Panel Discussion AI in Digital Public Infrastructure (DPI) India AI Impact Summit

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

Session at a glance

Summary

This discussion focused on the intersection of Artificial Intelligence (AI) and Digital Public Infrastructure (DPI), examining how governments and organizations can leverage these technologies for inclusive development while maintaining appropriate safeguards. The panel, moderated by C.V. Madhukar from CoDevelop, included representatives from government, international development organizations, and the private sector, discussing India’s leadership in DPI and its potential application to AI implementation.

Dr. Hans Wijayasuriya emphasized that governments must prioritize inclusion, integrity, and safeguards when implementing AI systems, noting that DPI foundations must be mature before AI can be effectively layered on top. He stressed that AI should not redefine DPI but rather serve as scaffolding to accelerate service delivery while maintaining sovereignty through technological neutrality. Robert Opp from UNDP highlighted the importance of embedding safeguards from the beginning of any AI-DPI implementation, emphasizing that efficiency alone should not be the driving metric if it leads to exclusion of vulnerable populations.

Sangbu Kim from the World Bank discussed how DPI can prevent siloed approaches in the AI era, noting that the evolution from supplier-centric to user-centric computing makes DPI more relevant than ever. Saibal Chakraborty from Boston Consulting Group drew parallels between India’s DPI journey and its emerging AI strategy, explaining how AI is being treated as shared public infrastructure similar to how DPI was developed, with initiatives like providing affordable GPU access to startups.

The panelists agreed that the next three to five years will be critical for establishing the right balance between innovation and safeguards, with particular emphasis on making AI accessible to underserved populations through voice-first capabilities and multilingual interfaces.

Keypoints

Major Discussion Points:

– Government Implementation of AI with DPI: Focus on three key pillars – inclusion (ensuring AI doesn’t increase divides and supports voice-first, multilingual capabilities), integrity (building AI on mature DPI foundations with clean data and reliable APIs), and sovereignty (maintaining national control over technology while enabling innovation)

– Safeguards and Responsible AI Development: Emphasis on implementing safeguards frameworks from the beginning of AI projects, including bias detection, explainability, human-in-the-loop systems, and ensuring that efficiency doesn’t override inclusion as the primary metric

– India’s AI Infrastructure Model: Discussion of India’s approach to treating AI as shared public infrastructure, similar to their DPI success, including providing affordable GPU access (under $1/hour), government data access, and targeted funding for socially sensitive sectors beyond fintech

– Scaling AI for Development: Exploration of how AI can accelerate sustainable development goals through population-scale solutions, with initiatives like UNDP’s “100 Pathways” project to identify scalable AI use cases and the World Bank’s shift toward demand-driven, user-centric approaches

– Future Policy and Innovation Ecosystem: Discussion of the delicate balance governments must strike between data sovereignty and enabling innovation, the need for accountable institutions at both national and state levels, and creating controlled access to government data for AI training

Overall Purpose:

The discussion aimed to explore how AI can be integrated with Digital Public Infrastructure (DPI) to accelerate development outcomes, particularly focusing on lessons from India’s DPI success and how they can be applied to responsible AI implementation globally.

Overall Tone:

The tone was consistently optimistic and forward-looking throughout the conversation. Speakers expressed excitement about AI’s potential while maintaining a pragmatic focus on safeguards and responsible implementation. There was notable pride in India’s DPI achievements and confidence that similar success could be replicated in the AI space. The discussion maintained a collaborative, solution-oriented atmosphere with speakers building on each other’s points rather than presenting conflicting viewpoints.

Speakers

– Speaker 1: Role/Title not specified, appears to be an event organizer or host

– Saibal Chakraborty: Managing Director and Senior Partner, Boston Consulting Group

– Sangbu Kim: Vice President for Digital, World Bank

– C.V. Madhukar: Chief Executive Officer of CoDevelop, serving as the moderator for this session

– Robert Opp: Representative from UNDP (United Nations Development Programme), working on safeguards, Global Digital Compact, and sustainable development goals

– Dr. Hans Wijayasuriya: Government representative (appears to be from Sri Lanka based on context), dealing with national government policy on AI and DPI

Additional speakers:

– Arjun: Mentioned as having introduced the panelists, likely another event organizer or host

Full session report

This panel discussion at the India AI Impact Summit examined the intersection of Artificial Intelligence (AI) and Digital Public Infrastructure (DPI), bringing together perspectives from government, international development organisations, multilateral banks, and the private sector. The conversation, moderated by C.V. Madhukar from CoDevelop, explored how nations can harness AI’s transformative potential while maintaining robust safeguards and ensuring inclusive development outcomes.

Madhukar opened by highlighting India’s unique positioning in the global AI landscape: “India is saying, look, there will be a Chinese model, there will be an American model, there will be a whole bunch of other innovations that are going on. What do we do to embrace and use all of this for our benefit?”

Government Implementation Framework: The Three Pillars Approach

Dr. Hans Wijayasuriya, representing the Sri Lankan government perspective, articulated a framework for government AI implementation centered on three fundamental pillars: inclusion, integrity, and sovereignty. His approach to inclusion emphasized that government-introduced technologies must actively reduce rather than exacerbate existing divides, highlighting AI’s capabilities in enabling voice-first interactions and real-time translation to expand access for previously excluded populations.

On integrity, Dr. Wijayasuriya presented a crucial insight: “AI will not redefine DPI. AI would, or at least where we look at it from now, maybe I’ll be wrong in six months from today, but the DPI foundations must be in place first.” This positions AI as scaffolding rather than replacement for existing infrastructure, requiring mature data architectures, clean registers, and reliable APIs before effective AI deployment.

His discussion of sovereignty defined it not as technological isolation but as maintaining national capability and control over critical building blocks, including vendor neutrality and cloud independence. He noted the particular challenges for smaller nations: “being small, you’re on the wrong side of the AI divide unless you’re economically in a very powerful position,” citing Singapore as “an outlier, a small country with a lot of economic power.”

Dr. Wijayasuriya emphasized AI’s scaling properties, noting that “bias, opacity, at scale would mean harm at scale as well. So everything AI scales.” He highlighted AI’s potential for API orchestration and enabling “unconstrained scenarios” where “AI can do a billion scenarios.”

International Development Perspective: Safeguards and Implementation

Robert Opp from UNDP emphasized that the population-scale reach of DPI amplifies both opportunities and risks. His key insight was that “if efficiency is your only metric, then you will probably rush ahead and leave people out,” reframing how success should be measured in AI and DPI implementations.

UNDP has developed safeguards work supported by CoDevelop and the Gates Foundation, with frameworks now being implemented at national levels. Opp stressed the importance of early integration of safeguards rather than retrofitting them after deployment, and outlined requirements for inclusive AI systems including multilingual platforms, multimodal interfaces, and bias detection mechanisms.

He announced UNDP’s “100 Pathways” initiative, developed in partnership with Xstep and other organizations, taking a use-case driven approach to discovering and scaling responsible AI applications across different development contexts. Opp also noted UNDP’s internal organizational focus: “there is an internal to the organization level, which is how do we ensure that UNDP itself has capabilities for leveraging AI to maximum effect.”

Multilateral Banking Evolution: From Supply to Demand

Sangbu Kim, Vice President for Digital at the World Bank, highlighted a fundamental shift in development challenges. Despite over 90% of sub-Saharan Africa having 3G+ mobile coverage, the challenge has moved from infrastructure supply to demand creation and value generation.

Kim described an evolution in computing paradigms “from the very supplier-centric approach through the user-centric approach,” positioning DPI as well-suited for the AI era due to its user-centric design principles. The World Bank’s approach now emphasizes creating demand through government programs and developing specific use cases rather than focusing primarily on infrastructure deployment.

His emphasis on “small AI”—referring to practical, life-changing applications rather than model size—reflects a pragmatic approach prioritizing tangible improvements in citizens’ daily lives.

Private Sector Innovation and Public Infrastructure

Saibal Chakraborty from Boston Consulting Group highlighted how India’s DPI success is being replicated in AI. He noted that India is “a country of 120 unicorns, and every unicorn, some way or the other, leverages the DPIs,” demonstrating the transformative power of shared public infrastructure.

However, Chakraborty identified a critical market failure: while India has abundant venture capital funding, 90% flows into fintech and e-commerce, leaving climate, education, and MSME sectors underfunded. He described this as requiring “a tricky balancing act” where valuable government data needs controlled exposure to enable innovation without compromising security or sovereignty.

The India AI Mission’s approach treats AI as shared public infrastructure, with Saibal noting that “more than 38,000 GPUs are now available at, you know, less than rupees 60 per hour,” dramatically lowering barriers to AI development. He mentioned that “central government and the states are thinking of building fund of funds, which actually then, you know, encourage VCs to co-invest, focusing on those socially sensitive sectors.”

Key Challenges and Future Directions

The discussion revealed consensus on several critical priorities for AI and DPI integration. Voice-first capabilities emerged as particularly significant for enabling digital inclusion for populations previously left behind by text-based interfaces.

The challenge of data governance featured prominently, with speakers acknowledging the need for sophisticated frameworks that enable controlled access to valuable government datasets while maintaining appropriate protections. Dr. Wijayasuriya emphasized that institutional capacity represents a foundational requirement for effective implementation.

Looking ahead, the panelists identified the need for sophisticated institutional frameworks capable of balancing innovation with safeguards, comprehensive data governance, and mechanisms for ongoing monitoring and adjustment of AI systems. The discussion presented an optimistic yet realistic vision of AI’s potential to accelerate development outcomes while acknowledging the significant governance, equity, and sovereignty challenges that must be addressed to realize this potential responsibly.

Session transcript

Speaker 1

economy. Saibal Chakraborty, Managing Director and Senior Partner, Boston Consulting Group. The moderator, C.V. Madhukar, Chief Executive Officer of CoDevelop. And Mr. Sangbi Kim, Vice President for Digital, World Bank, who will be joining us in a bit. Thank you. right I will let the moderator take it forward

C.V. Madhukar:

thank you so much thank you Arjun and thank you all to the panelists the last session of the last day is a bit challenging so we will try and keep this focused and at the end if you have any questions we have time it would be great to have any questions if you want to have any discussions I think we have heard a lot about AI and DPI in the last four days I don’t want to belabor the point I think what we can celebrate for sure in India is that in terms of DPI and the thinking that we take to the world and to our own problems is amongst the best in the world.

And as we look at the journey on AI, which is just beginning for most of the world, what I see is if I look at the US, for instance, there is one spectrum of conversation, which is AGI and beyond. And on the other end, there is a despondency and worry and concern about jobs and privacy and a whole bunch of other important considerations. I think where India is, is somewhat different. India is saying, look, there will be a Chinese model, there will be an American model, there will be a whole bunch of other innovations that are going on. What do we do to embrace and use all of this for our benefit? I think that optimism and sense of can do and must do, I think is very exciting, and I think it’s been palpable in the last four or five days.

So to discuss the power of AI and DPI and AI in DPI, we have a wonderful panel as Arjun has introduced already. I will start with Dr. Hans, if you don’t mind. I think you represent a national government and you are living through these choices on a day -to -day basis. As a pragmatic government that has to think about sovereignty, inclusion, safeguards, what are the main considerations that are on top of your mind?

Dr. Hans Wijayasuriya

Thanks for the question. You’re right, I think those three words are very key. When you’re talking from a government perspective and therefore national, inclusion, integrity or the safety of the citizens. and also safeguards. I’ll just run through these three very quickly. Why inclusion? When it’s government and you introduce a new capability, you have to be sure that that capability does not increase divides, that it actually reduces divides. So you have to be very sensitive on the inclusion angle. And, for instance, AI, together with DPI, can stretch inclusion through its voice -first capabilities, we talked about cloud -first, API -first, et cetera. Now we can really seriously talk about voice -first and also the translation capabilities.

And accessibility has to be also broadened in terms of multi -modality and also, where necessary, include a human in the loop in the service delivery cycle as well. So some of the inclusion dimensions. Then more to integrity, which is a tougher one, And I think that’s a really good point. And I think that’s a really good point. An important point when you’re looking at integrity is to start from the premise that AI will not redefine DPI. AI would, or at least where we look at it from now, maybe I’ll be wrong in six months from today, but the DPI foundations must be in place first. DPI should be mature, and your approach to implementing DPI should be mature, and then you apply AI as a scaffolding on top of that foundation to accelerate your build and delivery.

So what are those foundations? Clean data and data maturity, maturity of your data architectures, clean registers, for instance, also reliable APIs, APIs which are not susceptible to cyber attacks, et cetera, and also the institutional capacity. The institutional capacity behind the DPI delivery. So these are foundations which should be in place. Now, on top of that, you apply AI and there are several unique features of AI which would deliver you super experience to the citizen. And I’ll come to that in a short time. The last one would be the safeguards, bias detection, and also the augmentation to consent. Because when you have AI systems in place, your consent could be AI generated as well. So you need to be careful about the augmentation you require, explainability, and human in the loop as well in terms of your safeguards.

So we need to be conscious that bias, opacity, at scale would mean harm at scale as well. So everything AI scales. So we need to be conscious that bias, opacity, at scale would mean harm at scale as well in terms of your safeguards. So we need to be conscious that bias, opacity, at scale would mean harm at scale as well in terms of your safeguards. Sovereignty is all about capability. It’s not isolation. it’s about building the capability in terms of being neutral and having a neutral capability in terms of vendors, in terms of cloud, in terms of technologies so that you have control, that the state has control over those building blocks and can choose across technology delivery parts as well as core technologies also data classification, protection, privacy basics, so I think if you go back to the foundations of protecting your citizens and their data you can’t go wrong and AI will only be an accelerator finally the experience angle, because a government would be very keen to deliver super experience and where does AI come in here I believe there are some specific strengths of AI when combined with DPIs and that is in terms of in particular API orchestration picking the right API to call at the right time also the fact that AI enables unconstrained scenarios so while linear methodologies would be constrained to say 4 or 5 scenarios when a citizen needs some help AI can do a billion scenarios and each scenario could be implemented using a unique set of API calls and unique set of DPI access and this can be orchestrated using AI so can AI make a big difference a leap forward in service delivery?

I believe so can governments and the sovereign use it or should they? Definitely but we need to be conscious of those 4 dimensions that I just mentioned of inclusion, integrity, safeguards, sovereignty all of these are important and we are still coming together to deliver a perfect experience

C.V. Madhukar:

Thank you, Dr. Hans. I will give a bit of a breather to Sangbu. Thank you, Sangbu. Great to have you. I’ll go to Robert first and then… Robert, as you at UNDP have led a very important work on safeguards, worked on the Global Digital Compact, engaged a number of countries on sustainable development goals and how AI and DPI can be an accelerant to all of those outcomes if you want. From your vantage point as you look at the AI revolution that’s unfolding upon us, what captures, what’s top of mind for UNDP?

Robert Opp

Yeah, no. So I think that the reason we have been so excited about digital public infrastructure as an approach overall is that it really does bring some very particular characteristics. And one of those, maybe the most important, is the population scale. and so it is something that can reach so many people so quickly if you get it right. We also have been learning that if you don’t get it right, then you can have problems and challenges at scale and so in the DPI space, one of the things that concern us the most is how do we ensure that as countries are building their DPI, how do we make sure that we are putting the safeguards in place?

And this is work that has been supported generously by Codevelop and Gates Foundation and others and has led over the last year and a half or so to the creation of a universal DPI safeguards framework which we’re now implementing or supporting a number of countries at national level to implement. But… But when we talk about that, what does it actually mean? And Dr. Hans referred to some of these important things. And so we are talking about the DPI as a whole. And so we are talking about the DPI as a whole. And so we are talking about the DPI as a whole. And it’s about what we’re learning is that the earlier in the process that you can start discussing the safeguards, the better off you’ll be down the road in terms of inclusion.

So if efficiency is your only metric, then you will probably rush ahead and leave people out. But if inclusion is your driving KPI, then you really need to make sure that you’re sitting down at the beginning and planning and designing with people in mind. When it comes to AI, then, it’s basically the same thing. And we need to be really careful, as Dr. Hans was saying, that we are putting the inclusion aspects, the safeguard aspects, at the center of our planning from the very beginning. And so that means, and you referred to a couple of these, but, you know, a multilingual platform, multimodal that can support people with disabilities, making sure that you are correcting for people.

So that’s one of the things that we’re doing. And I think that’s a really good thing. And I think that’s one of the things that we’re doing. And I think that’s one of the things that we’re doing. And I think that’s one of the things that we’re doing. And I think that’s one of the things that we’re doing. And I think that’s one of the things that we’re doing. And I think that’s one of the things that we’re doing. And I think that’s one of the things that we’re doing. And I think that’s one of the things that we’re doing. And I think that’s one of the things that we’re doing. and then of course making sure that data sets are there’s bias detection or you’ve got some understanding of your accuracy representation all of those kinds of things because when you’re going to layer AI into DPI as an accelerator well then you want to be sure that you’re on the right track and that people are considered from the very beginning with that and so I think that’s we see all the

C.V. Madhukar:

Thank you Rob Can I go to Sangbu now as you see the evolution of DPI plus AI from the World Bank standpoint one of the things that we’ve often looked at is is having open data sets that can train air engines would be an important way of advancing the benefits of AI but as you know governments build silos one silo after the other how are you seeing this from the World Bank’s standpoint what is the next journey for the next 3 -5 years looking like in terms of getting countries to become more AI ready any thoughts on that would be great

Sangbu Kim

DPI has a lot of aspects and characteristics this is one of the very productive and efficient way to ensure the interoperability even though it cannot ensure everything but we make some more effort to clarify some more interoperability capability so but it is basically what it looked like. So that’s why in AEI era, in order to prevent some siloed approach, DPI can play a very significant role, I would say. If you think about the AEI era, what is the relationship between DPI and AEI? If I just compare the DPI from the previous version of DPI, I would say DPI is more helpful for AEI era compared to the previous mobile era in many reasons. If you look back on our history of computing, we started from the computer and PC and evolved to mobile and evolved to AEI.

The trend is that it is from the very supplier -centric approach through the user -centric approach. We are evolving from the supplier mindset through the user mindset. The DPI is exactly just some of the really, really important tools to ensure that user -centricity. Because without identifying some good tools of users and some interoperability, it cannot really be achieved to fully support the user -customized things. So to me, the DPI and AI will be really important relationships. On the other hand, one opportunity… I also see through this trend… maybe DPI can be also very well upgraded quickly and efficiently upgraded through AI enabled technology so we used to collect all the data in a very manual way and then try very hard to streamline the governance of data sometimes very manually and sometimes by some intervention by the programmer but now we can see some old medical way so that we can quickly streamline all the DPI platforms so this is really a good opportunity for all of us from the World Bank perspective we really expect to see some more progress of this space

C.V. Madhukar:

Wonderful Saibal as a senior partner at Boston Consulting Group you have a bird’s eye view of a lot of early thinking on AI led innovation around the world, especially in the private sector. And I’m also sure you’ve been observing closely the India trajectory of DPI in the last decade or so. What lessons from the DPI journey in India can we take to the AI era that might also propel private sector innovation to levels beyond if we just didn’t think about DPI as core infrastructure for AI?

Saibal Chakraborty

So I think, firstly, India’s journey in DPIs has been a fascinating one. It makes me immensely proud that whichever country I go to, and I do work with quite a few South Asian and Southeast Asian countries, India is almost always seen as a benchmark in DPI, and now increasingly AI on top of DPI. I think, so maybe I’ll just answer your question in two parts, right? I mean, when we were building DPIs, starting with Aadhaar, you know, and then, you know, moving on to UPI, et cetera, the idea was always to build open population scale software, which can then trigger innovation, right? And that’s exactly what has happened over the last decade, right? The amount of innovation that has been built on these DPIs is mind -blowing.

I mean, India is now a country of 120 unicorns, and every unicorn, some way or the other, leverages the DPIs. So then, coming to the second part of your question, I mean, what lessons, right? So if you look at the way India is now thinking about AI, and as BCG, we have been privileged to be part of two of the very leading companies in the world, right? So we have done several seminal efforts, one with India AI Mission to build AI Coach, India’s national AI platform. and also with the state of Telangana to build the equivalent for the state of Telangana. Both of these happened last year. I think we are taking a very, very similar ethos, right?

So if you think about India, as I mentioned, there are 120 unicorns. There is no dearth of VC funding in India at all. However, 90 % of that VC funding actually goes into fintech and e -commerce. Very little goes into climate and sustainability. Very little goes into education. Very little goes into MSME relevant topics. So there is a gap, right? So what these platforms are trying to do? And then similarly, access to data within private sector, there is good quality data. But the biggest source of data in India is the government. The access to government data is still at a very nascent state. Quality of data and access to data. And then, of course, the biggest thing in AI, which is compute, right?

I mean, access to compute is what makes or breaks a startup. So the way in India, the way I see it, the way we have started thinking about AI platforms, and I’ll use the word platform, it treats AI as a shared public infrastructure. Just like DPI was a shared public infrastructure, it treats now AI as a shared public infrastructure. If you look at India AI mission, more than 38 ,000 GPUs are now available at, you know, less than rupees 60 per hour, which is less than a dollar per hour. So if you are a startup, very early stages, working in your garage, suddenly GPUs have become a bit more affordable for you, right? That’s genuine shared public infrastructure.

Government data, it’s early days, it’s very early days, but government data is being provided access to through platforms like AI Coach, or in Telangana through the TGDX. to solve the financing problem. How do you channel financing into socially sensitive sectors? The central government and the states are thinking of building fund of funds, which actually then, you know, encourage VCs to co -invest, focusing on those socially sensitive sectors where they would normally not invest in. So if you think about it, the ethos is very similar to when we were building DPIs. How do I create shared capabilities centrally, which then can trigger an entire new wave of startups, you know, and therefore a market ecosystem, just like the DPIs did.

So that’s how I see the journey.

C.V. Madhukar:

That’s great insight. I think it seems from what you say, there’s a beginning of a new innovation cycle for the private sector cycle, and we’re looking forward to what comes out. I just wanted to, we have about 10 minutes left. have a somewhat common forward -looking question to all panelists. As the economists say, in the long run, we’re all dead. But what is long run in the AI ecosystem? Is it five years? Is it three years? Because it’s so hard to predict everything. Every day there’s something new happening. So I don’t know, Dr. Hans, if you’re okay going first on this forward -looking question. The question I would ask of you is, as a relatively smaller island nation, how do you expect to leverage this wave of AI innovation over the next three years, maybe five years?

What steps are you anticipating? How are you preparing yourselves to leverage this power that we have to advance our development outcomes?

Dr. Hans Wijayasuriya

So there’s a plus and a minus of being small. Let’s start with some of the challenges. Being small, you’re on the wrong side of the AI divide unless you’re economically in a very powerful position. Say for example, Singapore is an outlier, a small country with a lot of economic power and therefore attracts investors, attracts talent and the sighting of business. Country, one of the challenges we have in Sri Lanka is one, getting that minimum level of sovereign AI infrastructure in place, having the ecosystems around it, retaining our talent. Sri Lanka has very good talent but retaining and developing the talent for Sri Lanka is a challenge but one that we are confident that we can deliver.

So future of AI I think will depend on the market. We’ll also depend on the people. We’ll also depend on the trust in us. The institutions like the data protection, the institutions as well as the laws, and Sri Lanka is very mature on this front. So on the trust side, a smaller country can execute precisely with laser -sharp focus and therefore has a strength. Talent side, again, it’s something to focus on. Now when it comes to the marriage with DPI, I think it falls onto the positives of being a small country because the ability to implement modular systems in a neat and flexible way, in a way that these blocks themselves will evolve, on the confidence that you have a strong trust environment, gives you the ability to build in AI where, like I mentioned earlier, AI sits.

On top of a solid DPI, a mature DPI frame. So I feel the future, AI future will of course be very close, can add that extra piece of experience, lower cost, faster, and more flexible, meaning that it can address multiple scenarios through digital twin and other such AI constructs to deliver citizens a very customized, I’m from the service industry in the past, so I use the word customized, but citizen -specific experience. We’ve been tracking the learning about the focus of the new government and their leadership and the presence of AI in the world. We’re working with the government’s leadership, making big advances on DPA and AI, and looking forward to much exciting stuff in Sri Lanka in the next two to three years.

C.V. Madhukar:

Thank you for those comments, Dr. Hans. Rob, could I come to you and think about, you know, you’ve gone through the process over the last couple of years with DPI safeguards. You have the Global Digital Compact. I know there’s a lot of work you’re doing on AI safeguards. Moving away from safeguards, I wanted to see how you are envisioning the developmental role of UNDP leveraging AI in the next three years. I guess three years is long term, but anything you can say that would be helpful for us.

Robert Opp

No, absolutely. So I think there’s a couple levels. So there is an internal to the organization level, which is how do we ensure that UNDP itself has capabilities for leveraging AI to maximum effect. And so there’s a kind of a base level of work that we’ve done internally to the organization, upskilling programs, investing in some, you know, making sure foundation model capabilities are available, working in some SLMs, et cetera, et cetera. Then there’s the layer of working across the kind of verticals that UNDP has, whether it’s environmental action, governance programs, energy, et cetera, et cetera. And so how do we embed AI solutions and thinking and approaches into those verticals? And then there’s the picture of how are we going to support our country partners?

And as you said, we’re engaged in quite a few countries already on AI transformation support, and it’s kind of looking at ecosystem pieces. Do countries have that mix of elements that Dr. Hans was referring to? Do you have the compute accessibility? Do you have talent? Do you have the data available? And so on and so forth. but the one thing that we’ve announced in this summit, during this summit is an exciting partnership with Xstep and a number of other players and we’re one of those players on something we’re calling 100 Pathways or Diffusion Pathways and that is looking kind of like more of a use case driven approach and finding over the next few years 100 different pathways to scaling responsible use of AI along different use cases and it’s something that we’re really excited about because I think we need that to complement the ecosystem support

C.V. Madhukar:

That’s so exciting to hear because I think there will be a lot of iteration discovery and innovation to discover those 100 pathways to actually add value to people on the ground looking forward to what comes out of that Thank you if you were to look at the last decade of how development banks have funded and looked at digitization, and now if you look at the three years ahead of you, what might the World Bank and MDBs do differently to be ready, help countries become more ready for the AI era that you haven’t been doing in the last decade or so? Any thoughts would be great.

Sangbu Kim

So good news is that from the Internet network point of view, the coverage is pretty good. So even in sub -Saharan Africa, more than 90 % of area of sub -Saharan Africa is covered by three -plus generation mobile tower. But the issue is that we are really struggling with lack of demand. How are we going to fully utilize this by creating some more value and profit? So now we are modifying some approach to really think about creating demand through government program, through developing some use cases. That’s why we are just keep highlighting the importance of small AI. Small AI is not really a small thing. So it is really about how we can really change the lives of our people.

So our approach is a little tweaking to the user -centric and demand -driven things. That’s our approach.

C.V. Madhukar:

That’s great. And I think MDBs and the relationships that they have with countries can make a big difference in how the evolution and the benefits of AI will come to people. So looking forward to what comes out. Saibal, I know I started off by bucketing you as a private sector guy. But I also know you’ve been thinking deeply about government policy that enables or doesn’t enable innovation and growth. So as you look at the next few years, even from private sector innovation lens, what government policies might propel the innovation ecosystem? to serve the underserved populations around the world. Any quick thoughts, Saibal?

Saibal Chakraborty

So, see, I think, you know, the government has a very tricky balancing act, right? I mean, when we were going through this entire experience of building AI Kosh, there’s obviously, for very good reasons, a lot of sensitivity around sovereign data, what data to expose, and if exposed to whom, right? Equally, without sharing of data, I mean, the reason why AI has taken off in a big way in some sectors is because internet came into picture 30 years back, and internet has been pumping, you know, billions and billions and gigabytes of data, right? So AI has something to chew upon. Now, if we have to do the same with government data, then that data, you know, needs to be exposed in a controlled manner.

So my sense is that from a policy standpoint, how do you actually provide that access to data? I mean, walking that tightrope where valuable data is made available to the innovators while not compromising on sovereignty or safety. I think that is one of the policy areas the government has to look at. Specifically for a country like India or similar countries which operate in a federated model, the center can do only so much. The real action, as we know, in a country like India happens at the state level and we have 30 plus of them combining states and union territories. So at the state level also, similar policies and institutions have to be set up. So Telangana, for example, has set up a Section 8 public sector undertaking to drive AI, right?

That creates the kind of focus and the agility that you will need to keep pace with this technology and do some real work at the grassroots. So my suggestion would be create those. Accountable institutions. who can anchor and drive AI and amend policies around data to make sure that people get access, the innovators get access while not compromising on sovereignty. It’s not an easy thing to do, but yeah, those would be my words.

C.V. Madhukar:

Thank you, Saibal. I think the role of institutions, both for safeguards, but also to enable the innovation ecosystem haven’t been more important than it’s now more important than ever before. I think it’s hard to summarize this conversation, but I will say that I think we’re at the cusp of something extremely important and something very potent in some ways and can unlock a lot of opportunity for innovation. billions of people. Especially, I think, the segment of the population that was left out of the digital revolution because voice was not the predominant way of interacting. I think AI opens up that window and hopefully will drive much more widespread adoption and usage by common people around the world.

Looking forward to the next few years and thank you very much to our panelists for a wonderful discussion. Thank you all. Thank you.

Speaker 1

Thank you so much to all the speakers. We will just have one memento being given by the organizing team. We made it. Thank you so much for being a part of the India AI Impact Summit. Just to tell you that the expo will be open tomorrow. People still want to come in and people are still not tired, but we are done for today and the sessions are done. Thank you so much. Thank you. Thank you.

Dr. Hans Wijayasuriya

Speech speed

134 words per minute

Speech length

Future roadmap and AI adoption in developing countries

Topics

Information and communication technologies for development | Capacity development

Robert Opp

Speech speed

170 words per minute

Speech length

Future roadmap and AI adoption in developing countries

Topics

Capacity development | Artificial intelligence

C.V. Madhukar

Speech speed

139 words per minute

Speech length

1254 words

Speech time

538 seconds

India‑Leadership

Explanation

Madhukar celebrates India’s DPI achievements as among the best globally, positioning India as a model for AI‑enabled public services.

Evidence

“I think what we can celebrate for sure in India is that in terms of DPI and the thinking that we take to the world and to our own problems is amongst the best in the world.” [41]. “And I’m also sure you’ve been observing closely the India trajectory of DPI in the last decade or so.” [42].

Major discussion point

Government priorities for AI in DPI

Topics

Information and communication technologies for development

Moderator‑Framing

Explanation

Madhukar frames the session by asking how India’s DPI journey can inform AI strategies and private‑sector innovation.

Evidence

“What lessons from the DPI journey in India can we take to the AI era that might also propel private sector innovation to levels beyond if we just didn’t think about DPI as core infrastructure for AI?” [3]. “So to discuss the power of AI and DPI and AI in DPI, we have a wonderful panel as Arjun has introduced already.” [8].

Major discussion point

Leveraging India’s DPI experience for private‑sector innovation

Topics

Information and communication technologies for development

Saibal Chakraborty

Speech speed

155 words per minute

Speech length

Leveraging India’s DPI experience for private‑sector innovation

Topics

Financial mechanisms | Environmental impacts

Sangbu Kim

Speech speed

108 words per minute

Speech length

474 words

Speech time

263 seconds

Open‑Data‑Interoperability

Explanation

Sangbu stresses the need for open datasets and interoperability to train AI models and avoid siloed government data practices.

Evidence

“One of the things we’ve often looked at is having open data sets that can train AI engines would be an important way of advancing the benefits of AI but as you know governments build silos one silo after the other…” [63]. “Because without identifying some good tools of users and some interoperability, it cannot really be achieved to fully support the user‑customized things.” [66].

Major discussion point

Building AI‑ready DPI

Topics

Data governance | Artificial intelligence | Information and communication technologies for development

Demand‑Driven‑AI

Explanation

He describes a shift from supplier‑centric to user‑centric, demand‑driven AI, emphasizing “small AI” use cases that create real demand.

Evidence

“Small AI is not really a small thing.” [88]. “our approach is a little tweaking to the user‑centric and demand‑driven things.” [92].

Major discussion point

Future roadmap and AI adoption in developing countries

Topics

Artificial intelligence | Social and economic development

Speaker 1

Speech speed

74 words per minute

Speech length

129 words

Speech time

104 seconds

Voice‑First‑Inclusion

Explanation

Speaker 1 notes that AI’s voice‑first and multimodal capabilities can bring digital services to populations previously excluded from the digital revolution.

Evidence

“AI, together with DPI, can stretch inclusion through its voice‑first capabilities, we talked about cloud‑first, API‑first, et cetera.” [11]. “Now we can really seriously talk about voice‑first and also the translation capabilities.” [114].

Major discussion point

Closing reflections on AI’s inclusion potential

Topics

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

Agreements

Agreement points

AI should be built on mature DPI foundations rather than replacing them

Speakers

– Dr. Hans Wijayasuriya
– Saibal Chakraborty

Arguments

DPI foundations must be mature before applying AI as scaffolding on top

AI should be treated as shared public infrastructure, similar to how DPI was built

Summary

Both speakers agree that AI should complement and build upon existing DPI infrastructure rather than replace it. Dr. Hans emphasizes that DPI foundations must be established first, while Saibal advocates for treating AI as shared public infrastructure following the same successful model as DPI.

Topics

Information and communication technologies for development | Artificial intelligence

Inclusion must be prioritized from the beginning of AI and DPI implementation

Speakers

– Dr. Hans Wijayasuriya
– Robert Opp
– C.V. Madhukar

Arguments

Arguments

Need for controlled data exposure policies that enable innovation while maintaining sovereignty

Shift from supply-driven to demand-driven approach focusing on creating value and use cases

Summary

Both speakers acknowledge the challenge of making government data available for AI innovation while protecting sovereignty and citizen interests. They agree that a balanced approach is needed that enables innovation through controlled data access while maintaining security and sovereignty.

Topics

Data governance | The enabling environment for digital development | Human rights and the ethical dimensions of the information society

Information and communication technologies for development | Artificial intelligence | The digital economy

Unexpected consensus

Small countries can be advantageous for AI implementation

Speakers

– Dr. Hans Wijayasuriya

Arguments

Small countries can execute with laser-sharp focus despite challenges in retaining talent

Explanation

While one might expect small countries to be at a disadvantage in AI development, Dr. Hans argues that small countries like Sri Lanka can actually have advantages in executing AI initiatives with precision and focus, despite challenges like talent retention. This represents an unexpected optimistic perspective on small nation AI capabilities.

Topics

Capacity development | The enabling environment for digital development

Infrastructure coverage is not the main challenge in developing regions

Speakers

– Sangbu Kim

Arguments

Despite good mobile coverage, struggling with lack of demand utilization in developing regions

Explanation

Contrary to common assumptions that infrastructure coverage is the primary barrier in developing regions, Sangbu reveals that over 90% of sub-Saharan Africa has mobile coverage, but the real challenge is creating demand and utilization. This shifts focus from supply-side to demand-side solutions.

Topics

Closing all digital divides | Information and communication technologies for development

Private sector innovation requires public sector data and infrastructure support

Speakers

– Saibal Chakraborty

Arguments

90% of VC funding goes to fintech/e-commerce while climate, education, and MSME sectors lack funding

AI platforms provide affordable GPU access (less than $1/hour) to enable startup innovation

Explanation

There’s unexpected consensus that successful private sector AI innovation actually depends heavily on public sector support through shared infrastructure and data access. This challenges the typical narrative of private sector independence and highlights the need for public-private collaboration.

Topics

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

Overall assessment

Summary

The speakers demonstrate strong consensus on several key principles: AI should build upon mature DPI foundations rather than replace them, inclusion must be prioritized from the beginning, comprehensive safeguards are essential, and successful AI implementation requires balanced approaches to data governance and institutional support. There is also agreement on the importance of voice-first and multilingual capabilities for accessibility.

Consensus level

High level of consensus with complementary perspectives rather than conflicting viewpoints. The speakers represent different sectors (government, international development, private sector, multilateral banks) but share similar values around responsible AI implementation, inclusion, and the importance of strong foundational infrastructure. This consensus suggests a mature understanding of AI implementation challenges and a shared commitment to equitable outcomes, which bodes well for coordinated global efforts in AI and DPI development.

Differences

Different viewpoints

Approach to data sharing and access

Speakers

– Saibal Chakraborty
– Dr. Hans Wijayasuriya

Arguments

Need for controlled data exposure policies that enable innovation while maintaining sovereignty

Governments must balance inclusion, integrity, safeguards, and sovereignty when implementing AI

Summary

Saibal emphasizes the need for more open data access to enable innovation, arguing that valuable government data should be made available to innovators in a controlled manner. Dr. Hans takes a more cautious approach, emphasizing that sovereignty and safeguards must be prioritized, with data protection being fundamental before any exposure.

Topics

Data governance | Human rights and the ethical dimensions of the information society | The enabling environment for digital development

Development approach – supply vs demand focus

Speakers

– Sangbu Kim
– Robert Opp

Arguments

Shift from supply-driven to demand-driven approach focusing on creating value and use cases

Supporting countries with ecosystem elements including compute accessibility, talent, and data availability

Summary

Sangbu advocates for shifting from infrastructure supply to demand creation through use cases, noting that despite good coverage, utilization remains low. Robert focuses on ensuring countries have complete ecosystem elements including infrastructure, suggesting a more comprehensive supply-side approach to support.

Topics

Information and communication technologies for development | Social and economic development | The enabling environment for digital development

Unexpected differences

Role of small countries in AI development

Speakers

– Dr. Hans Wijayasuriya

Arguments

Small countries can execute with laser-sharp focus despite challenges in retaining talent

Explanation

Dr. Hans presents a unique perspective that small countries actually have advantages in AI implementation through focused execution and strong trust environments, which contrasts with typical assumptions that only large countries with extensive resources can succeed in AI development. This perspective was not challenged by other speakers but represents an unexpected viewpoint.

Topics

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

Overall assessment

Summary

The discussion revealed relatively low levels of fundamental disagreement, with most differences centered on implementation approaches rather than core principles. Key areas of difference included the balance between data openness and sovereignty, and whether to prioritize supply-side infrastructure or demand-side use case development.

Disagreement level

Topics

Closing all digital divides | Artificial intelligence

Information and communication technologies for development | Artificial intelligence | The digital economy

Takeaways

Key takeaways

AI should be implemented as scaffolding on top of mature DPI foundations, not as a replacement for DPI infrastructure

Governments must balance four critical dimensions when implementing AI: inclusion, integrity, safeguards, and sovereignty

AI enables voice-first and multimodal capabilities that can reduce digital divides and include previously underserved populations

India’s approach treats AI as shared public infrastructure, similar to how DPI was built, enabling startup innovation through affordable access to compute resources

Early planning with safeguards is essential – inclusion must be the driving KPI rather than efficiency alone to prevent leaving people out

Development banks are shifting from supply-driven to demand-driven approaches, focusing on creating value and use cases rather than just infrastructure coverage

The marriage of AI and DPI requires strong institutional capacity, clean data architectures, reliable APIs, and robust cybersecurity measures

Small countries can leverage their ability to execute with laser-sharp focus, though they face challenges in talent retention and accessing sovereign AI infrastructure

Resolutions and action items

UNDP announced the ‘100 Pathways’ initiative in partnership with Xstep and other players to find 100 different pathways to scaling responsible AI use cases

Implementation of universal DPI safeguards framework at national levels across multiple countries

India AI Mission providing access to 38,000+ GPUs at less than $1 per hour to make compute affordable for startups

Building fund of funds by central government and states to encourage VC co-investment in socially sensitive sectors

Sri Lanka working on establishing minimum level of sovereign AI infrastructure and developing talent retention strategies

Unresolved issues

How to balance controlled data exposure for innovation while maintaining sovereignty and safety – described as a ‘tricky balancing act’

Access to government data remains at a ‘very nascent state’ despite government being the biggest source of quality data

Talent retention challenges for smaller countries in the AI era

Funding gaps persist with 90% of VC funding going to fintech/e-commerce while climate, education, and MSME sectors remain underfunded

The challenge of creating demand and utilization despite good mobile infrastructure coverage in developing regions

Long-term sustainability and scalability of AI initiatives at state and local levels in federated systems

Suggested compromises

Implementing human-in-the-loop systems to balance AI automation with human oversight and safety

Creating accountable institutions at both central and state levels to balance agility with governance requirements

Developing multimodal platforms that can serve both tech-savvy and traditional users through various interaction methods

This comment honestly addresses the challenges faced by smaller nations in the AI era while identifying specific advantages they might have (precision, focus, trust). It provides a realistic assessment of geopolitical dynamics in AI development while maintaining optimism about smaller countries’ potential.

Impact

This comment brought a crucial perspective about digital sovereignty and the challenges of smaller nations, adding nuance to the discussion beyond the experiences of large countries like India. It influenced the conversation toward considering how AI benefits can be more equitably distributed globally.

Overall assessment

Explanation

Addressing the funding gap in critical development sectors requires innovative financial instruments and policy interventions.

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.