Building Trusted AI at Scale Cities Startups & Digital Sovereignty – Panel Discussion Moderator Sidharth Madaan
20 Feb 2026 13:00h - 14:00h
Building Trusted AI at Scale Cities Startups & Digital Sovereignty – Panel Discussion Moderator Sidharth Madaan
Summary
The panel opened by noting that the world is at a “defining moment” for work, with AI promising new productivity and jobs while also generating anxiety about disruption to white-collar roles [1][2][3]. Deepak Bagla emphasized that no clear playbook exists for the coming transition, recalling how bank tellers-once thought immune-were the first to disappear with digitisation, and warning that the next five years will be especially turbulent [6-16]. He argued that psychological readiness and rapid reskilling will be essential, urging workers to identify new skill sets to stay employable [27-29].
Radhika presented research showing that only about 3-4 % of global occupations are at high risk of full automation, rising to roughly 6 % in high-income countries, while around 20 % face partial task automation that can free time for higher-value work [34-42]. She stressed that supporting the small fraction of displaced workers will require not just training but broader industrial, macro-economic and social-protection policies, whereas the larger “middle” group should focus on augmenting productivity with generative AI [43-49].
Sanjeev Bikhchandani reported that Naukri’s hiring volumes have not yet declined, underscoring the difficulty of forecasting impacts, and he drew a parallel to the 1980s computer adoption that ultimately boosted productivity rather than causing layoffs [56-60][64-68]. He advised individuals to continuously learn multiple AI platforms-four per quarter-to remain employable in a rapidly changing landscape [65-68].
Prashant Warier explained that in healthcare AI will primarily upskill doctors by automating radiology interpretation, note-taking, and test recommendation, thereby expanding capacity especially in low-resource settings [95-104]. However, he cautioned that strict regulatory approval and liability concerns mean AI will remain a decision-support tool rather than a full replacement for clinicians for the foreseeable decade [108-119].
Returning to the gig and informal economy, Radhika highlighted that over 45 % of India’s workforce is in agriculture and 95 % of enterprises have fewer than ten employees, making them vulnerable to being left out of AI gains unless digital infrastructure and tailored financial support are provided [161-172]. She also noted that existing labour laws lag behind the rise of platform work, prompting calls for updated conventions and social-security schemes to protect non-standard workers [173-176].
In the rapid-fire closing, Bagla called for coordinated action among government, academia and society to harness AI’s “delta multiplier” for national benefit [175]. Prashant projected that a successful AI rollout could lift global GDP growth to 10 % or more by 2030 [176]. Radhika concluded that an inclusive transition-creating better, more productive jobs while ensuring agricultural and MSME sectors and informal workers are not abandoned-should be the benchmark of success [178].
Keypoints
Major discussion points
– Uncertainty and the need for psychological readiness and reskilling – The panel stressed that there is “no playbook” for the coming AI era and that the next five years will be “the toughest times of disruption.” Preparing people mentally for possible job loss and identifying new skill sets are seen as the first priorities [12-16][27-29].
– Limited full-automation risk but widespread task-level change – Research cited by Radhika shows that only 3-6 % of jobs worldwide have a high likelihood of total displacement, while about 20 % will see some tasks automated, creating opportunities to boost productivity. Managing this transition therefore requires not only reskilling but also broader industrial, macro-economic and social-protection policies [34-42][44-48].
– Current labour market signals and the importance of continuous AI upskilling – Sanjeev noted that hiring at Naukri has not yet declined, echoing the 1980s computer adoption story where productivity rose without mass layoffs. He advises individuals to “learn how to use three AI platforms every quarter” to stay employable [57-60][64-68][70-84].
– Healthcare as a sector where AI augments rather than replaces professionals – Prashant explained that AI can help address radiology shortages, automate routine primary-care tasks, and provide decision-support tools, but regulatory clearance and liability issues mean doctors will remain central for the next 5-10 years [95-104][108-119][120-124].
– Implications for the informal, gig and MSME workforce and the need for inclusive policy – Radhika highlighted that over 90 % of India’s workers are in agriculture, self-employment or micro-enterprises, sectors that risk being left out of AI gains. She called for digital infrastructure, financing, and updated labour-law frameworks to extend AI benefits to these workers [161-170][172-176][177-178].
Overall purpose / goal of the discussion
The panel convened to assess how generative AI will reshape the future of work-especially knowledge-intensive and informal jobs-by weighing disruption against productivity gains, identifying skill and policy gaps, and proposing concrete actions for businesses, policymakers, educators and workers to navigate the transition responsibly.
Overall tone and its evolution
The conversation began with a tone of caution and anxiety about “disruption” and job loss [1-3][12-16]. As speakers shared data and historical analogues, the tone shifted to a more measured, evidence-based perspective that emphasized opportunities and the importance of upskilling [34-42][57-68]. When discussing specific sectors (healthcare) and the vast informal economy, the tone became constructive and forward-looking, calling for inclusive policies and collaborative effort [95-124][161-178]. The discussion closed on an optimistic, collaborative note, stressing collective action and a vision of AI-driven growth for India [175-178].
Speakers
– Speaker 1 – Role: Moderator / host (appears to be the event moderator) – Area of expertise: (not specified)[S1]
– Prashant Warier – Area of expertise: Healthcare AI, radiology-AI applications – Role/Title: Panelist (no specific title mentioned)[S4]
– Deepak Bagla – Area of expertise: AI innovation, policy & entrepreneurship – Role/Title: Mission Director, Atal Innovation Mission[S6][S7]
– Sanjeev Bikhchandani – Area of expertise: Employment platforms, AI in recruitment – Role/Title: Founder, InfoEdge (Naukri.com)[S8][S9]
– Radhika – Area of expertise: Labour economics, AI-impact research – Role/Title: Researcher (Podar International School)[S10][S11]
Additional speakers
– Nadeka – (no role, title or area of expertise mentioned in the transcript or sources)
– jiv – (no role, title or area of expertise mentioned in the transcript or sources)
The panel opened by framing the present as a “defining moment” for work, where generative AI promises fresh productivity gains and new jobs while simultaneously fuelling anxiety about disruption to white-collar occupations [1-3]. The moderator then asked Deepak Bagla to outline how businesses and policy-makers should navigate this transition [4-5].
Bagla responded that there is “no playbook” for the coming era [12-13]. He recalled that, in 1986, bank tellers were taught they were the only immutable role, yet they became the first victims of digitisation [7-11]. Projecting forward, he warned that the next five years are likely to be among the toughest periods of disruption [15-18] and that workers must first prepare psychologically for possible job loss before thinking about reskilling [27-29]. To build future resilience, he highlighted a school-level initiative that introduces AI and tinkering, encouraging a shift toward task-oriented learning rather than traditional, formal education pathways [21-25].
Bagla also recounted an anecdote about an Ivy-League professor whose master’s students began questioning high tuition fees because AI could provide answers, illustrating early signs of credential pressure [124-129].
Radhika complemented this view with empirical data. She noted that only 3-4 % of global occupations have a high likelihood of full automation, rising to about 6 % in high-income economies [37-41]. Around 20 % of jobs will experience partial task automation, freeing time for higher-value work [42]. Consequently, she argued that managing the transition requires more than reskilling; it also demands coordinated industrial, macro-economic, trade and social-protection policies to absorb displaced workers and to support those whose roles are only partially automated [44-48][49-51].
Sanjeev Bikhchandani, representing the online recruitment platform Naukri, reported that hiring volumes have not yet declined, underscoring the difficulty of forecasting AI’s impact [57-60]. He drew a parallel with the 1980s introduction of personal computers, which ultimately boosted productivity without massive layoffs [64-68]. From this history he distilled a personal prescription: individuals should master three new AI platforms each quarter – roughly twelve a year – to remain employable [65-68][70-84].
Prashant Warier turned to the health sector, pointing out the acute shortage of radiologists in India (one per 100 000 people) and many low-resource countries [95-99]. He explained that AI can upscale clinicians by automating image interpretation, note-taking, test recommendation and triage, thereby expanding capacity [99-104][105-107]. However, he cautioned that regulatory clearance (e.g., FDA, CDSCO) and liability concerns will keep doctors and nurses central to decision-making for at least the next five to ten years, positioning AI as a decision-support tool rather than a replacement [108-119][120-124].
The conversation then shifted to education and credentials. Bagla suggested that AI may erode the value of long-duration degrees, potentially allowing teenagers as young as 13 years to perform task-based work and that the traditional “age barrier” could disappear [130-135]. Sanjeev counter-pointed that elite degrees such as those from IITs still serve as strong filters of ability, perseverance and problem-solving, and that leadership roles continue to require people-skills and maturity beyond technical fluency [136-144].
Addressing the vast informal economy, Radhika reminded the panel that over 45 % of India’s workforce remains in agriculture, 55 % is self-employed and 95 % work in enterprises with fewer than ten employees [165-167][169-172]. For this segment, the primary risk is not automation but exclusion from AI-driven productivity gains due to limited digital infrastructure, financing and skill access [170-173]. She called for updated labour-law frameworks that cover platform and gig work, alongside social-security schemes such as India’s “code” for platform workers [173-176].
When asked which layer of the AI stack should receive priority, Bagla advocated focusing on the application side, where small, executable solutions can be rapidly scaled [150-152]. He also stressed that success will hinge on coordinated action among government, academia, industry and civil society, describing the AI “delta-multiplier” as a core engine for India’s growth [175].
Across the discussion, the panel found common ground on several points: continuous upskilling is essential-whether through school-level tinkering, broad reskilling programmes, or personal mastery of multiple AI tools [21-28][43-49][65-67]; AI is expected to augment productivity rather than wholesale replace jobs, with only a modest share of occupations facing full displacement and many roles being reshaped at the task level [24-25][41-43][99-104][119-121]; and an effective transition requires coordinated policy beyond training, encompassing industrial strategy, macro-economic measures and social protection [175][46-48].
Nevertheless, notable disagreements persisted. Bagla warned of a severe, near-term disruption wave, whereas Radhika’s data suggested that full-automation risk is limited to a small fraction of jobs [16][27-28][37-42]. Bagla’s emphasis on early education and psychological readiness contrasted with Radhika’s call for broader macro-policy interventions to absorb displaced workers [21-28][46-48]. On the relevance of formal degrees, Bagla envisaged a future where AI flattens credential hierarchies, while Sanjeev maintained that elite degrees remain valuable signals of ability and commitment [130-135][136-144]. Finally, the panel differed on how to define “success” by 2030: Bagla spoke of a coordinated, inclusive AI multiplier for India; Sanjeev focused on net job creation; Prashant projected a 10 %+ rise in global GDP; and Radhika insisted on an inclusive transition that benefits agriculture, MSMEs and the informal sector [175][176][177][178].
Key take-aways
– Anticipate disruption: the next five to ten years will be the most disruptive period, yet no definitive playbook exists [15-18].
– Recognise automation limits: only 3-6 % of jobs face near-total automation while ~20 % will see partial task automation, creating productivity opportunities [37-42].
– Deploy coordinated policies: industrial, macro-economic, trade and social-protection measures are required alongside reskilling [44-48].
– Master multiple AI tools: individuals should aim to master three new AI platforms each quarter [65-68][70-84].
– Introduce AI early in schools: early exposure fosters task-oriented mindsets [21-25].
– Augment, not replace, clinicians: AI will support doctors and nurses, with regulatory and liability frameworks limiting full replacement [108-119][120-124].
– Value elite credentials for soft skills: while AI challenges traditional degrees, elite qualifications still signal essential people-skills [136-144].
– Include the informal sector: bring agriculture, MSMEs and gig workers into the AI fold through digital infrastructure, financing and updated labour laws [170-173][173-176].
– Prioritise the application layer: focus on executable AI solutions that can be scaled quickly [150-152].
– Measure success inclusively: by 2030, success should be gauged through inclusive job creation, productivity gains and contribution to high-growth economies [175-178].
The rapid-fire closing reinforced this vision: Bagla called for all stakeholders to work together to unleash the AI delta-multiplier for India [175]; Prashant projected that AI-driven growth could lift global GDP by more than 10 % by 2030 [176]; Sanjeev defined success as a net increase in jobs [177]; and Radhika summed up an inclusive AI transition that delivers better, more productive employment across agriculture, MSMEs and the informal sector [178]. The panel therefore concluded that, despite uncertainty, a coordinated, skill-focused yet policy-rich approach can steer the AI revolution toward broad-based prosperity.
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?
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.
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?
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.
Sanjeev, with Naukri, you have a front seat to what’s happening in this space. Like, are you seeing structural shifts?
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.
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?
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
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?
Within the AI stack or generally?
Within the AI stack.
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
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
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.
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.
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.
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
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
krishan i think success for ai is the world’s gdp growing at 10 or more by 2030
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
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.
With that, we’ll wrap this panel discussion. Thank you so much for the insightful comments.
“The moderator framed the present as a “defining moment” for work, where generative AI promises fresh productivity gains and new jobs while simultaneously fuelling anxiety about disruption to white‑collar occupations.”
The moderator’s description of a defining moment with dual possibilities and anxiety is directly echoed in the knowledge base entries describing the panel’s framing of the AI transition as a pivotal moment for work with new productivity and job creation alongside growing anxiety [S4] and [S5] and [S70].
“Bagla responded that there is “no playbook” for the coming era.”
Deepak Bagla’s statement that there is no playbook aligns with the knowledge-base note that highlights his view of working without a playbook as a key strength in the AI-driven future [S14].
“Projecting forward, he warned that the next five years are likely to be among the toughest periods of disruption.”
Historical analyses in the knowledge base note that transition periods after major technological shifts can be especially challenging, providing context for Bagla’s warning about a tough five-year disruption window [S68].
“He highlighted a school‑level initiative that introduces AI and tinkering, encouraging a shift toward task‑oriented learning rather than traditional, formal education pathways.”
The knowledge base discusses a broader move toward competency-based, task-oriented education as opposed to duration-based degree programs, adding nuance to Bagla’s school-level AI initiative claim [S74].
“He drew a parallel with the 1980s introduction of personal computers, which ultimately boosted productivity without massive layoffs.”
The knowledge base references historical precedent that new technologies (e.g., personal computers) tend to create jobs over the long term, even if the transition is challenging, supporting the analogy used by Bagla [S68].
The panel shows strong convergence on three core themes: (1) the necessity of continuous upskilling/reskilling; (2) AI as a productivity‑enhancing tool that will reshape tasks rather than eliminate whole occupations; and (3) the need for coordinated policy action beyond individual training. These shared positions cut across speakers from business, academia, and the tech sector, indicating a broad consensus on how to navigate the AI transition.
High – the majority of speakers align on the nature of AI’s impact (augmentation over displacement) and on the combined need for skill development and systemic policy support. This consensus suggests that future initiatives should prioritize integrated skill programmes, inclusive policy frameworks, and application‑focused AI deployment to harness AI’s growth potential while mitigating disruption.
The panel shows substantial disagreement on the scale of AI‑driven job loss, the primary policy response (skill‑centric vs macro‑policy), the future relevance of formal education, and the metric for a successful AI transition. While there is consensus that AI will reshape work and that upskilling is needed, the speakers diverge sharply on how severe the disruption will be, which levers should dominate policy, and whether growth should be measured by GDP, net job creation, or inclusive outcomes.
High – the differing views on magnitude, policy focus, education pathways and success criteria indicate that stakeholders are not aligned on core strategic choices. This lack of consensus could lead to fragmented interventions, risking either over‑regulation or insufficient support for vulnerable groups, and may impede the formulation of a coherent national AI strategy.
The discussion was steered by a handful of data‑rich and perspective‑shifting remarks. Deepak Bagla’s opening anecdote shattered the myth of ‘safe’ jobs, prompting a focus on uncertainty. Radhika’s automation statistics reframed the narrative from catastrophic loss to nuanced, partial disruption, which opened space for policy‑oriented dialogue. Sanjeev’s historical analogy and concrete learning roadmap gave the audience actionable takeaways, while Prashant’s sector‑specific analysis showed how AI’s impact varies across industries. Deepak’s education‑disruption insight and Radhika’s reminder of the informal‑sector majority broadened the conversation beyond the formal economy. Sanjeev’s credential‑vs‑skill comment added balance, preventing an overly technocratic view. Collectively, these pivotal comments redirected the panel from speculative fear to a layered, inclusive vision of AI‑driven work, shaping both the tone and the substantive direction of the entire discussion.
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.
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