Skilling and Education in AI

20 Feb 2026 17:00h - 18:00h

Session at a glance

Summary

This discussion focused on leveraging artificial intelligence as a tool for development and equality in India, examining both opportunities and challenges in AI adoption across various sectors. The conversation began with identifying four key areas where AI can make significant impact: agriculture, small businesses, education, and healthcare, with particular emphasis on how AI could help smallholder farmers reduce crop losses from 40-50% to 20-30% through pest identification and localized solutions.

A central theme emerged around the need to build “trust infrastructure” alongside digital infrastructure, as people will only adopt AI technologies they can understand and trust. The speakers highlighted India’s unique advantages, including high digital trust levels (70% compared to 25-30% in the US), widespread connectivity, and successful digital public infrastructure like UPI. However, they acknowledged that AI will inevitably create inequalities, as algorithms reflect historical biases and access to advanced AI tools remains uneven globally.

The discussion revealed comprehensive skilling initiatives already underway, including AI awareness programs, vocational training integration, and stackable micro-credentials that can adapt to rapidly changing skill requirements. Speakers emphasized moving from traditional digital literacy to “work literacy” with bite-sized, consumable content that reduces friction to learning. They also addressed the challenge of preparing workers for environments where they collaborate with physical AI and autonomous systems.

Infrastructure development emerged as crucial, with investments in data centers, subsea cables, and compute capacity to reduce dependence on external resources. The panelists concluded by identifying key priorities for 2030: building trust infrastructure, providing every Indian with an AI assistant, embedding ethics in AI education, ensuring affordable compute access, and creating economic models for AI diffusion to make artificial intelligence truly serve as an equalizer in Indian society.

Keypoints

Major Discussion Points:

– AI Applications for Development: The discussion highlighted four key sectors where AI can drive significant impact in India – agriculture (helping smallholder farmers reduce crop losses from 40-50% to 20-30%), small businesses (enabling one-person operations through AI assistance), education and skill building, and healthcare.

– Trust Infrastructure as Critical Challenge: A major theme was the need to build “trust infrastructure” alongside digital infrastructure. Speakers emphasized that people will only adopt AI if they understand and trust it, addressing concerns about algorithmic transparency, data usage, and decision-making processes.

– AI as a Force for Inequality: The discussion acknowledged that AI will likely increase inequality through various mechanisms – algorithms reflecting historical biases, unequal access to tools and resources, geographic disparities, and concentration of AI development in few companies/countries.

– Skilling and Workforce Transformation: Extensive discussion on preparing India’s workforce for AI integration, including creating stackable micro-credentials, embedding AI modules in existing training programs, and developing AI literacy from basic awareness to advanced engineering skills.

– India’s Unique Advantages and Infrastructure Needs: The conversation highlighted India’s strong starting position with high digital trust levels (70% vs 25-30% in the US), robust digital public infrastructure, and the opportunity to create AI solutions specifically for Indian contexts and languages.

Overall Purpose:

The discussion aimed to explore how India can leverage AI as an equalizer rather than a divider, focusing on practical strategies for implementation across key sectors while addressing challenges around trust, inequality, and workforce preparation.

Overall Tone:

The tone was cautiously optimistic throughout. Speakers acknowledged both the tremendous opportunities AI presents for India’s development and the significant challenges that must be addressed. The conversation maintained a balanced, pragmatic approach – neither dismissing concerns nor dampening enthusiasm – while emphasizing the need for thoughtful, human-centered implementation strategies.

Speakers

Speakers from the provided list:

– Speaker 1: Professor (specific title/role mentioned in transcript), expert in AI applications, agriculture, and trust infrastructure

– Speaker 2: CEO of NSDC (National Skill Development Corporation), expert in workforce skilling and AI training programs

– Neena Pahuja: Former executive member of NCBT (National Council for Vocational Training), expert in certification standards and AI skilling frameworks

– Rakesh Kaul: Expert in digital infrastructure and AI accessibility

– Speaker 3: Representative from a technology company involved in AI infrastructure development, data centers, and connectivity solutions

– Moderator: Discussion facilitator

Additional speakers:

– Arunji: Mentioned by the Moderator as CEO of NSDC, appears to be the same person as Speaker 2

– Neenaji: Mentioned by the Moderator, appears to be referring to Neena Pahuja

– Anandji: Mentioned by the Moderator in reference to skilling programs, but does not appear to speak directly in the transcript

Full session report

This comprehensive discussion examined how India can leverage artificial intelligence as a transformative force for development and equality, bringing together perspectives from academia, government agencies, and industry to address both the immense opportunities and significant challenges facing AI adoption across Indian society.

AI’s Transformative Potential Across Key Sectors

The conversation began with a Professor’s detailed analysis of four critical sectors where AI can drive substantial impact in India. Agriculture emerged as the highest-priority area, not only because it employs the largest workforce but also due to its significant productivity gaps. The Professor highlighted how smallholder farmers in the global south typically lose 40-50% of their crops to pests, representing a massive economic opportunity. AI-powered pest identification systems that provide solutions in local languages using locally available ingredients could reduce these losses to 30% or 20%, creating substantial income improvements for farmers. This represents a compelling use case where the human incentive for adoption is clear and immediate.

Small businesses were identified as the second major opportunity, where AI can enable entrepreneurs to operate as one-person enterprises by providing capabilities traditionally requiring multiple employees. AI can handle market research, analysis, and various business functions, democratising access to sophisticated business tools. Education and skill building, along with healthcare, rounded out the four priority sectors, each offering unique opportunities for AI to address systemic challenges and improve outcomes for millions of Indians.

The Critical Challenge of Trust Infrastructure

A central theme that emerged was the Professor’s concept of “trust infrastructure” as perhaps the most significant barrier to AI adoption. Unlike previous technology rollouts where access, connectivity, or device availability were primary constraints, the AI revolution faces a fundamentally different challenge. India has successfully built robust digital public infrastructure and has achieved widespread connectivity and device penetration. However, the opacity of AI systems creates a unique trust gap that must be bridged.

The discussion revealed that users need to understand what happens inside AI “black boxes” to feel comfortable accepting AI-generated decisions and outputs. This trust challenge manifests in multiple ways: concerns about hiring algorithms and their fairness, questions about medical diagnoses provided by AI systems, uncertainty about language translation accuracy, and scepticism about AI-generated images and content on social media. Additionally, users are increasingly aware that their interactions with AI systems generate data that becomes input for further AI development, raising questions about data ownership, usage, and potential misuse.

The Professor noted India’s unique advantage in this context through comparative trust statistics: digital trust levels in India stand at approximately 70%, compared to just 25-30% in the United States. This “trust dividend” represents a significant competitive advantage that could accelerate AI adoption if properly leveraged, but it also creates a responsibility not to squander this societal asset through poor implementation or broken promises.

Acknowledging AI as a Force for Inequality

The Professor took a notably realistic turn in acknowledging that AI will inevitably create new forms of inequality, despite its potential benefits. However, he emphasized that “none of this means we should stop the train” regarding AI development. This inequality stems from several structural factors that cannot be easily addressed through policy alone. Algorithms trained on historical data will inevitably reflect and potentially amplify past inequalities, as data serves as “a mirror to our past,” and historical patterns have not been equitable.

Access inequality will manifest in multiple dimensions: differential access to advanced AI tools, geographic disparities between urban and rural areas, and varying quality of AI services across different socioeconomic segments. The global concentration of AI development in the United States and China creates additional dependency concerns, with much of China’s AI infrastructure built on US foundations, resulting in a remarkably small number of companies and individuals controlling the foundational technologies upon which global AI systems depend.

Resource consumption presents another inequality dimension, as AI systems require substantial energy, water, space, and environmental resources. This creates potential conflicts between AI development and environmental sustainability, with resource-intensive AI potentially exacerbating existing environmental inequalities.

Comprehensive Skilling and Workforce Transformation Strategies

Arun from the National Skill Development Corporation (NSDC) outlined extensive ongoing efforts to prepare India’s workforce for the AI era through innovative skilling approaches. NSDC has developed a four-pronged strategy addressing career trajectory guidance, AI-specific skilling programmes, AI-enhanced training delivery, and AI-powered programme monitoring and evaluation.

A sophisticated three-tier framework has emerged for AI education: “AI for all” focusing on basic awareness and usage skills, “AI for practitioners” addressing workplace integration and role transformation, and “AI for engineers” providing deep technical expertise. This framework recognises that different segments of the workforce require different levels of AI literacy and engagement.

Arun highlighted that NSDC works with 36 sector skill councils and 400 training partners, and noted India’s demographic dividend of adding “a million plus to workforce every year.” The organization has already seen significant uptake, with over 200,000 people registered for basic AI courses launched in July. The concept of stackable micro-credentials and nano-credentials represents an innovative response to the rapid obsolescence challenge in AI skills, allowing learners to continuously update their skills by adding new components as requirements evolve.

Practical implementation includes embedding AI modules into existing vocational training programmes, with every ITI student now receiving 7.5-hour AI awareness modules. The approach extends beyond theoretical knowledge to practical applications, such as teaching beauticians to use virtual try-on systems, helping tailors implement AI-powered design tools, and enabling workers to use AI for quality assessment, including determining “what’s a good weld or what’s a bad weld.”

Neena Pahuja from the National Council for Vocational Training (NCBT) emphasized the SWOT initiative and the importance of creating AI applications “made in our languages, made for our specific use.” She highlighted working with 10,000 students in Future Skills Centers and partnerships with major technology companies including Microsoft, Google, Amazon, Schneider, and Siemens.

Infrastructure Development and Technological Sovereignty

A technology company representative highlighted the critical importance of building comprehensive AI infrastructure within India rather than relying on external resources. This full-stack approach encompasses foundational compute infrastructure, connectivity systems, and last-mile application delivery. Significant investments are being made in AI data centres, including a major facility in Visakhapatnam, coupled with direct subsea cable connections to reduce dependency on routing through other countries.

The infrastructure discussion emphasised the importance of connecting solutions across entire value chains rather than creating isolated AI applications. In education, this means integrating AI across learning, administration, and workforce preparation. In agriculture, it involves connecting farmers from seed selection through market access, providing integrated information about weather patterns, planting schedules, harvest timing, market conditions, and financial support.

Access to affordable compute emerged as a critical success factor, with recognition that India’s AI ambitions require domestic computational resources that can serve the scale of India’s population and economic needs. This infrastructure development is coupled with efforts to create economic models for AI diffusion, ensuring that advanced AI capabilities can reach beyond well-funded enterprises to small businesses and individual users.

Innovative Approaches to AI Education and Adoption

The discussion revealed sophisticated thinking about how to make AI education accessible and effective for India’s diverse population. A key insight was the need to move from traditional “digital literacy” to “work literacy,” recognising that modern learners prefer consumable content delivered in short, focused segments rather than lengthy traditional courses.

This approach acknowledges changing attention spans and consumption patterns, with users increasingly accustomed to one- or two-minute content segments. Educational content must be designed for “anytime, anywhere, any media, any duration” consumption, removing friction from the learning process. The strategy involves creating engaging short-form content that can capture interest and then connecting interested learners to more comprehensive resources and hands-on facilities.

Arun mentioned the SID platform for large-scale program management, enabling NSDC to monitor and evaluate training programs across their extensive network. These programmes are embedded within credit-based systems, allowing students to build AI expertise progressively throughout their academic careers.

Addressing Workforce Transition and Human-AI Collaboration

A particularly forward-looking aspect of the discussion addressed the psychological and practical challenges of preparing workers for environments where they collaborate directly with AI systems and autonomous technologies. This goes beyond traditional retraining to address fundamental mindset shifts required when working alongside robotic systems, AI agents, and in automated manufacturing environments.

The discussion acknowledged that this transition will be challenging even for educated workers, requiring not just technical training but psychological preparation for new forms of human-machine collaboration. This includes understanding how to work effectively with AI assistants, how to maintain human oversight and control in automated environments, and how to adapt to rapidly changing role definitions as AI capabilities expand.

Rapid-Fire Vision for Decisive Action by 2030

The discussion concluded with rapid-fire responses from each speaker about the most decisive actions needed for India’s AI success by 2030:

Professor: Emphasized the critical importance of building trust infrastructure, focusing on making humans comfortable with AI “black boxes” and ensuring transparency in AI decision-making processes.

Arun (NSDC): Articulated the ambitious vision that “every Indian should have access to an AI assistant” within three years, leveraging India’s existing digital infrastructure and high trust levels.

Neena (NCBT): Stressed that “ethics and values should be part of every AI course taught in India,” emphasizing the need to create a generation of AI developers and users who prioritise responsible development and deployment.

Rakesh: Highlighted that “access to affordable compute is crucial” for India’s AI success, emphasizing the infrastructure foundation needed to support widespread AI adoption.

Technology Company Representative: Focused on the need for “economic models for AI diffusion,” ensuring that AI capabilities can reach beyond well-funded enterprises to small businesses and individual users, building on significant compute infrastructure investments.

Strategic Priorities and Path Forward

The discussion revealed a sophisticated understanding of both AI’s transformative potential and its inherent challenges. The consensus emerged around several strategic priorities: building trust infrastructure that makes AI systems transparent and accountable to users, ensuring ethical AI development through education and policy frameworks, creating comprehensive domestic AI infrastructure to reduce external dependencies, and developing innovative educational approaches that can keep pace with rapid technological change.

The conversation demonstrated that India’s approach to AI adoption is distinctly human-centred, prioritising purpose over power and focusing on practical applications that address real societal challenges. The recognition of AI as both an opportunity and a potential source of inequality has led to proactive strategies for inclusive development, leveraging India’s unique advantages in digital trust and demographic dividend while addressing the fundamental challenges of workforce transition and technological sovereignty.

The path forward requires coordinated action across government, industry, and educational institutions, with success measured not just by technological advancement but by AI’s contribution to reducing inequality and improving lives across all segments of Indian society. The discussion provided a roadmap for making AI serve as an equaliser rather than a divider, grounded in practical experience and realistic assessment of both opportunities and challenges ahead.

Session transcript

Speaker 1

In two significant areas, one is in agriculture, which is the highest employer, biggest employer anywhere. It’s also one of the least productive of sectors that we have anywhere. And that productivity gap in agriculture, if we can narrow it even by a small percentage, you will move the needle by a significant amount. And AI can do that. Just think about much of agricultural output in the global south comes from smallholder farmers who lose 40 % to 50 % of their crop because of pests. Now, if a farmer can identify what the pest is and use a homemade remedy that is given to them in their own language and using local ingredients, if I can move that 40 % down to 30 % or 20%, suddenly a huge swing in the farmer’s income.

So there is no question from a human perspective, if my income is going to, if my crop loss is going to, go up by 10 or 20%, you know, I will adopt it. So. So that’s the first thing, which is purpose. Now, in addition to agriculture, small businesses. I don’t really need a whole bunch of employees if I can essentially harness AI to do market research, to do analysis, and almost be an employee. And I can be a one -person shop and employ and really build a business. Now, beyond that, there are several other areas of application, which, you know, we’ve done the analysis to kind of see, you know, where are some of the biggest opportunities.

So there’s agriculture, small business. After that comes education and skill building. Another very powerful use of AI. And a fourth area is health care. Now, for each of these areas, there is an element of a major chasm that the humans need to cross. And that chasm doesn’t have to do with technology. It doesn’t have to do with how big the pipe is. It doesn’t have to do with whether I have access to, you know, any of the devices. It doesn’t have to do with the various elements of the digital public infrastructure. In fact, India is one of the shining examples. of the distribution system, the rails having been laid. But the key chasm, the big jump that we need to make is across a trust gap, which is in addition to digital infrastructure, in addition to other forms of infrastructure that includes talent and data and compute, there is a trust infrastructure that needs to be built.

Because from a human perspective, I will use a piece of technology if I can trust it. Now, there are many reasons why people are, on the one hand, very excited about AI, as is very evident over here, and at the same time, there is a lingering concern. There is a lingering concern because I don’t quite understand what’s inside that black box. I don’t quite understand how the hiring algorithms work. Why did I get rejected from this job? Why did I get that diagnosis? From a healthcare system. What is the language system telling me? Is something being lost in translation? Can I trust an image that has just been sent to me on social media? So the issues of trust are a very important set of questions.

And then the data that I’m submitting into the system, simply by interacting with AI, I’m submitting data and providing input. I’m actually acting as labor for the AI industry. What’s happening to the data? Who’s using it? Where does it go? Can it be used against me? Is it going to be used in my favor? So the whole question of trust is going to be an enormously important part that we need to consider. So first, purpose. Second is creating a trust infrastructure. And the third is recognizing that no matter what we say, no matter what rhetoric we put on our screens, no matter how many alliterative slogans we have in our meetings, AI is going to be a force for inequality.

There are many reasons why AI is going to create an unequal playing field, not the least of which being the fact that the algorithms are feeding on data. Data is simply a reflection of the past, and as we know, the past is not a terribly equal place. So that algorithm is going to essentially act as a mirror to our past, and maybe part of the risk is that the inequalities of the past get reinforced into the future. There are inequalities in terms of who has access to better tools. Now, even with open source and people being able to, you know, vibe code themselves, there’s an element of democratization, but there could be very different levels of access across a society.

So the usage context itself could be unequal. There could be inequalities when you go into different parts of the world, when you go to different parts of the country. So geographically, there is likely to be inequality. There’s inequality in terms of who’s providing you AI. So today, much of the frontier AI models are coming from two places, United States and China. And much of China’s AI infrastructure is built on top of a foundation from the United States. Much of the foundation of the United States, the leaders of the companies that are producing it, they’re all over here, really small. So it’s a tiny industry that’s providing us the foundation from which we are building the rest of the system.

And then one last really major source of potential inequality has to do with the resources that AI is absorbing, primarily energy, water, space, and even kind of our environmental resources, enormously important. Now, none of this means that we should stop the train. But we need to understand the human impact. that AI is going to have, both positive and negative, as we move forward and put the relevant policy systems in place, the relevant trust -building systems in place. Otherwise, we might be not only wasting a demographic dividend that India has got, but a trust dividend that India has got. One critical and really important aspect of an ecosystem like India is that it’s a very trusting society, very trusting in terms of digital.

It’s a very trusting infrastructure. Trust levels in India is in the 70 % range, whereas in the United States is in the 25 % to 30 % range. That’s a huge platform to build on. And it’s going to be really important for us to follow through with that trust that users, our potential consumers are giving us, and for the policy and the technology sector to be able to make sure that that trust dividend is not wasted. So with that… But I’m going to sit down, and I look forward to learning from my colleagues on the panel about how do we make AI more purposeful and not just powerful. Thank you.

Moderator

Thank you, Professor, for the insightful remarks. Very exciting, and at the same time, you know, you raise some concerns around inequality. Let me first go to Arunji. You know, as we said, like the demographic dividend that India holds, we will be adding a million plus to workforce every year. How do we make sure that they’re skilled, they’re ready for what the market is asking, the skills are continuously shifting, as CEO of NSDC with the mandate of skilling the population? How do you look at this? How is, you know, do you see AI as a threat, as an enabler? How are you approaching this?

Speaker 2

So, good morning. AI is an opportunity and an enabler. So let me begin with a few words about NSDC itself. So this is a national platform institution under the Ministry of Scale Development. And we work through two arms, 36 sector scale councils and close to around 400 training partners. So these are the two arms through which we have been working in the scaling space for the last two decades. With AI coming in, of course, it’s an opportunity, as I say. But primarily in four areas we have started work. One is, of course, AI and how career trajectories are getting shaped. So we require some kind of guidance, direction, et cetera. So work on that front.

Second is creating scaling programs for AI, AI scaling programs. Third is how does AI itself affect the entire value chain of scaling when it comes to, say, training, assessments, counseling and the other. areas. And the last is since we do large scale program management, how do we use AI to evaluate or monitor outcomes? These are the four primary areas we are working on. Obviously I will just pick for each of these areas in brief. The first one, setting the agenda or setting the direction with respect to careers, NSDC and the sector skill council, specifically the IT sector skill council, we have brought about certain reports, how jobs get shaped by AI, the new jobs and how the existing jobs get changed, etc.

Within that is career counselling. Once you know that this is the way a certain job would get transformed or a new job would come, a lot of career counselling is required for students. So how do we create AI enabled career counselling tools, models, etc. So that’s one area of big work. work. Coming to AI skilling programs, clearly there are three trials. The first is of course where we talk about AI for all skilling, which is more like AI awareness and AI usage. So we have this skilling for SOAR program under which we work with schools etc. The second is where we talk about how does skilling affect practitioners or people in the workplace. And this is where our sector skills councils are busy putting together how do we make the current programs, how do we bring in AI modules in it.

Of course to begin with how AI affects job roles to start with and then translating that into how the new programs would look like. The third area is AI for engineers where we skill engineers and this is where we work with engineering colleges. We have something called the Future Skills Centers and we work with close to right now around 10 ,000 students and around 50 ,000 students. And we work with 10 ,000 students and around 50 ,000 students. So we have a lot of different things going on. We have a lot of different things going on. We have a lot of different things going on. We have a lot of different things going on. We have a lot of different things going on.

We have a lot of different things going on. We have a lot of different things going is to create close to around 22 companies work with us, including Microsoft, Google, and Amazon, and Schneider’s, and Siemens, et cetera. And we create these kind of skilling centers within engineering colleges. The good thing about it is that this is part of the credit -based system. So students can pick up over the four years they are doing the engineering every year, every semester they pick up a course, and you string together a course, then you have a kind of a program for, say, an AI architect or something like that. So we look at the entire skilling program. The third is, as I said, AI is changing the way we skill, you know, the way we train, the way we assess.

Early days, again, pilots on, how do you use AI as a training assistant, you know, to our trainers, you know? So what do we do, how do we work with that? Similarly, assessment is a big area. Please see, many of our training involves vocational training, which means hands -on training. So we use AI for hands -on training. Hands -on training requires a lot of… piloting etc so towards that we are working can we say for example just giving an example say what’s a good weld or what’s a bad weld if the AI is trained on that then it can help the current assessor in actually you know actually providing a better assessment and also augmenting the number of assessors we are currently having the last piece is we have our skill platform for large scale program management today it is called SID and we are now bringing elements AI into it so that how do we how do we monitor outcomes better so big challenges in a country like India is monitoring outcomes they are facing how to use AI on that area also so

Moderator

very interesting and exciting to see what you have brought to the table Neenaji if I can move to you Anandji spoke about the skilling programs but certification standards are very key and how do you do that in an environment where skills are you know the courses are becoming outdated in months the requirements are shifting from your vantage point like a lot of content being created a lot of initiatives all around how do we define qualified professional in AI? Is there a plan for certification or standard setting? How should we think about it?

Neena Pahuja

Thank you so much. Thank you for inviting me. I’m a former executive member of NCBT. One minute about NCBT. NCBT is a regulatory body NCBT is a regulatory body under the Ministry of Skill Development. So something on AI since we’re sitting in an AI conference. Around two and a half years back we came up with a skilling framework for AI. And the framework actually talks about three layers of skilling for AI. It talks about skilling for all. It talks skilling for few and skilling for many and skilling for few. all of the initiative we started working as part of the SWOT initiative that was mentioned by Arun also. Now what does that mean? Like all of us know how to use payment gateways or payment UPI etc.

Can we actually use AI in a similar way? So our thought was can we take AI to everyone every nook and corner a radiowala or a plumber or somebody who is a beautician etc. So what did we do on that? And I’m going to take a minute before I come to a certification question. We actually have tried creating a small nano -credential for something which a beautician can use and how can she use AI for giving a better service to the customers. We’ve actually created a virtual try -on for a tailor. How can a tailor use a virtual try -on concept for actually taking it to, you know, telling a person which design or what kind of a color suits a person.

We actually created basic courses, of course, on AI, which also have been done and they were launched sometime in July. We’ve got around two lakhs plus people who registered on those courses. But idea was, how can we take it to everyone? So how can simple things like, how can a plumber find out if there’s a fault in the pipe? So can AI be used there? So one of the points which I think Professor talked about was, how do we take it to masses? How can AI make an impact in our lives or everybody’s life, like internet is doing or anybody else is doing? So that’s what we’ve tried doing as part of some of the courses that we already are in a state to launch.

In fact, some of the courses have been launched. We all been talking about in this conference that AI is going to replace coders. So there’s going to be lots of jobs, etc. But still, how can actually you use AI for helping to launch? We actually are stalling. we’ve actually demonstrated how AI can help in coding also. How can it help me to learn coding? How can it help me to test a particular program? So AI doesn’t stop at just being AI and taking away jobs. I think we have to groom and possibly diffuse, I mean, the word which has been said, the concept of AI which is happening. Now let’s look at certification in the courses.

Very wonderful question he asked. Things are changing almost every day. I think the carpenter’s role is going to change. In fact, we have from Furniture and Putting, the Sixth Grade Council, a small little model which says, how can I design a particular furniture better if I have AI? Knowing the wood, amount of wood, and the space for which I have to design the furniture, can the carpenter actually use AI to design the furniture better? That’s the way it’s going to make an impact. Now how can I embed this course in a course which I’m teaching a carpenter? That’s the impact it’s going to make. And that’s what we’re trying to do. So a wonderful question from that point of view.

So what we’ve done is we came up with the concept of stackable micro -credentials or stackable nano -credentials. Which means based on the changes which are happening, you could actually stack the small, small modules together and make a skill that is required that needs to be done. And in these skills, you could also have something like employability skill, which could be design thinking or others. And it could also be an AI module which can be embedded, which actually tells you how AI can be embedded in a particular course. For example, our ITIs already have a small seven and a half hours module which have been embedded as basic concepts of AI which are now being taught to every ITI student.

Now what we want to see is how can we actually do our late machines or other machines, how can they be done in a better way with AI. So that’s why we are trying to do it. Now certification is now, the way we are trying to do is small, small certificates. You know, which a person earns, can I. actually lead to credit and the total of that actually can also take you to a larger credit and that’s how it’s been planned as part of something called National Credit Framework that we actually came up with from NCVT and the Ministry of Education. This was launched around two years back. So that’s how it’s been planned. I hope that answers your question.

Moderator

To Rakesh, historically, you know, whenever general purpose huge technological change comes in, it ends up increasing divide for some time till people evolve and you know, you learn new skills and get off the curve. Now in India’s vantage point, we spoke about the starting point we have. In your view, what needs to be done differently? We heard a few initiatives in motion to make sure that we cross this transition and manage this transition very carefully and aptly.

Rakesh Kaul

Thank you so much. It is a very, very pertinent question. And I truly believe that in the past, we have been able to do this. And I truly believe that in the past, we have been able to do this. And I truly believe that in the past, we have been able to do this. era, India was at a disadvantage. It was very difficult when we had internet coming in. India was not as uniquely placed as it is placed today. We have ubiquitous connectivity. We have low cost of connectivity. We have a huge amount of internet penetration. We have applications which are general purpose applications used by a billion people like UPI and others. So today our starting point is very, very different, not only for our users, but also for those who are making these applications.

I think journey started about 10 years back when India realized that we have to make applications for our own people and not just rely on the world to make applications for us and take them forward. So that’s where we are today. Hence, the opportunity for us is immense, given that we are a billion people with access to low cost connectivity, reliable connectivity, already using these applications for our financial transactions and other use cases. And especially for all the programs that we just heard, NSGC and others are today making. So I’ll just talk about three things that I thought I’ll bring to your notice is, I think the point that we should move from digital literacy to work literacy.

What that typically, the point that I’m wanting to make here is that, just a minute, it’s, my phone is misbehaving. So the idea here is how can we remove friction to learning? And friction to learning, typically, the point that I wanted to make is how can it be anytime, anywhere, any media, any duration. And more and more, we are seeing that our population, our people are used to one minute, two minute consumable content. Giving a one hour lecture or one hour content may be difficult now. People really need to consume it in two minutes. If they like, they might go ahead with the two hours. content. So are we creating the right content for our users or are we just trying to reshape the content we have a lecture somewhere and we give a video on a platform and say consume it.

So this whole content strategy has to work if the skill is to be really imparted for people in a consumable manner where the as I said friction to usage is least and obviously if we dovetail the programs that sir was just talking about into those 600 labs that digital AI mission is going to set up across India then somebody who’s interested and you hook on by this small meaningful content on an Instagram can the person really find it interesting and get to somewhere where they can really see the benefit of it and we heard Nandan talking about it is that we should lead from the first principles it is not only giving black boxes to people and saying work with it if you really want India to progress people should understand also a lot of people should understand what goes into this.

Black box so that they can start innovating around so I think that’s one which is remove friction. the second is I think we are all talking about agents and we are talking about physical AI it is not going to be easy for any worker including trust me for me if tomorrow I am told that my secretary is an agent and not a physical person although the productivity of that agent may be much better now imagine we are getting into a workforce where we are talking of lights out factories it’s a reality in China where the factories are totally autonomous how do you get your workforce to work with this physical AI you are working and besides you there is a robotic arm working doing half the work how do you work in such environments where a lot of work is being done by physical AI it will take a lot of mindset shift it will take a lot of role shifts and it is not only telling them what AI is how their roles are changing what is now expected of them once that is clear then only you will be able to train them better so therefore how do we move towards this journey and in our mind be very optimistic of the fact that it is here and it will impact us.

We have to be ready. There is enough and more for a country like ours to become the torchbearers for the world of how to engage 1 billion people on this endeavor. We will create data which will train apps but for that we will have to take on the mantle of creating our own apps which are purposefully made for India, made in our languages, made for our specific use. Thank you.

Moderator

is going to add to the broader AI ecosystem that we need?

Speaker 3

So when I look at the work that we’ve been doing across board and across product areas, and speaking to some of the announcements we’ve made this week, what we’re looking at is how do we bring the full stack of AI into India, right from the foundational level. It’s creating a secure, resilient infrastructure. How do we bring that computational power that India needs in India and not having to rely on that power, that compute in other markets? And that’s why we started out with the build of the data center, the AI data center in Vizag. Adding to that is how do we then ensure connectivity with the rest of the world? That’s where we’ve looked at the subsea cable investments that we’re going to do, which is going to connect Vizag to, to the, to, uh, the U.S. directly, you know, circumventing through the southern hemisphere.

And then as you go up the stack, like you’re talking about, is how do we build out applications and solutions that are actually delivering value to the last mile citizen on the ground, whether it’s in agriculture or health. But every time we look at it, we are looking at how do we kind of, you know, complete the circle. So if you look at it in the education space and the work we’ve been doing with Charn Singh University, is how do you have AI not just at the skilling level, but how do you bring in AI at the learning level and the administration level so we can create a more effective and efficient way of actually delivering AI to the students.

And we’re addressing every part of that chain of learning. How then do you connect it to the workforce, correct? So as you look at the professional certification. And it’s important that these loops start to close because that’s when you actually see impact. That’s key. Similarly, as we talked about in agriculture healthcare, correct? How, in agriculture, you go from seed to market. How do you give information to a farmer to understand the weather pattern so he’s better able to know when to sow, when to harvest, information on the market, information on financial support. So the whole aspect that we are thinking through is how do we kind of help connect the dots? How do we come in and provide our support and our technology to ISVs to create these solutions to connect the dots?

Because that last mile connectivity is actually going to determine the success of this technology. Thank you.

Moderator

I think we’re quite at time, but I want to take just a minute for a last question to all the panelists together. If you had to identify one decisive action which India should take looking at 2030, something that we can be proud of, what action should India take to make AI as an equalizer? Let me start with you, Professor. five second response is rapid fire.

Speaker 1

Five second response, I think the one action that we need to take is improve the trust infrastructure and make sure that the human at the other side of the AI understand what’s inside the black box, at least to the extent that it makes them feel comfortable accepting the output and the decision that the black box is offering.

Speaker 2

I think in the next three years I think every Indian should have access to an AI assistant whether it’s a farmer, a student or anything. We have the platforms, we have the DPIs in place, we have the language thing in place, we also have the SIDs, the skills platform in place. It’s time we put it all together and create every Indian having one assistant working with them.

Neena Pahuja

I think what I would love is that if ethics and value is also part of every AI course that’s taught, at least in India, we will possibly create a different kind of AI creators in that field. That would be my opinion. Thank you.

Rakesh Kaul

I would believe that it is access to affordable compute which will be important for India to succeed.

Speaker 3

I think I’m going back to my first point is on the flywheel. I think a lot of the investments are coming into the compute side. How do we look at bringing in investments and creating economic models for the diffusion of AI and that’s going to be important.

Moderator

Trust, ethics, compute, human at the core of everything. Thank you so much for the exciting discussion. Thank you. Can I have a round of applause for the panelists and our moderator please.

Speaker 1

Speech speed

165 words per minute

Speech length

Trust and ethical challenges in AI adoption

Topics

Human rights and the ethical dimensions of the information society | Artificial intelligence

Speaker 2

Speech speed

185 words per minute

Speech length

923 words

Speech time

299 seconds

NSDC four‑pronged AI skilling initiative

Explanation

Speaker 2 outlines NSDC’s comprehensive AI programme that includes career‑path guidance, scaling programmes, hands‑on training and outcome monitoring to embed AI across the skill ecosystem.

Evidence

“The first one, setting the agenda or setting the direction with respect to careers, NSDC and the sector skill council, specifically the IT sector skill council, we have brought about certain reports, how jobs get shaped by AI, the new jobs and how the existing jobs get changed, etc.” [48]. “Second is creating scaling programs for AI, AI scaling programs.” [15]. “And the last is since we do large scale program management, how do we use AI to evaluate or monitor outcomes?” [12]. “So we use AI for hands -on training.” [6].

Major discussion point

Skilling the workforce for an AI‑enabled economy

Topics

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

Deploy personal AI assistant for every Indian

Explanation

Speaker 2 proposes that within three years every citizen—farmer, student or otherwise—should have access to a personal AI assistant to democratise AI benefits.

Evidence

“I think in the next three years I think every Indian should have access to an AI assistant whether it’s a farmer, a student or anything.” [81].

Major discussion point

Strategic actions for India by 2030

Topics

Artificial intelligence | The digital economy | Social and economic development

Neena Pahuja

Speech speed

173 words per minute

Speech length

930 words

Speech time

322 seconds

Stackable nano/micro‑credentials and AI modules

Explanation

Neena describes the creation of stackable nano‑ and micro‑credentials, basic AI courses and embedded AI modules in ITI curricula to build modular, up‑datable skill pathways.

Evidence

“So what we’ve done is we came up with the concept of stackable micro -credentials or stackable nano -credentials.” [58]. “We actually created basic courses, of course, on AI, which also have been done and they were launched sometime in July.” [59]. “For example, our ITIs already have a small seven and a half hours module which have been embedded as basic concepts of AI which are now being taught to every ITI student.” [60]. “Which means based on the changes which are happening, you could actually stack the small, small modules together and make a skill that is required that needs to be done.” [63].

Major discussion point

Skilling the workforce for an AI‑enabled economy

Topics

Capacity development | Artificial intelligence

Embed ethics and values into AI education

Explanation

Neena advocates that ethics and values be integral to every AI course so that future AI creators are guided by responsible principles.

Evidence

“I think what I would love is that if ethics and value is also part of every AI course that’s taught, at least in India, we will possibly create a different kind of AI creators in that field.” [79].

Major discussion point

Strategic actions for India by 2030

Topics

Human rights and the ethical dimensions of the information society | Artificial intelligence | Capacity development

Rakesh Kaul

Speech speed

182 words per minute

Speech length

878 words

Speech time

288 seconds

Emphasise affordable compute for India

Explanation

Rakesh stresses that affordable compute resources are essential for India’s AI success, underpinning both skill development and deployment.

Evidence

“I would believe that it is access to affordable compute which will be important for India to succeed.” [66].

Major discussion point

Skilling the workforce for an AI‑enabled economy

Topics

Artificial intelligence | Closing all digital divides | The enabling environment for digital development

Guarantee affordable domestic compute capacity

Explanation

He reiterates that ensuring low‑cost, locally available compute infrastructure is a strategic priority for scaling AI across the country.

Evidence

“I would believe that it is access to affordable compute which will be important for India to succeed.” [66].

Major discussion point

Strategic actions for India by 2030

Topics

Artificial intelligence | Closing all digital divides | The enabling environment for digital development

Speaker 3

Speech speed

164 words per minute

Speech length

480 words

Speech time

175 seconds

Build domestic AI data centre in Vizag and subsea cable

Explanation

Speaker 3 outlines the construction of a home‑grown AI data centre in Vizag, coupled with subsea cable investments to secure compute and connectivity independent of foreign infrastructure.

Evidence

“And that’s why we started out with the build of the data center, the AI data center in Vizag.” [72]. “That’s where we’ve looked at the subsea cable investments that we’re going to do, which is going to connect Vizag to, to the, to, uh, the U.S. directly, you know, circumventing through the southern hemisphere.” [73]. “How do we bring that computational power that India needs in India and not having to rely on that power, that compute in other markets?” [74].

Major discussion point

Infrastructure and ecosystem development

Topics

Artificial intelligence | The enabling environment for digital development

Foster investment models for AI flywheel

Explanation

Speaker 3 calls for investment and economic models that create a self‑reinforcing AI flywheel across compute, applications and services, driving sustained growth.

Evidence

“How do we look at bringing in investments and creating economic models for the diffusion of AI and that’s going to be important.” [67]. “I think I’m going back to my first point is on the flywheel.” [89].

Major discussion point

Strategic actions for India by 2030

Topics

Financial mechanisms | Artificial intelligence | The enabling environment for digital development

Moderator

Speech speed

154 words per minute

Speech length

399 words

Speech time

154 seconds

Highlight trust, ethics, compute and human‑centric AI

Explanation

The Moderator succinctly frames the discussion around the need for trust, ethical considerations, affordable compute and keeping humans at the centre of AI development.

Evidence

“Trust, ethics, compute, human at the core of everything.” [37].

Major discussion point

Overall thematic framing

Topics

Human rights and the ethical dimensions of the information society | Artificial intelligence | Closing all digital divides

Agreements

Artificial intelligence | Capacity development

Similar viewpoints

All three speakers emphasize the importance of comprehensive, multi-layered approaches to AI implementation that address different skill levels and use cases

Speakers

– Speaker 1
– Speaker 2
– Neena Pahuja

Arguments

Agriculture productivity can be significantly improved through AI pest identification and local language solutions

NSDC is working on four areas: career guidance, AI-enabled training, and program monitoring

Three-tier AI skilling framework: AI for all, AI for practitioners, and AI for engineers

Topics

Artificial intelligence | Capacity development | Social and economic development

Both speakers advocate for India-specific AI solutions that are designed for local needs and contexts rather than adapting global solutions

Speakers

– Rakesh Kaul
– Speaker 3

Arguments

India must create applications specifically designed for Indian users in local languages

Full-stack AI infrastructure needed from foundational compute to last-mile applications

Topics

Artificial intelligence | The enabling environment for digital development

All three speakers acknowledge that AI implementation will create challenges and disruptions that require proactive management and investment

Speakers

– Speaker 1
– Rakesh Kaul
– Speaker 3

Arguments

AI will inevitably create inequality due to algorithms reflecting biased historical data

Workforce must adapt to working alongside physical AI and autonomous systems

Economic models for AI diffusion need investment alongside compute infrastructure

Topics

Artificial intelligence | Closing all digital divides | The digital economy

Unexpected consensus

Arguments

Trust gap is the major barrier to AI adoption, not technology or infrastructure limitations

Access to affordable compute is essential for India’s AI success

Summary

Speaker 1 emphasizes trust infrastructure as the key barrier, while Rakesh Kaul identifies affordable compute access as the critical requirement for AI success

Topics

Artificial intelligence | Building confidence and security in the use of ICTs | The enabling environment for digital development

Approach to AI transparency and user understanding

Speakers

– Speaker 1
– Speaker 2

Arguments

Users need to understand AI black boxes to feel comfortable with AI decisions and outputs

Every Indian should have access to an AI assistant within three years

Summary

Speaker 1 emphasizes the need for users to understand how AI works before adoption, while Speaker 2 focuses on rapid deployment of AI assistants without emphasizing user understanding of the technology

Topics

Artificial intelligence | Human rights and the ethical dimensions of the information society | Closing all digital divides

Unexpected differences

Resource allocation priorities for AI development

Speakers

– Speaker 1
– Speaker 3

Arguments

AI consumes significant resources including energy, water, and environmental resources

Economic models for AI diffusion need investment alongside compute infrastructure

Explanation

While Speaker 1 raises concerns about AI’s resource consumption as a source of inequality, Speaker 3 advocates for increased investment in AI infrastructure and diffusion models, creating an unexpected tension between resource conservation and expansion

Topics

Artificial intelligence | Environmental impacts | Financial mechanisms

Overall assessment

Summary

The main areas of disagreement center around implementation priorities (trust vs. compute access), the pace and approach to AI deployment (understanding vs. rapid access), and resource allocation strategies

Disagreement level

Moderate disagreement level with significant implications – while all speakers support AI adoption in India, their different priorities could lead to conflicting policy recommendations and resource allocation decisions that may impact the effectiveness and equity of AI implementation

Partial agreements

Both speakers recognize the importance of addressing ethical concerns in AI, but Speaker 1 focuses on the inevitability of inequality while Neena Pahuja proposes proactive ethics integration in education as a solution

Speakers

– Speaker 1
– Neena Pahuja

Arguments

AI will inevitably create inequality due to algorithms reflecting biased historical data

Ethics and values should be integrated into every AI course taught in India

Topics

Artificial intelligence | Human rights and the ethical dimensions of the information society

All speakers agree on democratizing AI access for all Indians, but differ on implementation – Speaker 2 focuses on AI assistants, Neena Pahuja on practical work applications, and Rakesh Kaul on localized application development

Speakers

– Speaker 2
– Neena Pahuja
– Rakesh Kaul

Arguments

Every Indian should have access to an AI assistant within three years

AI should be made accessible to all workers including radiowala, plumber, beautician through simple applications

India must create applications specifically designed for Indian users in local languages

Topics

Artificial intelligence | Closing all digital divides | Capacity development

Both speakers acknowledge India’s infrastructure advantages, but Rakesh Kaul emphasizes existing digital infrastructure while Speaker 3 focuses on building comprehensive new AI-specific infrastructure

Speakers

– Rakesh Kaul
– Speaker 3

Arguments

India’s existing digital infrastructure provides a strong foundation for AI adoption

Full-stack AI infrastructure needed from foundational compute to last-mile applications

Topics

Artificial intelligence | The enabling environment for digital development | Information and communication technologies for development

Similar viewpoints

All three speakers emphasize the importance of comprehensive, multi-layered approaches to AI implementation that address different skill levels and use cases

Speakers

– Speaker 1
– Speaker 2
– Neena Pahuja

Arguments

Agriculture productivity can be significantly improved through AI pest identification and local language solutions

NSDC is working on four areas: career guidance, AI-enabled training, and program monitoring

Three-tier AI skilling framework: AI for all, AI for practitioners, and AI for engineers

Topics

Artificial intelligence | Capacity development | Social and economic development

Both speakers advocate for India-specific AI solutions that are designed for local needs and contexts rather than adapting global solutions

Speakers

– Rakesh Kaul
– Speaker 3

Arguments

India must create applications specifically designed for Indian users in local languages

Full-stack AI infrastructure needed from foundational compute to last-mile applications

Topics

Artificial intelligence | The enabling environment for digital development

All three speakers acknowledge that AI implementation will create challenges and disruptions that require proactive management and investment

Speakers

– Speaker 1
– Rakesh Kaul
– Speaker 3

Arguments

AI will inevitably create inequality due to algorithms reflecting biased historical data

Workforce must adapt to working alongside physical AI and autonomous systems

Economic models for AI diffusion need investment alongside compute infrastructure

Topics

Artificial intelligence | Closing all digital divides | The digital economy

Takeaways

Key takeaways

AI has transformative potential in four key sectors: agriculture (highest impact through pest identification reducing crop losses), small businesses (enabling one-person operations), education/skill building, and healthcare

Trust infrastructure is the critical barrier to AI adoption in India, not technology or digital infrastructure – users need to understand AI ‘black boxes’ to feel comfortable with AI decisions

India has a significant advantage with 70% digital trust levels compared to 25-30% in the US, creating a ‘trust dividend’ that should not be wasted

AI will inevitably create inequality due to biased historical data, unequal access to tools, geographic disparities, and resource concentration in US/China

A three-tier AI skilling framework is needed: AI for all (basic awareness), AI for practitioners (workplace integration), and AI for engineers (technical expertise)

Stackable micro-credentials and nano-credentials can address rapidly changing skill requirements in the AI era

India needs full-stack AI infrastructure built domestically, from foundational compute to last-mile applications, rather than relying on external resources

Ethics and values should be integrated into every AI course taught in India to create responsible AI creators

Resolutions and action items

NSDC to continue work on four areas: AI career guidance, AI skilling programs, AI-enabled training systems, and AI-powered program monitoring

Launch and scale AI awareness courses (already 200,000+ registered for basic AI courses launched in July)

Embed 7.5-hour AI modules in all ITI programs across India

Establish 600 AI labs across India through the digital AI mission

Build AI data center in Vizag with direct subsea cable connectivity to the US

Create AI assistants accessible to every Indian within three years

Develop AI applications in local languages for Indian-specific use cases

Integrate AI across the entire education value chain from learning to administration to workforce connection

Unresolved issues

How to effectively build trust infrastructure and make AI ‘black boxes’ transparent to users

How to prevent AI from reinforcing historical inequalities and biases

How to ensure equitable access to AI tools across different geographic regions and socioeconomic groups

How to manage workforce transition to working alongside physical AI and autonomous systems

How to create sustainable economic models for AI diffusion beyond just compute infrastructure investment

How to balance AI development speed with ethical considerations and trust-building

How to reduce dependency on US and China for foundational AI models and create indigenous alternatives

Suggested compromises

Focus on purpose-driven AI applications rather than just powerful AI systems

Balance AI automation with human understanding and control

Create stackable, modular learning systems that can adapt to changing requirements rather than fixed long-term programs

Develop AI applications that augment human capabilities rather than replace workers entirely

This comment synthesizes the discussion into a concrete, ambitious vision that leverages India’s existing digital infrastructure. It transforms abstract concepts into a tangible goal that builds on India’s proven digital public infrastructure success.

Impact

This vision statement provided a unifying goal that other panelists could rally around in their final responses, creating convergence in the discussion toward a shared aspiration for India’s AI future.

Overall assessment

Explanation

Neena suggested this would create a different kind of AI creators and developers, emphasizing the importance of ethical AI development

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.