s a bundle of tasks now there are some tasks in those occupations which are going to get automated.

And there are others which are not going to be done, not going to be. Now, last year, the ILO, late last year, the ILO actually put out a study where they looked at all the different occupations and they did a gradation of the extent to which they were exposed to automation. Now, if you look at the share of jobs where almost all the tasks had a high likelihood of automation and therefore were likely to be displaced, that number was actually somewhere between 3 % or 4%. And that’s a global average. If you actually break it down and look at it in countries with low income, middle income, it was even lower. In high income countries, that was close to 6%.

But the share of jobs where some tasks were going to be automated, but that also meant that there was more scope for freeing up time to bring in new tasks, enhance their productivity, was actually quite high. That was about 20 % of the jobs. So what I’m saying is that in order to manage this transition, there are two things we’re going to have to do. One, of course, it is indeed the case that a small proportion of people will lose their jobs and they will be displaced. We need to think about how they are going to be absorbed in other sectors. And that, to my mind, is going to require more than skilling and reskilling.

It’s also going to require thinking more carefully about industrial policy, about macroeconomic policy, trade policies, labor market policies, in particular, social protection. But for those who are actually in the middle, where some tasks will be automated and others will not, we need to think carefully about how those occupations can actually augment their productivity, how they can engage more meaningfully with Gen AI and enhance their productivity. Because remember, all of this then also has an implication that enhanced productivity, which has an implication in wages and prices. All of that also boosts demand in the economy, which then drives more job creation and investment. And that virtuous cycle of growth, investment, job creation. So I would say that, you know.

So, yes, support those who are, you need policies to support those who will be displaced, but at the same time, augment productivity in the other jobs, which are somewhere in the middle, and there is some buffer against automation.

Sidharth Madaan

Sanjeev, with Naukri, you have a front seat to what’s happening in this space. Like, are you seeing structural shifts?

Sanjeev Bikhchandani

You know, there’s a lot of feedback we get from media, from social media, from panelists. But you know what, as of now, Naukri growth has not been impacted. So on the ground, we are not seeing a reduction in hiring. But at the same time, we are careful and cautious and say, what will happen now? Answer, I don’t know. Right. And the truth is, nobody knows. And anybody who is telling you he knows is… is wrong. They don’t know. So because there’s so much happening, and it’s so chaotic, that you can’t really figure out, right, what is going to happen. Right. But I’ll go back in history a bit. 1982 I was in college Deepak was in college we were in college together actually in Delhi University and these two new companies were set up Aptek and NIIT saying we are going to teach you how to use a personal computer nobody cared a few cared but it was not mainstream it was not ok so most people didn’t care by 1985 you know it had become somewhat a requirement that if you go and learn how to use a computer maybe your prospects of getting a job go up or if you got a job maybe you will become more productive at your job in 1985 to the Rajiv Gandhi government the government said we are going to introduce computers in banks at that time banks mostly public sector banks the All India Bank Employees Association which is one of the most powerful trade unions in the country then went ballistic so you got to lose jobs you are going to lose jobs government said never mind we are putting them in anyway so computers came into banks they weren’t used for a while then they began to get used and guess what nobody lost jobs people got more productive people got MIS that they weren’t getting earlier they served their customers better nobody lost jobs so new technology increased productivity did not cause job losses now I am not saying that is exactly what will happen this time but you know maybe now will some jobs or tasks be get automated possibly so but will others come up almost certainly yes so what I tell individuals never mind policy guys and governments and you know multilaterals what I an organization what an individual is look you don’t bother about will jobs be lost will my job be lost will I lose my job and will I get a new job will I get a new job Then my answer is simple.

Learn how to use three AI platforms every quarter. By the end of one year, you know 12 AI platforms. Believe me, you will be employable. I’ll give you an illustration of this. I finished business school in 1989. By then, I had finished college. I had done three years of work in an ad agency and had done business school. That’s very important year. Why is it an important year? Because the classes of 1988 and 1989 were the first two batches to have graduated from the IIMs who had actually used PCs at the IIMs because the PCs came into the IIMs in 1987. So I walked into my job as PC literate. There were two PCs in the marketing department at the company where I was working.

All the other people were senior, very highly qualified. IITs were senior. But I was the only guy who was PC literate. Believe me, if they were sacking then, I would have been the only guy who was PC literate. in that department, I would have been the last to go. I knew how to use that technology. So if AI is coming, it has come. It is inexorable. It is relentless. It will come. It has come. Learn how to use it. So if you don’t do AI, AI will be done to you.

Sidharth Madaan

Very insightful. I think if you optimize local optima, we are somewhere going to find the global balance. Prashant, with that, let me Radhika referred to, you know, job is a bundle of tasks. Tasks will get disrupted. But the role might shift for all of us. Let’s make it real. You are closer to the medical community. How does the role of a doctor or a nurse change going forward? Can we envision an AI doctor in the future? What would the job look like?

Prashant Warier

I think healthcare is slightly different from a lot of other industries. I think it is highly regulated, number one. So I think about three things from a healthcare perspective. From a futuristic perspective as well. One is that we have to be able to able to make sure that we are able to able to the capacity is limited especially if you’re talking about the global south right india has i mean we operate in the radiology ai space we automatically interpret radiology images with ai and if you look at india india has got one radiologist for every hundred thousand people which is about and us has one radiologist for 10 000 people kenya if you look at kenya has the same number of radiologists as marginal hospital so um and and many african countries have like one or two radiologists very very small number right and so there is not enough capacity to meet that demand so when you look at job loss per se i mean there is not enough capacity to meet the demand that is there for health care so in many ways i mean you’re not going to lose jobs it is going to upskill people health care workers and doctors who are on the ground supporting patients so that’s that’s one is about upskilling uh people right and supporting uh making health care workers able to uh support patients maybe there is an ai doctor that can do primary care i mean primary care is something that can be significantly automated i mean you’re looking at three things that you’re doing in primary care one is to understand patient symptoms so ai can prompt the patient can understand what symptoms they might have Second is to recommend tests, which again, AI can identify the right tests and recommend what testing should do.

And third is around diagnosis and treatment, right? Again, which AI can potentially do or even sort of triaging to the specialist. So these are things which AI can do. So I think in general, AI is going to upscale doctors and healthcare workers to do better and meet more patients and save time, right? One of the things that we are seeing across the world is you are using AI agents to scribe and take notes of the doctor -patient conversations, which is a task which, I mean, if a doctor is meeting 40, 50 patients a day, and after every one of those conversations, they have to write down, take notes from that conversation, AI can do that automatically.

We use that, we use note takers in our meetings. Why can’t you use note takers in a doctor -patient conversation? So we are seeing that, I mean, upscaling sort of one area. Second area, I think, which is going to, at least from a healthcare perspective, I see is a tough one is around regulation, right? Everything that… AI does today in… healthcare across the world. In the US, it’s FDA cleared. You have to get FDA cleared to be able to actually provide a clinical decision support to a doctor. So that is not going away right now. And that FDA equivalent, India, CDSCO, every country has its own regulatory body. So you have to figure out how to cross that barrier.

That hurdle is still there. And that is not something that is going away right now. And that brings me to the third point, which is that today, I mean, if a doctor is taking a decision on a patient saying that this patient has tuberculosis, for example, or lung cancer, or any of those, right, they are taking liability for that decision. And till AI is going to be able to take that liability, that is going to be a decision that doctors will make. And so what I see today, and for the next at least five to 10 years, is that AI is going to be supporting doctors in making better decisions. It is helping, it is providing all the data in the right format.

For example, what we do is we are able to bring in the right data, and we are able to bring in the right data. And so that is going to be bring data from electronic medical records, PAX, basically imaging data of the patient, pathology data of the patient, bring all of that together into one place for the doctors to help. diagnose better. So you’re providing that support to the doctor in making a clinical decision and also providing treatment planning, sort of automated treatment planning of treatment plans, which they can use to then provide the treatment plan for that patient. So it is a supportive tool and I see that for the next several years, AI is going to be upskilling doctors in providing better care and providing more care to patients, especially in the global

Sidharth Madaan

If, you know, multiple areas or multiple playgrounds where action is happening, like there’s startups, there’s infra, there is energy, you know, yesterday our Honourable Minister spoke about the five layers of AI. Where do you see most amount of action needed? Like if you had to pick one area to double down on, what does India need?

Deepak Bagla

Within the AI stack or generally?

Sidharth Madaan

Within the AI stack.

Deepak Bagla

I think on the application side is where we will have… a very interesting play on actually the small ones and actually getting them executed. That’s where we’ve had some strength in any case. But let me just step back a minute beyond this question, if I may, with your permission. You know, one very interesting thing. Yesterday, the plenary, I was sitting right there and next to me was a professor from an Ivy League. Let me not just say it, but one of the top five Ivy Leagues. And I was asking the professor, when are you going to go back and start teaching? Because he was taking a break to do it. He told me a very interesting thing.

One of the big things which is happening in this university is that the master’s students are feeling that they don’t longer need to pay that big tuition fees because they are no longer getting challenged. Because AI is giving them all the answers. Now see the repercussion of that. When we say that we have a million people coming into the job market every year, every month in India. that is because we go through a bachelor’s and a master’s and then they’re coming in so let’s say like sanjeev and i started 22 23 24 one of the most interesting elements which was pointed out was that maybe that age barrier no longer remains you may have somebody who’s 13 year old and ready to do a job in a task and that is another trend which might just picked up because the moment you’re going to see a complete change in the educational system think of two industries which have so far withstood or been having a pushback on the huge change which can come to them the financial industry is one and the education industry but now they’re being challenged on it in a big way you’re four years master of two years master’s four years bachelor’s maybe nobody needs to do it but they’re being challenged on it in a big way you’re four years master of two years master’s so see the number of people which will get into the task creation and the task doing force that is another element which we’ve not yet been able to quantify

Sidharth Madaan

very insightful answer jiv this will allow me to go back to the first question i asked you are you seeing a structural shift like for example are people now instead of asking degree pedigree asking for more afluency basic skills instead of

Sanjeev Bikhchandani

oh i people talk about it i’m not sure how many people actually do it right at the end of the day if you’ve got a credential it matters see uh what does an iit degree mean at what level it means you’ve learned something another level it means boss you were you you have demonstrated commitment to a prepare to get it so you you are able to work hard you know some level of physics chemistry maths that’s how you cleared the entrance exam right and you were at the top of the academic heap and that’s how you got into the place in the first place So when we go to IIT to recruit, we don’t hire for the specific knowledge they got at IIT.

We hire for the fact that it’s a fantastic filter on several accounts, right? Also, right? And to some extent, you know, a 13 -year -old, you know, ready for work. Look, business is about people. Business is about people and managing people and working with people and selling to people and, you know, running teams, being a good team player, being a good leader. So that comes with at least some years of experience, some years of, you know, maturity, right? So can I be a forex trader in front of a computer at 16? If I’m technically good enough, answer is yes. But can I be a forex trader in front of a computer at 16? Can I lead a team of salespeople out in the field?

who are calling on clients who are 20 years older than me, I don’t know. Maybe you can, maybe you can’t. So, you know, some stuff, I mean, people are still people.

Sidharth Madaan

Nadeka, what does it mean for the gig workers and the temp workforce? And, you know, the labor laws were written long back. What would it mean as we move ahead? How should we even think of the labor laws or the role that the temporary labor brings in? Like, we are done with the age of working in the same organization for 30 years, as I just mentioned.

Radhicka Kapoor

So you’re talking about temp workers and gig workers. And before I answer that more directly, I just want to reflect on the comments that have been made by the other panelists. You know, the conversation that we’re having here on displacement and productivity enhancement, including the comments that I made earlier, we’re really talking only about 10 % of India’s workforce at this point. The conversation on AI is right now, you know, today we are having this summit in the global south. And the Global South still, vast proportion of the workforce is in the informal sector. For India, 45 % of its workforce is still in the agricultural sector. 55 % of the people are self -employed.

95 % of employment is in enterprises with less than 10 workers. So, you know, that part of the conversation, we are completely missing out in the future of work. And I think we need to bring that in here as well, because a lot of the gig work and the casual work that you’re referring to is essentially what we see in the informal sector. And for that sector, the risk is not excessive automation. They might completely miss the bus and not realize any of these gains or productivity gains from AI. So we also need to think more carefully about how all of this can enhance productivity in the agricultural sector. How there could be greater AI adoption amongst micro and small.

All enterprises, which are basically the engines of job creation in India. and that’s again going to require a lot more than skilling and credentials but also they’re going to need a lot of financial support for adopting AI they are going to need digital infrastructure access to broadband so on and so forth and now going back to your question on the changes in the world of work and labor regulations indeed there is no denying the fact that labor regulations have not kept pace with the changes in the employer -employee relationships we now live in a world of work where there is a proliferation of non -standard employer employment arrangements the platform economy is a manifestation of that as well and certainly there’s a need to update that at the ILO for example there is a conversation for two years which is happening on what are the kinds of conventions and recommendations that are required to bring decent work into the platform economy and of course India is leading in that conversation with the code and social security which seeks to provide social protection even to platform workers so that’s a very forward looking ambition.

Sidharth Madaan

Yes well we’re at time but I’ll just say that I think it’s a very important point that I think it’s a very important point to just end with one last question rapid fire one word maximum five second answer a lot of still unknowns what does success look like in 2030 what would you be proud of we’ll go in a row

Deepak Bagla

most critical point when everyone works together the government the society the people the academia i think that joining the dots is absolutely core to seeing any element of success for anyone and last point i think the biggest benefit of the delta multiplier of ai is india or will be india

Sidharth Madaan

Prashant

Prashant Warier

i think success for ai is the world’s gdp growing at 10 or more by 2030

Sanjeev Bikhchandani

i think uh if there is net job increase which means the jobs lost if any are less than the jobs created i think that is success

Radhicka Kapoor

i think an inclusive ai transition where we have better jobs, more productive jobs, and where the agricultural sector and the MSME sector have benefited from this transition and we don’t leave the informal sector behind.

Sidharth Madaan

With that, we’ll wrap this panel discussion. Thank you so much for the insightful comments.

Deepak Bagla

Speech speed

176 words per minute

Speech length

Vision of success by 2030

Topics

Artificial intelligence | Social and economic development

Radhicka Kapoor

Speech speed

186 words per minute

Speech length

Vision of success by 2030

Topics

Social and economic development | The digital economy

Sanjeev Bikhchandani

Speech speed

163 words per minute

Speech length

Vision of success by 2030

Topics

Social and economic development

Prashant Warier

Speech speed

211 words per minute

Speech length

Vision of success by 2030

Topics

Artificial intelligence | The digital economy

Sidharth Madaan

Speech speed

189 words per minute

Speech length

AI‑driven disruption and the need for reskilling

Topics

The digital economy

Agreements

Agreement points

Unprecedented uncertainty and lack of playbook for AI transition

Speakers

– Deepak Bagla
– Radhicka Kapoor
– Sanjeev Bikhchandani

Arguments

Next 5-10 years will bring unprecedented disruption requiring psychological preparation for job loss

Need policies supporting displaced workers through industrial, macroeconomic, and social protection measures

Current hiring data shows no reduction in job demand despite AI concerns

Summary

All three speakers acknowledge that we are in uncharted territory with AI transformation, emphasizing the uncertainty and need for careful preparation despite different perspectives on the scale of disruption

Topics

Artificial intelligence | The digital economy

AI will augment rather than completely replace most jobs

Speakers

– Radhicka Kapoor
– Prashant Warier
– Sanjeev Bikhchandani

Arguments

Only 3-4% of jobs globally face complete displacement, with 20% experiencing partial task automation

Healthcare won’t see job losses due to severe capacity shortages, especially in global south

Net job creation with more jobs created than lost

Summary

There is strong consensus that AI will primarily enhance productivity and augment human capabilities rather than cause mass unemployment, with most jobs experiencing task-level changes rather than complete elimination

Topics

Artificial intelligence | The digital economy | Social and economic development

Need for comprehensive policy responses beyond just reskilling

Speakers

– Deepak Bagla
– Radhicka Kapoor

Arguments

Success requires collaboration between government, society, academia, and people

Need policies supporting displaced workers through industrial, macroeconomic, and social protection measures

Summary

Both speakers emphasize that managing AI transition requires holistic policy approaches involving multiple stakeholders and comprehensive support systems, not just individual skill development

Topics

The enabling environment for digital development | Artificial intelligence | Capacity development

Importance of continuous learning and adaptation

Speakers

– Deepak Bagla
– Sanjeev Bikhchandani

Arguments

Focus should be on task-oriented skills rather than formal education systems

Individuals should learn 3 AI platforms quarterly to remain employable

Summary

Both speakers stress the critical importance of ongoing skill development and adaptation to new technologies, though they differ on whether this should happen within or outside traditional education systems

Topics

Capacity development | Artificial intelligence

The enabling environment for digital development | Artificial intelligence | Capacity development

Unexpected consensus

AI will benefit developing countries more than developed ones

Speakers

– Deepak Bagla
– Radhicka Kapoor
– Prashant Warier

Arguments

India positioned as biggest beneficiary of AI’s delta multiplier effect

Only 3-4% of jobs globally face complete displacement, with 20% experiencing partial task automation

Healthcare won’t see job losses due to severe capacity shortages, especially in global south

Explanation

Surprisingly, all speakers suggest that developing countries, particularly India, may actually benefit more from AI than developed countries due to capacity gaps, unmet demand, and opportunities for leapfrogging. This contrasts with typical narratives about technology divides

Topics

Artificial intelligence | Closing all digital divides | Social and economic development

Traditional education credentials will persist despite AI disruption

Speakers

– Deepak Bagla
– Sanjeev Bikhchandani

Arguments

Traditional education system faces challenges as students question value of expensive degrees

Credentials and institutional filters still matter for hiring despite skills-based rhetoric

Explanation

Despite Bagla’s observation about students questioning expensive degrees, both speakers ultimately agree that institutional credentials will remain important, suggesting that education will transform rather than disappear

Topics

Capacity development | Social and economic development | The enabling environment for digital development

Overall assessment

Summary

The speakers demonstrate remarkable consensus on key issues: AI will augment rather than replace most jobs, developing countries may benefit more than developed ones, comprehensive policy responses are needed beyond just reskilling, and continuous learning is essential. They agree on the unprecedented nature of the transition while maintaining optimism about net positive outcomes.

Consensus level

High level of consensus with complementary rather than conflicting perspectives. The agreement spans technical experts, policy researchers, business leaders, and industry practitioners, suggesting robust foundation for collaborative approaches to AI transition. The consensus implies that coordinated, multi-stakeholder responses focusing on augmentation rather than replacement, with special attention to developing country opportunities, may be the most effective path forward.

Differences

Capacity development | The digital economy

Unexpected differences

Current market reality vs future predictions

Speakers

– Sanjeev Bikhchandani
– Deepak Bagla

Arguments

Current hiring data shows no reduction in job demand despite AI concerns

Next 5-10 years will bring unprecedented disruption requiring psychological preparation for job loss

Explanation

Despite both being business leaders, Bikhchandani reports no current impact on hiring while Bagla predicts severe disruption ahead, showing disagreement on whether AI impact is imminent or already manifesting

Topics

The digital economy | Artificial intelligence

Scope of AI impact discussion

Speakers

– Radhicka Kapoor
– Other panelists

Arguments

90% of India’s informal workforce risks missing AI productivity gains entirely

Various arguments focused on formal sector workers

Explanation

Kapoor uniquely challenges the entire panel’s focus by pointing out they’re only discussing 10% of India’s workforce, while 90% in the informal sector face completely different AI-related challenges

Topics

Closing all digital divides | Social and economic development | The digital economy

Overall assessment

Summary

The panel shows moderate disagreement on the timeline and severity of AI’s impact on employment, the continued relevance of traditional education credentials, and the scope of workers that should be prioritized in AI transition discussions

Disagreement level

Moderate disagreement with significant implications – while speakers agree AI will transform work, their different perspectives on timing, scale, and appropriate responses could lead to very different policy recommendations and individual preparation strategies

Partial agreements

All speakers agree that AI will bring significant changes requiring adaptation, but they disagree on the scale of disruption and appropriate response strategies – Bagla focuses on psychological preparation for widespread displacement, Kapoor emphasizes comprehensive policy responses, and Bikhchandani advocates for individual skill development

Speakers

– Deepak Bagla
– Radhicka Kapoor
– Sanjeev Bikhchandani

Arguments

Next 5-10 years will bring unprecedented disruption requiring psychological preparation for job loss

Need policies supporting displaced workers through industrial, macroeconomic, and social protection measures

Individuals should learn 3 AI platforms quarterly to remain employable

Topics

Artificial intelligence | Capacity development | The digital economy

Both speakers agree that certain sectors face unique challenges with AI adoption, but they focus on different solutions – Kapoor emphasizes the need for financial support and digital infrastructure for informal workers, while Warier focuses on regulatory frameworks and upskilling in healthcare

Speakers

– Radhicka Kapoor
– Prashant Warier

Arguments

90% of India’s informal workforce risks missing AI productivity gains entirely

Healthcare won’t see job losses due to severe capacity shortages, especially in global south

Topics

Closing all digital divides | Social and economic development | Artificial intelligence

The enabling environment for digital development | Artificial intelligence | Capacity development

Takeaways

Key takeaways

The next 5-10 years will bring unprecedented AI-driven workplace disruption, but complete job displacement affects only 3-4% of roles globally while 20% will see partial task automation

Individual adaptation through continuous AI learning (3 platforms per quarter) is more effective than waiting for systemic solutions

Healthcare and sectors with capacity shortages will see AI augmentation rather than job replacement, with workers becoming more productive

India’s informal sector (90% of workforce) risks being left behind in AI transition, requiring targeted policy interventions for agricultural and MSME sectors

Traditional education systems face fundamental challenges as AI reduces the perceived value of formal degrees and credentials

Success requires collaborative approach between government, academia, society, and individuals rather than isolated efforts

Regulatory frameworks and liability structures will slow AI adoption in critical sectors like healthcare, maintaining human oversight requirements

Resolutions and action items

Individuals should learn to use three AI platforms every quarter to maintain employability

Implement policies supporting displaced workers through industrial, macroeconomic, and social protection measures

Focus on AI applications layer development as India’s competitive advantage area

Provide financial support and digital infrastructure access to micro and small enterprises for AI adoption

Update labor regulations to address non-standard employment arrangements and platform economy workers

Unresolved issues

How to quantify and manage the influx of younger workers (potentially 13-year-olds) entering task-oriented job markets

Whether credentials and formal education will actually become less important in hiring practices despite current rhetoric

How to effectively bridge the gap between AI productivity gains and the 90% informal workforce in developing countries

The timeline and mechanism for updating labor laws to match evolving employer-employee relationships

How to balance AI automation with regulatory requirements and liability concerns in critical sectors

The actual impact on traditional education business models and alternative pathways to employment

Suggested compromises

Focus on augmenting human capabilities with AI rather than complete replacement, especially in healthcare and knowledge work

Develop hybrid approaches where AI handles routine tasks while humans retain decision-making authority and liability

Create inclusive AI transition strategies that benefit both formal and informal sectors rather than leaving traditional workers behind

This comment is thought-provoking because it identifies a fundamental disruption to the education system that hadn’t been discussed. It suggests that AI might not just change jobs but could collapse traditional educational pathways and age-based career progression, which has profound implications for how we think about human capital development.

Impact

This comment introduced an entirely new dimension to the discussion – the disruption of educational institutions and traditional career timelines. It prompted Sanjeev to respond with a more nuanced view about the continued importance of credentials and human maturity in business contexts, adding complexity to the conversation about skills versus credentials.

Overall assessment

Explanation

Kapoor mentioned that supporting displaced workers will require ‘more than skilling and reskilling’ including ‘industrial policy, macroeconomic policy, trade policies, labor market policies, in particular, social protection’ but didn’t detail specific policy recommendations.

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.

Building Trusted AI at Scale Cities Startups & Digital Sovereignty – Panel Discussion Moderator Sidharth Madaan

Summary

Keypoints

Major Discussion Points:

Overall Purpose:

Overall Tone:

Speakers

The Unprecedented Nature of AI Disruption

Educational System Disruption and Workforce Entry Points

Evidence-Based Analysis vs. Doomsday Predictions

Current Market Reality and Individual Adaptation Strategies

Sector-Specific Transformations: Healthcare as a Case Study

The Informal Sector Challenge: India’s Missing 90%

Policy Frameworks and Labour Market Evolution

Vision for Success in 2030

Conclusion and Implications

Psychological readiness & reskilling imperative

Explanation

Evidence

Major discussion point

Topics

Task‑oriented jobs and erosion of degree relevance

Explanation

Evidence

Major discussion point

Topics

Prioritise the application layer and foster collaboration

Explanation

Evidence

Major discussion point

Topics

Vision of success by 2030 – joint effort

Explanation

Evidence

Major discussion point

Topics

Policy beyond skilling, including social protection

Explanation

Evidence

Major discussion point

Topics

Only 3‑6 % of jobs fully automatable; many see partial automation

Explanation

Evidence

Major discussion point

Topics

Gig/informal workers risk being left behind; need infrastructure & finance

Explanation

Evidence

Major discussion point

Topics

Update labour laws and extend social protection to platform workers

Explanation

Evidence

Major discussion point

Topics

Inclusive AI transition benefiting agriculture, MSMEs and informal sector

Explanation

Evidence

Major discussion point

Topics

Quarterly learning of AI platforms to stay employable

Explanation

Evidence

Major discussion point

Topics

Historical tech adoption boosted productivity without mass layoffs

Explanation

Evidence

Major discussion point

Topics

Hiring remains robust; AI skill acquisition essential

Explanation

Evidence

Major discussion point

Topics

Credentials remain a strong hiring filter

Explanation

Evidence

Major discussion point

Only 3‑6 % of jobs fully automatable; many see partial automation

Global GDP growth exceeding 10 % driven by AI by 2030