Trusted Connections_ Ethical AI in Telecom & 6G Networks
20 Feb 2026 18:00h - 19:00h
Trusted Connections_ Ethical AI in Telecom & 6G Networks
Session at a glance
Summary
This discussion focused on the integration of artificial intelligence in telecommunications networks, held as part of India’s AI Impact Summit 2026 and organized by the Telecom Regulatory Authority of India (TRAI). TRAI Chairman Anil Kumar Lahoti opened the session by emphasizing that AI is no longer an add-on to telecommunications but has become a foundational capability, particularly crucial given India’s scale of over 1.3 billion telecom subscribers. He highlighted that AI is already delivering tangible benefits, including significant energy savings and the blocking of nearly 400 million spam calls daily through AI-powered filtering systems.
The panel discussion featured industry experts from major telecommunications equipment manufacturers including Ericsson, Qualcomm, Nokia, and Tejas Networks. Magnus Ewerbring from Ericsson emphasized India’s advantageous position with over 90% 5G population coverage, noting that networks already use AI for optimization but are moving toward fully autonomous operations by 2028. Dr. Vinesh Sukumar from Qualcomm discussed the democratization of AI through edge computing on personal devices, highlighting the importance of hybrid AI systems that balance cloud and edge processing.
Pasi Toivanen from Nokia stressed the critical importance of building collaborative ecosystems among technology players, regulators, and government agencies to fully capture AI’s potential while addressing security risks. Shantaram Jagannath from Tejas Networks presented a framework for AI adoption based on cost optimization and revenue generation, suggesting that telecom networks could become platforms for AI services similar to app stores. The discussion addressed key challenges including maintaining service equity between urban and rural areas, managing the transition from current networks to AI-native systems, and preparing for a future where AI agents may outnumber human users on networks. The consensus emerged that successful AI integration in telecommunications requires end-to-end ecosystem thinking, robust regulatory frameworks, and collaborative approaches to ensure both innovation and public trust.
Keypoints
Major Discussion Points:
– AI-Native Network Evolution: The transition from traditional telecom networks to AI-native 6G networks, where AI will be intrinsic rather than an add-on application layer, requiring networks to become fully autonomous and self-healing
– Responsible AI Implementation and Governance: The need for risk-based regulatory frameworks, transparency, accountability, and consumer protection as AI systems in telecom can affect millions of users simultaneously, with emphasis on maintaining public trust
– Edge vs. Cloud AI Processing: The strategic decisions around hybrid AI architectures, determining which AI functions should run on network edge devices versus centralized cloud systems, balancing performance, privacy, and efficiency
– Ecosystem Collaboration and Standards: The importance of building comprehensive ecosystems involving telecom operators, technology companies, regulators, and government agencies to address AI challenges collectively rather than in isolation
– Scalability and Infrastructure Challenges: Managing the transition of India’s massive telecom infrastructure (1.3 billion subscribers) to AI-enabled systems while ensuring equitable access across urban and rural areas and preparing for exponential growth in AI agents
Overall Purpose:
The discussion aimed to explore how telecom networks must evolve to support the AI era, focusing on technical architecture, regulatory frameworks, and responsible implementation strategies. The session was part of India’s AI Impact Summit 2026, specifically examining how to prepare telecom infrastructure for AI-native operations while maintaining security, sustainability, and equitable access.
Overall Tone:
The discussion maintained a consistently optimistic and forward-looking tone throughout. Speakers expressed confidence in India’s position as a leader in AI-telecom convergence, citing the country’s extensive 5G coverage and digital infrastructure. The tone was collaborative and solution-oriented, with industry experts emphasizing partnership and ecosystem thinking. While acknowledging significant technical and regulatory challenges, the overall sentiment remained positive about the transformative potential of AI in telecommunications, with speakers viewing obstacles as manageable through proper planning and cooperation.
Speakers
Speakers from the provided list:
– Ms. Pallavi Mishra – Event moderator/host organizing the discussion on AI and telecommunication at India AI Impact Summit 2026
– Shri Anil Kumar Lahoti – Honorable Chairman, Telecom Regulatory Authority of India (TRAI), telecom regulatory expert with dynamic leadership in the telecom regulatory ecosystem
– Shri Ritu Ranjan Mittar – Member TRAI, telecom policy expert with over three decades of experience in telecom networks, global standards, and spectrum policy, session moderator
– Magnus Ewerbring – Chief Technology Officer for Asia Pacific at Ericsson, global telecom innovator involved in developing region’s long-term technology vision from 5G deployment to 6G readiness
– Dr. Vinesh Sukumar – Vice President, Product Management at Qualcomm, seasoned product leader with over 20 years of experience in large-scale AI, deep learning, and mobile technologies across global telecom ecosystem
– Mr Pasi Toivanen – Representative from Nokia, leads strategic engagement with governments and industry on AI and connectivity initiatives, driving large-scale ecosystem collaboration in cloud, AI and AI RAN
– Mr. Shantigram Jagannath – Representative from Tejas Networks, technology strategist leading wireless products, network management system, and AI-driven innovations
– Audience – Participant who asked a question during the Q&A session
Additional speakers:
None identified beyond the provided speakers names list.
Full session report
This comprehensive discussion on artificial intelligence integration in telecommunications networks took place during India’s AI Impact Summit, organised by the Telecom Regulatory Authority of India (TRAI) in collaboration with India AI under the Ministry of Electronics and IT. The session was held on February 20th, marking TRAI’s 29th anniversary of shaping India’s telecommunications landscape, and brought together representatives from telecom operators, technology original equipment manufacturers (OEMs), policymakers, government officials, academia, and media to address the transformative convergence of AI and telecommunications.
Foundational Shift: From AI Add-On to AI-Native Networks
TRAI Chairman Anil Kumar Lahoti opened the session with a paradigm-shifting perspective that fundamentally reframed the relationship between AI and telecommunications. Rather than viewing AI as merely an application layer or add-on service, Lahoti emphasised that artificial intelligence has become a foundational capability intrinsic to network operations. He articulated a vision where upcoming 6G technology will be inherently AI-native, transforming telecom networks from simple data carriers into central pillars of India’s AI infrastructure.
This foundational shift carries profound implications for network design, operation, and user experience. Lahoti described networks that can self-heal, detect faults proactively before users experience problems, and deliver seamless connectivity to billions without interruption—capabilities that move beyond science fiction into practical reality. The Chairman positioned India’s nationwide fibre backbones and mobile broadband networks as constituting one of the world’s most widely distributed digital infrastructures, operating within mature operational and regulatory frameworks that provide a solid foundation for AI integration.
India’s Strategic Advantage and Current AI Benefits
The discussion highlighted India’s unique position in the global AI-telecommunications convergence, with the country operating telecom networks at unprecedented scale. With over 1.3 billion telecom subscribers and more than 1 billion data users, India represents a testing ground where AI-driven automation transitions from optional enhancement to indispensable necessity. This scale advantage positions India to lead global innovation in AI-native network operations.
Current AI implementations are already delivering tangible benefits across multiple dimensions. Operators report significant energy savings through AI optimisation, whilst AI and blockchain-based filtering systems now flag or block nearly 400 million suspected spam calls or messages daily. Enhanced enforcement capabilities have led to the disconnection of approximately 2.1 million spam numbers, demonstrating AI’s effectiveness in combating fraud and improving consumer safety. Additionally, TRAI is advancing the rollout of a digital consent acquisition framework following successful pilot runs with banks, ensuring consumers maintain digital control over consent for commercial communications.
Industry Expert Perspectives on Network Evolution
The panel discussion featured distinguished experts from major telecommunications equipment manufacturers, each offering unique insights into AI integration challenges and opportunities. Magnus Ewerbring from Ericsson emphasised India’s advantageous position with over 90% 5G population coverage, noting that whilst networks already utilise AI for optimisation, the industry is progressing toward fully autonomous operations. He outlined specific achievements his company has observed, including 10% capacity optimisation in link adaptation and 33% energy efficiency improvements in network operations.
Ewerbring described the industry’s journey toward level 4 autonomy by 2028, as defined by TM Forum standards, with 6G networks targeting fully autonomous level 5 operations. This progression represents a systematic evolution from current semi-automated systems to networks that can operate independently with minimal human intervention, fundamentally changing how telecommunications infrastructure is managed and maintained.
Dr. Vinesh Sukumar from Qualcomm focused on the democratisation of AI through edge computing, emphasising the importance of bringing AI inference capabilities directly to personal devices including phones, laptops, smart watches, and smart glasses. He highlighted the critical challenge of developing hybrid AI systems that intelligently balance processing between edge devices and cloud infrastructure. This hybridisation concept addresses the fundamental question of which AI functions should run locally on devices for privacy and responsiveness versus which should leverage cloud resources for complex processing and fleet management.
Sukumar identified key performance indicators that guide edge versus cloud decisions: data privacy, user privacy, responsiveness, and predictable end-to-end performance favour edge processing, whilst fleet management, AI/ML training, and complex scenario handling benefit from cloud infrastructure. However, he acknowledged that current routing decisions remain largely static, with predetermined workloads assigned to specific processing locations, whilst the ultimate goal involves dynamic, intelligent routing that can adapt based on conversation context and user needs.
Ecosystem Collaboration and Platform Innovation
Pasi Toivanen from Nokia introduced a crucial perspective on ecosystem collaboration, arguing that successful AI implementation requires unprecedented cooperation between technology players, regulators, and government agencies. He challenged the traditional competitive approach in telecommunications, asserting that no single entity, regardless of expertise, can fully capture the 360-degree complexity of AI implementation independently. This ecosystem thinking emphasises transparent value creation and distribution amongst collaborative partners.
Toivanen stressed that security risks in AI-enabled networks will be fundamentally different from traditional threats, requiring end-to-end thinking and collaborative approaches to address vulnerabilities effectively. He advocated for pushing decision-making capabilities to the network level wherever possible, minimising reliance on distant data centres to reduce inefficiency and complexity whilst maintaining robust security postures.
Shantaram Jagannath from Tejas Networks presented a comprehensive framework for AI adoption based on cost optimisation and revenue generation strategies. He outlined approaches for both capital expenditure (CAPEX) optimisation through architectural choices and operational expenditure (OPEX) optimisation through enhanced operational efficiency. For revenue generation, he identified product enhancement opportunities that improve network efficiency alongside innovative AI-as-a-service models.
Jagannath introduced a revolutionary concept of transforming telecom networks into AI platforms similar to app store models, where developers can upload AI applications that become accessible to all network users. This platform approach particularly addresses India’s “bottom of the pyramid” population, enabling democratised access to AI services through telecommunications infrastructure. He projected a fundamental shift from current human-centric networks to future networks potentially handling significantly more AI agents, requiring comprehensive rethinking of business models, pricing frameworks, and regulatory approaches.
Trust-Centric Governance and Regulatory Framework
Chairman Lahoti emphasised that trust must remain the central pillar of AI adoption in telecommunications, given that automated algorithmic decisions can simultaneously affect millions of users. This scale amplification makes transparency, accountability, and consumer rights protection non-negotiable requirements rather than optional considerations. He referenced the MANOV vision announced by the Honorable Prime Minister of India, which emphasises a human-centric framework for ethical, accountable, and inclusive AI governance.
TRAI has proactively addressed these challenges through a risk-based regulatory framework for AI in telecommunications, recognising that different AI use cases carry varying levels of risk. Low-risk applications may be guided through self-regulation mechanisms, whilst high-risk use cases—especially those directly affecting consumers—require stronger obligations around transparency, explainability, and human oversight. This nuanced approach enables innovation whilst ensuring appropriate safeguards.
In April 2024, TRAI further facilitated responsible innovation through recommendations on regulatory sandboxes, enabling live network testing of AI-enabled solutions within defined safeguards. This approach reflects TRAI’s regulatory philosophy of enabling innovation whilst ensuring public interest protection, providing controlled environments for testing AI applications relevant to 5G and future 6G networks.
The regulatory framework aligns with the Government of India’s broader AI governance approach, including the India AI mission and recently articulated AI governance guidelines. These principles prove particularly relevant for telecommunications, where AI systems interact continuously with citizens, enterprises, and public institutions.
Technical Architecture and Implementation Challenges
The discussion revealed significant technical challenges in implementing AI across India’s massive existing telecommunications infrastructure. A key debate emerged around optimal decision-making distribution within network architectures. Dr. Sukumar advocated for edge processing to handle privacy-sensitive operations and latency-critical applications, whilst Mr. Toivanen argued for concentrating most decisions at the network level to avoid inefficiency and complexity associated with distributed processing.
This architectural debate reflects broader challenges in hybrid AI implementation, where systems must intelligently route workloads between edge devices, network infrastructure, and cloud resources based on real-time context, user requirements, and network conditions. Current implementations largely rely on static routing decisions with predetermined workload assignments, but the industry aspires toward dynamic, intelligent routing capabilities.
The sustainability dimension adds another layer of complexity, as AI’s compute-intensive nature raises concerns about energy consumption and environmental impact. However, the discussion revealed that properly implemented AI can actually improve energy efficiency, with concrete examples of significant energy savings in network operations.
A critical question from the audience addressed the practical challenge of managing 118 crore mobile connections, highlighting the need for sophisticated network management capabilities that can create different network slices for various use cases while ensuring equitable resource distribution across urban and rural areas.
Future Vision and Economic Models
The session addressed practical implementation challenges specific to India’s telecommunications landscape, including the tension between AI-first architectures that require comprehensive infrastructure replacement versus approaches that leverage existing investments whilst adding AI functionality.
The discussion also explored future network usage patterns, with projections suggesting that AI agents could significantly outnumber human users in coming years. This transition from human-centric to AI agent-dominated networks raises questions about pricing models, regulatory oversight, and value distribution across the AI ecosystem.
The platform model concept introduces additional complexity, as telecom networks would need to balance their traditional role as connectivity providers with new responsibilities as AI service platforms. This transformation requires developing technical capabilities for dynamic application deployment and establishing governance mechanisms for platform operations.
International Cooperation and Standards
The discussion acknowledged that AI-driven telecom operations increasingly operate across borders, making interoperability, standards development, and ethical alignment global concerns. India’s experience deploying AI in telecommunications at population scale offers valuable lessons for international cooperation, whilst shared challenges require collaborative solutions.
The progression toward 6G networks necessitates international coordination on standards development, particularly for AI-native network architectures and autonomous operation capabilities. This coordination must address technical interoperability, security frameworks, and ethical guidelines that enable seamless international connectivity.
Conclusion and Path Forward
The session concluded with strong consensus that AI will undoubtedly shape the future of telecommunications, but the manner of design, governance, and deployment will determine whether this future proves trusted, inclusive, and resilient. The discussion successfully bridged technical capabilities with policy implications, business model innovation, and societal impact considerations.
TRAI’s commitment to working with all stakeholders—industry, policymakers, and international partners—ensures that AI in telecommunications serves both innovation and public good. The regulatory authority’s approach of enabling innovation whilst maintaining public interest protection provides a framework for responsible AI deployment at scale.
The conversation revealed that successful AI integration in telecommunications requires more than technical implementation; it demands ecosystem thinking, collaborative governance, and careful attention to equity and sustainability concerns. India’s unique position—with extensive 5G coverage, massive user base, and proactive regulatory framework—positions the country to lead global innovation in AI-native telecommunications whilst serving as a model for responsible AI deployment.
This first plenary session established the foundation for deeper discussions, with a second session planned to focus specifically on “building customer trust through AI-driven operations,” reflecting the continued emphasis on trust as the cornerstone of AI adoption in telecommunications infrastructure.
Session transcript
Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. being organized on the sidelines of India AI Impact Summit 2026. Today, we are gathered to discuss the new elements of AI and telecommunication. This event is organized by Telecom Regulatory Authority of India, TRI, in collaboration with India AI under Ministry of Electronics and IT. Today, interestingly, on 20th February, TRI marks 29 years of its journey in shaping India’s telecommunication landscape. Representatives from telecom operators, technology OEMs, policymakers, government, academia and media are present here. Aap sabhi ka hardik abhinandan hai. Kalpna kijiye ek aisa telecom network jo khud ko heal kar sakhe that can detect faults even before we know them and deliver seamless connectivity to billions without interruption.
This is not a science fiction. This is the power of AI in telecommunication. Today, AI is transforming industries. And as we look ahead, AI is all set to become even more transformative. From predictive network management to intelligent customer experiences, the possibilities are humongous. Now, it’s my proud privilege to invite Shri Anil Kumar Lahoti ji, Honorable Chairman TRAI, whose dynamic leadership continues to provide direction and strength to the telecom regulatory ecosystem of our country. Chairman sir needs no introduction. His vision has been instrumental in steering TRAI through a rapidly evolving digital landscape. I respectfully request Chairman sir to kindly deliver his inaugural address.
Distinguished leaders from the technology companies, from telecom service providers and industry associations, representatives from government, my colleague members from TRI and other colleagues from TRI, ladies and gentlemen. Good afternoon to all of you. It’s my privilege to welcome all of you to this session on Responsible AI in Telecom. This is a session by the side of India AI Impact Summit. During last few days, we have been listening to the world leaders from governments, technology companies, academia and civil society. AI is now and here. In this context, the very composition of this gathering reflects a shared responsibility of recognition. that artificial intelligence is no longer an emerging add -on to telecommunication. It’s a foundational capability shaping how networks are designed, operated and experienced by users.
Artificial intelligence and telecommunications complement each other to form the backbone for the intelligence era. Telecom networks are emerging as the primary carriers of AI, while AI itself is becoming the intelligence layer of telecom. In the upcoming 6G technology, AI will no longer be an application layer. It will be intrinsic. The telecom networks will be AI native. In this sense, telecom networks are no longer mere data carriers, but these are central pillar of India’s AI infrastructure. Our nationwide fiber backbones and mobile broadband networks constitute one of the most widely distributed digital infrastructures in the world, operating within mature operational and regulatory frameworks. India’s scale gives special significance to this convergence. With over 1 .3 billion telecom subscribers and over 1 billion data users, India operates telecom networks at a scale, where AI -driven automation is no longer optional.
It is indispensable. AI is already being deployed to optimize network performance, predict faults, improve energy efficiency, enhance customer experience, and combat fraud and spam communications. These deployments demonstrate how AI can improve service quality, resilience, and consumer safety when applied responsibly at the network level. India is already witnessing clear gains from the responsible use of AI in the telecom sector. Operators are stating significant energy saving with use of AI. Due to the effectiveness of AI and blockchain -based filtering operators are now flagging or blocking nearly 400 million suspected spam calls or messages each day. Enhanced enforcement and improved oversight of service providers has already led to the disconnection of about 2 .1 million spam numbers. The authority is also advancing the rollout of a digital consent acquisition framework following successful pilot runs with the banks to ensure consumers have digital control over consent for commercial communications.
At the same time, the scale at which AI systems operate is also increasing. The impact of AI in telecom also amplifies their impact. automated decisions taken by algorithms can affect millions of users simultaneously this makes trust the central pillar of AI adoption in telecommunication efficiency gains cannot come at the cost of transparency, accountability or consumer rights as telecom is an essential service public confidence must remain at the core of AI enabled transformation the government of India has been proactive in addressing this balance the India AI mission and the recently articulated AI governance guidelines emphasize a huge role in the development of AI and the recent articulated AI governance guidelines emphasize a huge role in the development of AI and the recent articulated AI governance guidelines emphasize a huge role in the development of AI emphasize a huge role in the development of AI and the recent articulated AI governance guidelines emphasize a huge role in the development of AI emphasize a huge role in the development of AI emphasize a huge role in the development of AI and the recent articulated AI governance guidelines one that encourages innovation while embedding safeguards by design.
These principles are particularly relevant for telecom, where AI systems interact continuously with the citizen, enterprises and public institutions. TRI has been aligned with this approach. In July 2023, TRI issued recommendations on leveraging artificial intelligence and big data in the telecommunications sector, proposing a risk -based regulatory framework for AI in telecom. This approach recognizes that not all AI use cases are safe, but that all AI use cases carry the same level of risk. While low -risk applications may be guided through self -regulation, high -risk use cases, especially those directly affecting consumers, require stronger obligations around transparency, explainability, and human oversight. In April 2024, TRI further facilitated this approach through its recommendations on the regulatory sandbox, enabling live network testing of AI -enabled solutions, including those relevant for 5G and future 6G networks within defined safeguards.
This reflects our regulatory philosophy of enabling innovation while ensuring that public interest remains protected. The MANOV vision announced yesterday by the Honorable PM of India emphasizes a human -centric framework for ethical, accountable, inclusive AI governance. The principles of the Mana vision are equally fundamental to AI governance in telecommunication. Coming back to the agenda of today’s program, the two plenary sessions, we have planned to capture this responsibility very well. The first session featuring technology developers will focus on preparing telecom networks for the AI era and examine how networks must evolve to become more intelligent, autonomous and resilient, while remaining secure and sustainable. The second session with representatives from telecom service providers and GSMA will address building customer trust through AI -driven operations, highlighting governance, ethics, accountability, and customer protection in an environment where AI -based decisions increasingly shape everyday connectivity.
As AI -driven telecom operations scale across borders, issues of interoperability, standards, and ethical alignment become global concerns. India’s experience of deploying AI in telecom at population scale offers valuable lessons, while international cooperation remains essential. To address shared challenges, let me conclude with this thought. AI will undoubtedly shape the future of telecommunications. But it is the way we design, govern and deploy AI that will determine whether this future is trusted, inclusive and resilient. TRI remains committed to working with all stakeholders, industry, policymakers and international partners to ensure that AI in telecom serves both innovation and public good. I wish this session fruitful deliberations and look forward to the insights that will emerge from today’s discussions. Thank you.
Thank you very much, Chairman, sir, for your inspiring address. You have illuminated how regulatory frameworks and policies are evolving AI -driven telecom. Sir, your words make us believe that this transformation is moving forward in a positive way. We are delighted to hear your perspective, sir. Heartful gratitude to all the esteemed speakers and guests. The inaugural session has set a vibrant context for our upcoming discussions. Now our first plenary session will begin. Our first session is on preparing telecom networks for AI era. In this session, our experts will discuss AI adoption in telecom, transparency, security, safety, sustainable AI networks, and embedding responsibility by design. To moderate this insightful discussion, we are honored to invite Sridhar Ranjan Mittal, member TRAI, an eminent telecom policy expert with over three decades of experience in telecom networks, global standards, and spectrum policy.
We are honored to have distinguished panel of industry leaders. I welcome on dies. Our first panelist, Mr. Magnus Eberberg, Chief Technology Officer for Asia Pacific at Ericsson, a global telecom innovator who had played a key role in developing region’s long -term technology vision from 5G deployment to 6G readiness. Joining us next is Dr. Vinesh Sukumar, Vice President, Product Management from Qualcomm, a seasoned product leader with over 20 years of experience in large -scale AI, deep learning, and mobile technologies across global telecom ecosystem. We also welcome Mr. Parsi Tovnen of Nokia, who leads strategic engagement with governments and industry on AI and connectivity initiatives, driving large -scale ecosystem collaboration in cloud. AI and AI RAN. Our next distinguished speaker is Mr. Shanti Gram Jagannath from Tejas Networks, a technology strategist leading wireless products, network management system, and AI -driven innovations.
I request all the panelists to join for a quick photograph session on the demand of the organizers. You all may stand for a moment. Thank you, sirs. We look forward to a thoughtful exchange on how AI -driven innovations are being used in the future. We are redefining telecom capabilities from our panelists. now I hand over the stage to our moderator Sri Rutheranjan Mithrasar to start the session
Thank you Madam Sri Lahoti Chairperson TRAI my colleague member Dr. Tangirala doyens of the industry OEMs your staff accompanying you my colleagues from TRAI industry associations young officers representatives of start -ups so it’s a very important session on how the telecom networks will actually evolve to AI we saw a lot of use cases in the last 2 -3 days related to farming education healthcare but today the focus is telecom and the first session, this session is on the network, the next session as you’ve been informed is the subscriber so as a telecom network we are introduced to term access network, so that is one thing we would like to understand how your access network is evolving with the AI, we all know that in the 6G, AI and communication is one of the important use cases one of the 6 cases similarly going to core what kind of changes do you envisage when you implement AI, especially with respect to core, chairperson spoke about the benefits that are already accruing for the network management when the network management is getting into the AI is getting into the network management management.
Another thing I would also request you to dwell on is that ultimately the AI is going to come on the handsets. So once AI is going to come in on the handsets, what kind of a challenge it will throw to the network? Another important aspect is also raised by chairperson is the security. Are are we going to be challenged by the AI being used for attacking the networks? And what kind of steps we intend to take? Another one thing with the AI is it is compute intensive. So the sustainability as also is listed is going to be important. So it will be important to know what kind of steps the OEMs are envisaging to take care of the sustainability part of it.
You will be all are kind of signatories to the or the countries signatories to the sustainable development goals of the UN so this aspect also is very important now without taking too much time I will, everybody wants to listen from the experts here, I will first like to invite Mr. Magnus Everbring from Ericsson to kindly share
Thank you very much, it’s a great pleasure to be here now, bottom of my heart I’m very impressed by this event and the messages we’ve heard and I think we just heard at the onset of this very much central message to leverage AI fully for ordinary people, consumers, for industries, for enterprise and government functions. It needs to use compute resources and that we often attribute to the data centers and indeed they will be there. But also we need to connect them. And here I think India comes out being very much in the pole position having a well over 90 % population coverage I’ve heard numbers even up to 99 % population coverage for 5G today.
And not only that, but truly well performing networks. That is a fundamental platform to drive innovation on and to drive innovation on together with AI. And looking then for the next five years to come, that indeed is what we will see. And the nations that in a few years time end of this decade are at the leading edge with the best of what 5G can do connecting the data centers with the AI applications in devices will be at the cutting edge and will have an advantage. And they are the ones having the least step into 6G. And again I think India is just in a supreme pole position to be there. The networks already today use AI.
They use that to a large degree but still I argue it’s only the beginning. We use it as the systems are being configured becoming more and more autonomous. The goal in the industry is still a good challenge but reachable is to reach what we call level 4 in TM Forum. By 2028 many mobile operators aspire to be there. and that’s beautiful, that’s very good but it is a big undertaking AI is being part to reach that level taking the next step I argue is what we really do with 6G, that’s to reach level 5 and be fully autonomous to cut the rope, cut the ties a bit and let it run and that we have in mind now as we gradually set the standard for 6G 6G shall be AI native and that will then be a baseline for bringing on the knowledge we’ve gained in 5G and to take the next step with 6G lastly then, networks for AI AI on the application side and here I argue it’s important to go society wide.
India is building its digital stack in an impressive way. Leverage AI in that. Industries come in, develop applications on top of it. We’ll drive efficiency locally and also will be export possibilities. So leverage the 5G systems with AI and cloud and then drive the way into 6G. Thank you.
Thank you. I got We at Qualcomm have been trying to really democratize AI and then try to see if we can translate AI to be resident on devices. These devices could be personal devices like phones, could be a laptop, could be your smart watches, your smart glasses, anything that you can think of. But doing that kind of AI inference on the edge is not easy, and especially when you want to go towards more personalized inference. I always joke with my colleagues that AI historically lacks common sense. How do you really translate that to something that’s meaningful to the user needs a lot of investment. In India, we’re seeing this significantly change. We’re seeing a lot of players putting a lot of focus, and how do we get it attached to the user and make a more important connection that drives a lot of these experiences.
At the same time, it’s also very critical of how do we look at coexistence between what runs on the cloud and what runs on the edge. It’s what we call a concept of hybridization. The concept of hybridization is the idea of a system that is built around the idea of a system that is built around the idea of a system that is built around the idea of a system that is built around the idea of a system that is built around the idea of a system that is built around the idea of a system that is built around the idea of a system that is built around the idea of a system that is built around the idea of a system that is built around the idea of a system that is built around the idea of a system that is built around the idea of a system that is built around the idea of a system that is built around the idea of a system that is built around the idea of a system that is built around the idea of a system that is built around the idea of a system that is built around the idea of a system that is built around the idea of a system that is built around the idea of a system that is built around the idea of a system that is built around the idea of a system that is built around the idea of a system that is built Hybrid AI, you know, working with our network operators is also not an easy concept.
It’s always been a challenge to understand, you know, which of these experiences would be transitioned towards the cloud, how do you make that decisions, and what runs on the edge. I think, you know, there’s a lot of research activity happening in this space, and India is definitely leading in this front. I totally expect in the next couple of, you know, months, we would see a strong transition where there’s a, you know, fundamental element of hybrid where you can have both edge and cloud coexist. And last but not
Thank you, Mr. Sukumar. I would like to invite Mr. Pasi from Nokia now.
Namaste. My sincere thanks for the opportunity to be here. It’s exciting. It’s exciting for the sake of this AI Summit, but it’s also very personal for me. I have been coming to the beautiful country of India since 1999, and it has been fascinating to see this country evolving all these years, over these years, in different locations. We were talking earlier today already in how many places I have been experiencing this evolution and development. It’s fascinating. So thanks for the opportunity to be here. It’s very important. I don’t know what to add after such a wonderful keynote by the chairman of the other opening talks. I think it is obvious that AI will also impact telecommunication networks, the network evolution, the business models, the innovation, everything around it.
So instead of going deeper in the technology discussion, because they were very well covered by the previous panelists, industry leaders, I would focus this couple of minutes for how. Not what. But how. How to capture the best of AI era. And for me, it comes around who is able to build the most value -rich, most welcoming, most compelling ecosystems. Who is able to gather like -minded technology players, regulators, government agencies to think together the opportunity, the risks, the challenges, that whole 360 of AI. I don’t believe that any player, even though they would be the smartest people on the planet, are able to… fully capture the 360 by playing alone. Ecosystem it is. How you are able to proactively define the overall value of this AI evolution.
And then transparently and proactively… agree how that value is distributed, how that value is maximized within that ecosystem. It might sound ideological. It might sound a little bit naive, but I’m a firm believer that it is key for success. It’s a key to capture and deliver all the opportunities within this AI era. It is the only way to address the security risks, which are now going to be different than any time earlier. I mean, we are going to have different access network. We are going to have richer number of applications, applications which are transferring more data, which increased amount of data is contributing for the security risks. We have to address that topic together, end to end.
And let’s do it. India can show the direction forward. For whole world. There is a tradition for great. collaboration, great innovation, so let’s do it. Thank you.
Thank you so much, Mr. Pasi. I would now like to invite Mr. Shantaram from Tejas.
Yes. Hi, good afternoon, everyone. Am I audible? Not audible? Now it’s okay. So my colleagues and my boss warned me that, you know, if you’re coming number four, then you may not have much to say, so think of something different. So there are a few things that I would like to say. A few things, you know, being from here, born, brought up, everything in India, making stuff for India, so I’ll try to, I’ll try to anchor my… few comments in terms of what we want to do here. I think the fundamental problem that, I don’t know if you happen to read this book by Sir C.K. Prahlad, where he talked about the economy at the bottom of the pyramid.
So we carry with us a responsibility of solving problems for the bottom of the pyramid, primarily. And Indian telecom especially has that additional responsibility of making sure that access is provided to, and at a very low cost, it is provided to pretty much everybody in the country. Now, in this context, if you want to accelerate the adoption of AI, I leave you with a framework of thought. This is what I use to basically figure out what it takes. So you can either be looking at cost, or you can be looking at revenue. Or both. So when telecom operators have to figure out what are they going to invest in, then these are the sort of the two guidelines or markers that they can use.
When it comes to cost, it is optimization of either the CAPEX or optimization of OPEX in a simplistic sense. OPEX would be operational efficiency. There’s a lot of literature which is available. And my friends from the global OEMs are far ahead in implementation of some of these. We in India are chasing it as well. In terms of the hardware, it leaves you with, again, two choices for architecture. Do you find a way to completely do an AI -first, AI -native architecture? Or should we find a path that has a bolt -on capability? Because, you know, you have equipment that’s already in the field, and there are a lot of investments that we are making, you know, very fresh.
unlike some of the more mature networks in the West where the capital cycle has already gone through you know 4g 5g and it’s it’s paid off over 5 10 years already but in India we have made fresh investments even as early as last year now that kind of equipment if it has to be leveraged for the next 10 years how do you deliver AI that is one challenge that we’ll have to solve so there will be a choice you know do you do AI first or do you do a bolt -on if you are looking at the revenue side of the equation again in simplistic sense there are two sort of big buckets there is a product enhancement where the telecom network itself is enhanced in terms of efficiency there’s a lot of work happening in 3gpp, bouth at 6G alliance, A lot of thought is going in in terms of how do you make, how do you bring in efficiency into the product, essentially optimizing the cost per bit, so to speak, and doing it in a way that is a lot smarter than what we did before.
And on the other side, you have a possibility of generating revenue by providing AI through the telecom network, which Pasi also kind of referred to. So that I see as a possibility of a two -way business model, which is an opportunity that’s available, where on one side you have all the users, you have communities, you have people in our villages, the farmers, etc., who are all on one side of it. And on the other side, you have startups, companies that are building models, companies that are building specific agentic applications. and in between you have the telecom player. And there’s a possibility if we do the frameworks right, and what do I mean by frameworks? The very top thing in terms of framework is trust.
There has to be trust, there has to be some amount of regulation, there has to be some amount of safety that comes with regulation. And allow people to dynamically upload, and it’s like an app store model. So the telecom network essentially becomes a platform where simple models, easy models can be actually uploaded and made accessible to all the users of the telecom network. It doesn’t have to be only the bottom of the pyramid, but of course the bottom and all the layers above. So I’ll leave you with this thought,
So thank you so much Mr. Shantaram for sharing your thoughts. Now for the next few minutes I will first have a round of questions with the panel here and then we will throw the session open for questions from the floor. So Mr. Magnus the first question is that we are talking of optimization with the AI but we always used to say that 4G, 5G networks are self -sorn. The concept of son was already there, self -optimization networks. So what does AI fundamentally change now?
Okay, thank you. Well, it’s always a journey. We are… For every move we make, we learn, we get more insights and then we take that and move further on. So in that sense, although we’re done… more and more autonomous parts of the systems in the past. It doesn’t say we can do more in the future. And we should do more, of course. Now, with AI, we get a very powerful tool to analyze a lot of data and to apply that knowledge onto a set of, in the system, some algorithms. And we’ve had some fantastic observations there. One was just the, in a very part that’s been optimized for decades on how we do the link adaptation, how we control the communication between the system and the device, we’ve managed to optimize the capacity by 10%.
So imagine a water… a loading spectrum of 100 megahertz. Then you had the equivalent of 110 megahertz. by applying this optimized algorithm. And that’s the cutting edge of what we can do today. I’m sure we can do improvements tomorrow also on that. How much? I don’t dare to state. In energy efficiency, as has been discussed there also, we, in another part of the system, applied AI analysis and make that part of the ongoing processing. And energy efficiency was optimized by 33%. For that part. And that’s an enormous savings applying that on a pan -India network, of course. So, again, it is about to, wherever you are, how far you’ve been, continue to do research to understand the future potential, and then step -by -step apply that with the knowledge that you have derived.
And you will continue to take steps and climb the ladder. Thank you.
so next question to you Mr. Sukumar so in the telecom hardware and software space which decisions should be pushed to the edge and what decisions should be centralized
it’s a great question by the way I think it’s really going to come down to elements of key performance indicators I would suppose if you’re looking at areas where you want to focus more on data privacy user privacy better responsiveness, better data management a predictable end to end performance to a large extent that’s going to be happening on the edge and this could look at experiences on data plane loops L1, L2 user kernel space operations anything to do with PII information or user privacy privacy, all that would be edge resident. And as you go more towards cloud, I would say the emphasis is going to be a lot more around fleet management, anything to do with AI, ML training as such.
There’s a concept which historically on the ML land was called MLOps from, you know, from training data all the way to inferencing and monitoring. We have a new model which looks at if there is areas there where things are breaking because of drift, how do we go fix that? That’s where I think cloud definitely helps. Now at the same time, it’s not very binary equation, which is one is edge, one is cloud. There’s also concepts of hybrid, where it’s going to be coexistence. As I was mentioning before in my talk, is that you have to find ways how edge can complement the cloud. And it could be elements of personalization, where you want to drive cloud.
What’s the edge? and you want to go towards more complex scenarios, then you position towards the cloud. A hybrid is in the very early stage. To a large extent, these days routers are very static in nature, meaning those workloads and experiences are predefined, saying X, Y, Z runs on the edge, you know, and A, B, C runs on the cloud. But the most difficult challenge has been is how do these routers can be intelligent enough when you happen to have a multi -turn conversation at some point of time positioned towards the cloud. I think that is something of a huge research topic, and I’m hoping in the next couple of months we’ll see some interesting results.
Thank you.
So the next question for you, Mr. Pasi. So I’m sure a lot of development is already taking with the telecom OEMs. Now, what are the decisions which are taken off -net? What kind of decisions do you expect will be taken off -net? Thank you. and what are the decisions during the operation of the network while using AI you think you would be taking?
Wow. And we have only this four minutes? Okay, okay. But on a serious note, I think jury is still out for this one. So how we are able to… And again, going back to my earlier comment, how we holistically address the topic, how we go through the overall value and that ecosystem and related functionality. I believe more will be done by the network itself. When we design it correctly, it’s able to do many of the security vulnerability assessments by itself. It’s… alerting upstream? Is it going to the edge or further? Let’s see. But I think it needs to be very intense dialogue between the network and edge. More decisions are traveling further to the regional data centers, more we are contributing to inefficiency and hence also the complexity.
So I would, my planning assumption is that I would push as much of the optimization automation to the network itself and then the limited cases to edge and less and less to the actual regional data center, to put it short.
Thank you for that. Shantaram, let me come to the ethical part of it. So let’s say we’re a base station serves urban area and also part of it serves the rural area. So how can we make sure that with the AI at the background the customer in a rural area is not deprived of the bandwidth vis -a -vis with respect to the urban area. So what kind of steps do you, I’m sure you will be looking at those things that the bandwidth is not constrained to an area or to a set of subscribers but what do you what are your thoughts? How can we check these things in a network?
Okay. So in just the easy answer is to buy more equipment. But But that’s a great question. It has always been, you know, this question has been live for many, you know, almost decades. I know we went through a case where, you know, we had net neutrality and those debates also happening. So it’s not different from those types of debates. I think while we look at access to AI, there is access to the central AI, right, which has to go through a backhaul capacity and so on. And obviously there one has to create different types of network slices for different types of use cases. And today’s technology, at least the way that we administer networks, it is quite possible to do that.
And it is possible to do that with single clicks. And with AI and us bringing in operational AI, it is, you know, it can even do it more efficiently. It can do it more efficiently without you having to think too much. you can just say that you know I need to create this kind of a bandwidth for this type of an application and the network management you know the assisted network management can actually go and do that for you. Now this coupled with having a lot of edge access for AI. So you know I’ll give I’ll share an example. In the US they recently launched an application where the telecom network can actually sense your voice metrics and identify you through that.
So imagine that kind of an application here in India where you know your identity etc. can actually be verified by the telecom operator not just by your number or by a digital information but by analog information that you are actually communicating and these types of applications can actually be launched. on the edge of the network so short answer step one try to have a lot of a GI and step to use lot more sophisticated network management capability to clearly separate out different types of
well thank you so much we can have a do the note that the times up but still we can have one or two quick questions yes mr.
good afternoon respected panelists it was a wonderful discussion but as I will try to keep myself short as I was hearing all that, we are having around 118 crores of mobile connections in India, built up over a huge network of say fiber and wireless and lead circuits and everything and once we are trying to introduce AI in that, as we know it’s not AI native, we have to build up in the form of external like apps and all and any single minute of disruption causes a huge resentment and the loss of the you know, all the time and resources, so how do we to actually progress from this 118 crores and we want to feed through the AI what is the vision in front of us that we can really carry forward from here, I will look for any of the panelists, thank you
Perhaps I can start thanks for it, it’s a wonderful question and sorry if I sound like a broken record but without thinking the network evolution end -to -end, you are not able to address it. So it comes to this ecosystem of players that you are able to model what the change is introducing to the network and optimize the network end -to -end. I think it is the only way. Otherwise, you are going to put patch fixes here and there based on certain application behaviors and you are not able to evolve the whole network.
Do you like to also substitute?
So I think essentially what you are saying is that it is a journey. And how do we sort of chart out that journey? There are two, three thoughts on this. I think one is that today we are telecom networks are mostly catering to human users. Of course there are enterprises etc. Three, four years, five years down the road I do expect AI users to be starting to dominate. So the business model and the regulation has to support that kind of an evolution. I think that is step one. We need to sort of think through what is, you know, how do we handle, so today we have 118 crores mobile phones. Five years from now we might actually have 500 crores of AI agents which are doing various things but they are still communicating either with each other or with central data repositories and so on.
And we need to basically figure out how do you charge for it? What is the, what is the economics of this? You know, how is it all going to work? Who is going to pay for all of that activity? So there is a lot of policy thought process that has to go in. On the physical side, we are building more and more and denser and denser fiber optics which are carrying 8 teras, 20 teras and so on and so forth. And in anticipation of something like this. So I don’t know if that answered your question but thank you so much.
Thank you. Thank you. Thank you.
Ms. Pallavi Mishra
Speech speed
56 words per minute
Speech length
601 words
Speech time
638 seconds
AI as a transformative catalyst
Explanation
Ms. Mishra emphasizes that AI is poised to become a game‑changing force for telecom, moving beyond current applications to reshape the industry’s future.
Evidence
“And as we look ahead, AI is all set to become even more transformative.” [1]. “Today, AI is transforming industries.” [2]. “This is the power of AI in telecommunication.” [8]. “We look forward to a thoughtful exchange on how AI‑driven innovations are being used in the future.” [12].
Major discussion point
AI as a transformative foundation for telecom
Topics
Artificial intelligence | Information and communication technologies for development
Shri Anil Kumar Lahoti
Speech speed
93 words per minute
Speech length
1023 words
Speech time
654 seconds
AI as a foundational capability & need for responsible governance
Explanation
Lahoti describes AI as a core capability that will shape network design, operation and user experience, and stresses that trustworthy, inclusive governance is essential for its adoption.
Evidence
“It’s a foundational capability shaping how networks are designed, operated and experienced by users.” [16]. “But it is the way we design, govern and deploy AI that will determine whether this future is trusted, inclusive and resilient.” [17]. “automated decisions taken by algorithms can affect millions of users simultaneously this makes trust the central pillar of AI adoption in telecommunication…” [18]. “The MANOV vision announced yesterday by the Honorable PM of India emphasizes a human‑centric framework for ethical, accountable, inclusive AI governance.” [20].
Major discussion point
AI as a transformative foundation for telecom
Topics
Artificial intelligence | Human rights and the ethical dimensions of the information society | The enabling environment for digital development
Demonstrated benefits of AI – energy savings, spam blocking, digital consent
Explanation
Lahoti cites concrete outcomes from AI deployments in telecom, such as large‑scale spam filtering, notable energy reductions and overall service quality improvements.
Evidence
“Due to the effectiveness of AI and blockchain‑based filtering operators are now flagging or blocking nearly 400 million suspected spam calls or messages each day.” [27]. “Operators are stating significant energy saving with use of AI.” [28]. “AI is already being deployed to optimize network performance, predict faults, improve energy efficiency, enhance customer experience, and combat fraud and spam communications.” [25].
Major discussion point
AI as a transformative foundation for telecom
Topics
Artificial intelligence | Environmental impacts | Building confidence and security in the use of ICTs
Trust and risk‑based regulatory framework essential for AI adoption
Explanation
Lahoti highlights the need for a risk‑based regulatory approach to ensure AI systems are safe, transparent and respect user rights while fostering innovation.
Evidence
“In July 2023, TRI issued recommendations on leveraging artificial intelligence and big data in the telecommunications sector, proposing a risk‑based regulatory framework for AI in telecom.” [85]. “automated decisions taken by algorithms can affect millions of users simultaneously this makes trust the central pillar of AI adoption in telecommunication…” [18].
Major discussion point
Governance, trust, ethics, and equitable access
Topics
Artificial intelligence | Human rights and the ethical dimensions of the information society | The enabling environment for digital development
Scalability – AI indispensable for India’s massive subscriber base
Explanation
Lahoti points out that with over 1.3 billion subscribers, AI‑driven automation is no longer optional but a necessity for managing the scale of India’s telecom networks.
Evidence
“With over 1 .3 billion telecom subscribers and over 1 billion data users, India operates telecom networks at a scale, where AI‑driven automation is no longer optional.” [35]. “At the same time, the scale at which AI systems operate is also increasing.” [99].
Major discussion point
Scalability and large‑scale deployment challenges in India
Topics
Artificial intelligence | Information and communication technologies for development
Shri Ritu Ranjan Mittar
Speech speed
119 words per minute
Speech length
743 words
Speech time
374 seconds
Technical challenges – compute intensity, access‑network evolution, handset impact
Explanation
Mittar notes that AI introduces heavy compute demands and raises questions about how it will affect access networks, core integration and handset capabilities.
Evidence
“So what does AI fundamentally change now?” [7]. “Another one thing with the AI is it is compute intensive.” [15]. “So once AI is going to come in on the handsets, what kind of a challenge it will throw to the network?” [40]. “The concept of son was already there, self‑optimization networks.” [69].
Major discussion point
Technical implementation and performance gains
Topics
Artificial intelligence | Capacity development
Ensuring equitable AI‑driven resource allocation between rural and urban users
Explanation
Mittar raises the need for AI to allocate bandwidth fairly so that rural customers are not disadvantaged compared with urban ones.
Evidence
“So how can we make sure that with the AI at the background the customer in a rural area is not deprived of the bandwidth vis‑a‑vis with respect to the urban area.” [90]. “So let’s say we’re a base station serves urban area and also part of it serves the rural area.” [91].
Major discussion point
Governance, trust, ethics, and equitable access
Topics
Closing all digital divides | Artificial intelligence
Magnus Ewerbring
Speech speed
125 words per minute
Speech length
764 words
Speech time
365 seconds
AI‑driven network autonomy roadmap
Explanation
Ewerbring outlines how AI provides powerful data analysis tools that enable networks to become more autonomous and self‑optimising.
Evidence
“Now, with AI, we get a very powerful tool to analyze a lot of data and to apply that knowledge onto a set of, in the system, some algorithms.” [6]. “The networks already today use AI.” [37]. “We use it as the systems are being configured becoming more and more autonomous.” [50].
Major discussion point
Technical implementation and performance gains
Topics
Artificial intelligence | Information and communication technologies for development
Quantifiable performance gains – +10 % capacity, –33 % energy use
Explanation
He provides specific metrics showing AI can increase capacity by 10 % while cutting energy consumption by a third.
Evidence
“And energy efficiency was optimized by 33%.” [44]. “…we’ve managed to optimize the capacity by 10%.” [45].
Major discussion point
Technical implementation and performance gains
Topics
Artificial intelligence | Environmental impacts
AI as a competitive advantage for India’s digital economy and export potential
Explanation
Ewerbring argues that AI‑enhanced networks will give India a strategic edge globally and open export opportunities.
Evidence
“We’ll drive efficiency locally and also will be export possibilities.” [49]. “And the nations that in a few years time end of this decade are at the leading edge with the best of what 5G can do connecting the data centers with the AI applications in devices will be at the cutting edge and will have an advantage.” [62]. “India is building its digital stack in an impressive way.” [114]. “And here I think India comes out being very much in the pole position having a well over 90 % population coverage…” [118].
Major discussion point
Business models, ecosystem collaboration, and value creation
Topics
Artificial intelligence | The digital economy | Information and communication technologies for development
Dr. Vinesh Sukumar
Speech speed
202 words per minute
Speech length
882 words
Speech time
261 seconds
Hybrid edge‑cloud architecture for AI inference
Explanation
Dr. Sukumar foresees a near‑term shift toward hybrid deployments where edge and cloud work together to run AI models.
Evidence
“I totally expect in the next couple of, you know, months, we would see a strong transition where there’s a, you know, fundamental element of hybrid where you can have both edge and cloud coexist.” [55]. “As I was mentioning before in my talk, is that you have to find ways how edge can complement the cloud.” [56]. “But doing that kind of AI inference on the edge is not easy, and especially when you want to go towards more personalized inference.” [57]. “Now at the same time, it’s not very binary equation, which is one is edge, one is cloud.” [58].
Major discussion point
Technical implementation and performance gains
Topics
Artificial intelligence | Building confidence and security in the use of ICTs
Edge AI for privacy, personalization and low‑latency decisions
Explanation
He highlights that privacy‑sensitive and latency‑critical workloads are best kept at the edge, where user data never leaves the local device.
Evidence
“if you’re looking at areas where you want to focus more on data privacy user privacy better responsiveness, better data management … that’s going to be happening on the edge … all that would be edge resident.” [61].
Major discussion point
Technical implementation and performance gains
Topics
Artificial intelligence | Building confidence and security in the use of ICTs
Mr Pasi Toivanen
Speech speed
118 words per minute
Speech length
687 words
Speech time
348 seconds
Network‑centric automation – push optimization to the network, not regional data centres
Explanation
Toivanen argues that AI‑driven decisions should be executed as close to the radio as possible, reducing reliance on distant data centres and improving efficiency.
Evidence
“I would, my planning assumption is that I would push as much of the optimization automation to the network itself and then the limited cases to edge and less and less to the actual regional data center, to put it short.” [66]. “More decisions are traveling further to the regional data centers, more we are contributing to inefficiency and hence also the complexity.” [67]. “Otherwise, you are going to put patch fixes here and there based on certain application behaviors and you are not able to evolve the whole network.” [71].
Major discussion point
Technical implementation and performance gains
Topics
Artificial intelligence | The enabling environment for digital development
Ecosystem collaboration as the ethical basis for capturing AI value
Explanation
He stresses that bringing together technology firms, regulators and governments is essential to realise AI benefits responsibly.
Evidence
“Who is able to gather like‑minded technology players, regulators, government agencies to think together the opportunity, the risks, the challenges, that whole 360 of AI.” [88]. “And then transparently and proactively… agree how that value is distributed, how that value is maximized within that ecosystem.” [94]. “Ecosystem it is.” [95]. “How you are able to proactively define the overall value of this AI evolution.” [96]. “And again, going back to my earlier comment, how we holistically address the topic, how we go through the overall value and that ecosystem and related functionality.” [98].
Major discussion point
Business models, ecosystem collaboration, and value creation
Topics
Artificial intelligence | The enabling environment for digital development
Mr Shantigram Jagannath
Speech speed
Default speed
Speech length
Default length
Speech time
Default duration
AI‑enabled network slicing to guarantee fair bandwidth distribution
Explanation
Jagannath explains that AI can dynamically create and manage network slices, ensuring that different use‑cases receive appropriate resources, thus supporting equity.
Evidence
“you can just say that you know I need to create this kind of a bandwidth for this type of an application and the network management you know the assisted network management can actually go and do that for you.” [92]. “And obviously there one has to create different types of network slices for different types of use cases.” [93].
Major discussion point
Governance, trust, ethics, and equitable access
Topics
Artificial intelligence | Closing all digital divides
Strategic framework – cost optimisation vs revenue generation
Explanation
He outlines a decision matrix where operators can either focus on reducing CAPEX/OPEX or create new revenue streams by offering AI services through the network.
Evidence
“So you can either be looking at cost, or you can be looking at revenue.” [105]. “When it comes to cost, it is optimization of either the CAPEX or optimization of OPEX in a simplistic sense.” [106]. “there are two sort of big buckets … product enhancement … efficiency … and … generating revenue by providing AI through the telecom network.” [107].
Major discussion point
Business models, ecosystem collaboration, and value creation
Topics
Artificial intelligence | The digital economy | The enabling environment for digital development
Telecom as an AI model marketplace / app‑store model
Explanation
Jagannath proposes that telecom networks become platforms where developers can upload AI models, turning the network into a marketplace.
Evidence
“And allow people to dynamically upload, and it’s like an app store model.” [110]. “So the telecom network essentially becomes a platform where simple models, easy models can be actually uploaded and made accessible to all the users of the telecom network.” [111].
Major discussion point
Business models, ecosystem collaboration, and value creation
Topics
Artificial intelligence | The digital economy
Trust and regulation as prerequisites for AI adoption
Explanation
He underscores that trust, regulation and safety are the top pillars that must accompany any AI rollout in telecom.
Evidence
“There has to be trust, there has to be some amount of regulation, there has to be some amount of safety that comes with regulation.” [86]. “The very top thing in terms of framework is trust.” [87].
Major discussion point
Governance, trust, ethics, and equitable access
Topics
Artificial intelligence | Human rights and the ethical dimensions of the information society
Audience
Speech speed
134 words per minute
Speech length
149 words
Speech time
66 seconds
Risk of service disruption when integrating AI into a 118 crore‑connection network
Explanation
The audience raises concern that introducing AI into India’s massive, non‑AI‑native network could cause even brief outages that would have huge repercussions.
Evidence
“we are having around 118 crores of mobile connections in India… once we are trying to introduce AI in that, as we know it’s not AI native, we have to build up in the form of external like apps… any single minute of disruption causes a huge resentment and the loss of the you know, all the time and resources…” [80].
Major discussion point
Scalability and large‑scale deployment challenges in India
Topics
Building confidence and security in the use of ICTs | Artificial intelligence
Mr. Shantigram Jagannath
Speech speed
138 words per minute
Speech length
1391 words
Speech time
602 seconds
Regulatory trust framework as foundation for AI rollout
Explanation
Jagannath stresses that any AI deployment in telecom must be underpinned by trust, clear regulation and safety measures, positioning these as the top‑most pillars before operationalising AI solutions.
Evidence
“There has to be trust, there has to be some amount of regulation, there has to be some amount of safety that comes with regulation.” [11]. “I think that is step one.” [13].
Major discussion point
Governance, trust, ethics, and equitable access
Topics
Artificial intelligence | Human rights and the ethical dimensions of the information society
Learning from net neutrality debates to shape AI governance
Explanation
He draws a parallel with the earlier net‑neutrality discussions, suggesting that the experience of those regulatory debates can inform the design of AI governance frameworks for telecom.
Evidence
“I know we went through a case where, you know, we had net neutrality and those debates also happening.” [12]. “I think that is step one.” [13].
Major discussion point
Governance, trust, ethics, and equitable access
Topics
Artificial intelligence | The enabling environment for digital development
Leveraging global OEM experience and literature to accelerate AI adoption
Explanation
Jagannath points out that many global original equipment manufacturers are already ahead in AI implementation and that a wealth of literature exists, which India can tap into to fast‑track its own AI‑enabled telecom initiatives.
Evidence
“And my friends from the global OEMs are far ahead in implementation of some of these.” [6]. “There’s a lot of literature which is available.” [7].
Major discussion point
Technical implementation and performance gains
Topics
Artificial intelligence | Information and communication technologies for development
India’s proactive pursuit and anticipation of AI‑driven telecom transformation
Explanation
He acknowledges that India is actively chasing AI in the telecom sector and is preparing for its impact, signalling a national commitment to integrate AI technologies.
Evidence
“We in India are chasing it as well.” [3]. “And in anticipation of something like this.” [5].
Major discussion point
AI as a transformative foundation for telecom
Topics
Artificial intelligence | The enabling environment for digital development
Agreements
Agreement points
AI is becoming foundational and intrinsic to telecom networks rather than just an add-on application
Speakers
– Shri Anil Kumar Lahoti
– Magnus Ewerbring
– Ms. Pallavi Mishra
Arguments
AI is becoming foundational to telecom networks, not just an add-on, with 6G networks being AI-native
Networks are progressing toward level 4 autonomy by 2028, with 6G targeting fully autonomous level 5 operations
AI in telecommunications enables networks that can self-heal, detect faults proactively, and deliver seamless connectivity to billions without interruption
Summary
All speakers agree that AI represents a fundamental shift in telecom architecture, moving from being an application layer to becoming intrinsic to network operations, with 6G networks being AI-native from the ground up
Topics
Artificial intelligence | Information and communication technologies for development
AI provides significant operational benefits including energy efficiency and network optimization
Speakers
– Shri Anil Kumar Lahoti
– Magnus Ewerbring
Arguments
Operators are achieving significant energy savings and have disconnected 2.1 million spam numbers through AI and blockchain-based filtering
AI enables significant improvements including 10% capacity optimization in link adaptation and 33% energy efficiency gains in network operations
Summary
Both speakers provide concrete evidence of AI’s current benefits in telecom operations, with measurable improvements in energy efficiency and network performance optimization
Topics
Environmental impacts | Artificial intelligence
Ecosystem collaboration is essential for successful AI implementation in telecommunications
Speakers
– Mr Pasi Toivanen
– Mr. Shantigram Jagannath
Arguments
Success in AI era requires building value-rich ecosystems with collaboration between technology players, regulators, and government agencies
Network evolution must be planned end-to-end to handle transition from 118 crore human users to potentially 500 crore AI agents in the future
Summary
Both speakers emphasize that AI implementation cannot be achieved in isolation and requires comprehensive collaboration between multiple stakeholders including technology companies, regulators, and government agencies
Topics
The enabling environment for digital development | Artificial intelligence
Security and trust are fundamental concerns that must be addressed in AI deployment
Speakers
– Shri Anil Kumar Lahoti
– Shri Ritu Ranjan Mittar
Arguments
Trust must remain central to AI adoption, with transparency, accountability, and consumer rights protection as core principles
Security concerns arise from AI being potentially used to attack telecom networks, requiring proactive defensive measures
Summary
Both speakers highlight that security and trust are not optional considerations but fundamental requirements for AI deployment in telecommunications, requiring proactive measures and strong governance frameworks
Topics
Building confidence and security in the use of ICTs | Human rights and the ethical dimensions of the information society
Similar viewpoints
Both speakers advocate for intelligent distribution of AI processing, with edge processing handling privacy-sensitive and latency-critical operations while minimizing reliance on distant data centers for efficiency
Speakers
– Dr. Vinesh Sukumar
– Mr Pasi Toivanen
Arguments
Edge vs cloud decision-making should prioritize data privacy, user privacy, and responsiveness for edge processing
Decision-making should be pushed to network level for security and optimization, with limited cases going to edge or regional data centers
Topics
Human rights and the ethical dimensions of the information society | Data governance
Both recognize the specific challenges India faces in implementing AI across its massive existing telecom infrastructure, emphasizing the need for careful transition strategies that minimize disruption while leveraging recent investments
Speakers
– Mr. Shantigram Jagannath
– Audience
Arguments
Indian telecom faces unique challenge of leveraging fresh equipment investments while implementing AI, requiring choice between AI-first architecture or bolt-on capabilities
Introducing AI into India’s existing 118 crore mobile connections presents significant disruption risks that need careful management during the transition
Topics
Artificial intelligence | Information and communication technologies for development
Both speakers view India as being in an advantageous position for AI implementation in telecommunications, with strong existing infrastructure providing a foundation for transformative AI applications
Speakers
– Magnus Ewerbring
– Ms. Pallavi Mishra
Arguments
India’s 5G coverage of over 90% population provides a strong platform for AI innovation and positions India advantageously for 6G transition
AI is transforming industries with humongous possibilities in telecommunications, from predictive network management to intelligent customer experiences
Topics
Information and communication technologies for development | Artificial intelligence
Unexpected consensus
Sustainability concerns in AI implementation
Speakers
– Shri Ritu Ranjan Mittar
– Magnus Ewerbring
Arguments
AI’s compute-intensive nature raises sustainability concerns that must be addressed in alignment with UN Sustainable Development Goals
AI enables significant improvements including 10% capacity optimization in link adaptation and 33% energy efficiency gains in network operations
Explanation
While one speaker raises concerns about AI’s energy consumption, another provides evidence of AI actually improving energy efficiency, creating an unexpected consensus that sustainability must be actively managed rather than being inherently problematic
Topics
Environmental impacts | Artificial intelligence
Risk-based regulatory approach for AI applications
Speakers
– Shri Anil Kumar Lahoti
– Dr. Vinesh Sukumar
Arguments
TRAI has implemented risk-based regulatory framework for AI in telecom, with stronger obligations for high-risk applications affecting consumers
Edge vs cloud decision-making should prioritize data privacy, user privacy, and responsiveness for edge processing
Explanation
The regulatory authority and technology company unexpectedly align on the principle that different AI applications require different levels of oversight and processing approaches based on their risk profiles and privacy implications
Topics
The enabling environment for digital development | Human rights and the ethical dimensions of the information society
Overall assessment
Summary
The speakers demonstrate strong consensus on AI being foundational to telecom’s future, the need for ecosystem collaboration, and the importance of balancing innovation with security and trust. There is agreement on India’s advantageous position and the practical benefits AI already provides.
Consensus level
High level of consensus with complementary perspectives rather than conflicting views. The alignment suggests a mature understanding of AI’s role in telecommunications and readiness for coordinated implementation across regulatory, technology, and operational domains.
Differences
Different viewpoints
Where AI decision-making should be located in network architecture
Speakers
– Dr. Vinesh Sukumar
– Mr Pasi Toivanen
Arguments
Edge vs cloud decision-making should prioritize data privacy, user privacy, and responsiveness for edge processing
Decision-making should be pushed to network level for security and optimization, with limited cases going to edge or regional data centers
Summary
Dr. Sukumar advocates for edge processing for privacy and responsiveness concerns, while Mr. Toivanen argues for keeping most decisions at the network level to avoid inefficiency and complexity
Topics
Artificial intelligence | Building confidence and security in the use of ICTs
Implementation approach for AI in existing telecom infrastructure
Speakers
– Mr. Shantigram Jagannath
– Magnus Ewerbring
Arguments
Indian telecom faces unique challenge of leveraging fresh equipment investments while implementing AI, requiring choice between AI-first architecture or bolt-on capabilities
AI enables significant improvements including 10% capacity optimization in link adaptation and 33% energy efficiency gains in network operations
Summary
Mr. Jagannath emphasizes the practical constraints of recent equipment investments in India requiring bolt-on solutions, while Magnus focuses on the benefits achievable through AI optimization without addressing implementation constraints
Topics
Artificial intelligence | Financial mechanisms | The enabling environment for digital development
Unexpected differences
Scale of future network users and business model implications
Speakers
– Mr. Shantigram Jagannath
– Other panelists
Arguments
Network evolution must be planned end-to-end to handle transition from 118 crore human users to potentially 500 crore AI agents in the future
Various arguments about current AI implementation and optimization
Explanation
Mr. Jagannath’s projection of AI agents potentially outnumbering human users by 4:1 within 5 years represents a dramatically different vision of network evolution that other speakers did not address, suggesting disagreement on the timeline and scale of AI agent proliferation
Topics
Artificial intelligence | Social and economic development | The digital economy
Overall assessment
Summary
The main areas of disagreement center on technical architecture decisions (edge vs network-level processing), implementation approaches for existing infrastructure, and the scale/timeline of AI transformation
Disagreement level
Moderate disagreement level with significant implications – while speakers agree on AI’s transformative potential and the need for responsible implementation, their different approaches to technical architecture and implementation could lead to incompatible solutions and fragmented ecosystem development
Partial agreements
Partial agreements
Both speakers agree on the need for comprehensive, end-to-end planning and ecosystem collaboration, but disagree on the specific approach – Toivanen emphasizes ecosystem value distribution while Jagannath focuses on business model and policy framework changes
Speakers
– Mr Pasi Toivanen
– Mr. Shantigram Jagannath
Arguments
Success in AI era requires building value-rich ecosystems with collaboration between technology players, regulators, and government agencies
Network evolution must be planned end-to-end to handle transition from 118 crore human users to potentially 500 crore AI agents in the future
Topics
The enabling environment for digital development | Artificial intelligence
Both agree on the importance of privacy and user protection, but disagree on implementation – Lahoti emphasizes regulatory frameworks and transparency while Sukumar focuses on technical architecture solutions through edge processing
Speakers
– Shri Anil Kumar Lahoti
– Dr. Vinesh Sukumar
Arguments
Trust must remain central to AI adoption, with transparency, accountability, and consumer rights protection as core principles
Edge vs cloud decision-making should prioritize data privacy, user privacy, and responsiveness for edge processing
Topics
Human rights and the ethical dimensions of the information society | Data governance
Similar viewpoints
Both speakers advocate for intelligent distribution of AI processing, with edge processing handling privacy-sensitive and latency-critical operations while minimizing reliance on distant data centers for efficiency
Speakers
– Dr. Vinesh Sukumar
– Mr Pasi Toivanen
Arguments
Edge vs cloud decision-making should prioritize data privacy, user privacy, and responsiveness for edge processing
Decision-making should be pushed to network level for security and optimization, with limited cases going to edge or regional data centers
Topics
Human rights and the ethical dimensions of the information society | Data governance
Both recognize the specific challenges India faces in implementing AI across its massive existing telecom infrastructure, emphasizing the need for careful transition strategies that minimize disruption while leveraging recent investments
Speakers
– Mr. Shantigram Jagannath
– Audience
Arguments
Indian telecom faces unique challenge of leveraging fresh equipment investments while implementing AI, requiring choice between AI-first architecture or bolt-on capabilities
Introducing AI into India’s existing 118 crore mobile connections presents significant disruption risks that need careful management during the transition
Topics
Artificial intelligence | Information and communication technologies for development
Both speakers view India as being in an advantageous position for AI implementation in telecommunications, with strong existing infrastructure providing a foundation for transformative AI applications
Speakers
– Magnus Ewerbring
– Ms. Pallavi Mishra
Arguments
India’s 5G coverage of over 90% population provides a strong platform for AI innovation and positions India advantageously for 6G transition
AI is transforming industries with humongous possibilities in telecommunications, from predictive network management to intelligent customer experiences
Topics
Information and communication technologies for development | Artificial intelligence
Takeaways
Key takeaways
AI is transitioning from an add-on to a foundational capability in telecommunications, with 6G networks expected to be AI-native
India’s extensive 5G coverage (90%+ population) positions it advantageously for AI innovation and 6G transition
Trust, transparency, and accountability must remain central to AI adoption in telecom, given the scale of impact on millions of users
TRAI has established a risk-based regulatory framework for AI in telecom, with stronger obligations for high-risk consumer-facing applications
AI is already delivering significant benefits including 10% capacity optimization, 33% energy efficiency gains, and blocking 400 million spam communications daily
Success in the AI era requires building collaborative ecosystems between technology players, regulators, and government agencies rather than individual efforts
Hybrid AI systems combining edge and cloud capabilities are emerging as the preferred approach for balancing performance, privacy, and efficiency
The telecom industry must prepare for a fundamental shift from serving primarily human users to potentially handling 500 crore AI agents in the future
Resolutions and action items
TRAI to continue working with stakeholders, industry, policymakers and international partners to ensure AI serves both innovation and public good
Industry to target level 4 network autonomy by 2028 with progression toward fully autonomous level 5 operations in 6G
Continued rollout of digital consent acquisition framework following successful pilot runs with banks
Development of more sophisticated network management capabilities to create different network slices for various use cases
Investment in denser fiber optic infrastructure carrying 8-20 terabytes to support future AI traffic demands
Unresolved issues
How to balance leveraging fresh equipment investments in India while implementing AI (AI-first architecture vs bolt-on capabilities)
Determining optimal decision-making distribution between network edge, regional data centers, and cloud infrastructure
Establishing economic models and pricing frameworks for AI agent communications and services
Ensuring equitable bandwidth distribution between urban and rural areas when AI optimizes network resources
Managing the transition from current human-centric networks to AI agent-dominated traffic patterns
Addressing security vulnerabilities that may emerge from AI-driven network attacks
Developing dynamic routing capabilities for hybrid AI systems that can intelligently switch between edge and cloud processing
Suggested compromises
Implementing a risk-based regulatory approach where low-risk AI applications use self-regulation while high-risk applications require stronger oversight
Using regulatory sandbox approach to enable innovation while maintaining public interest protection
Adopting hybrid AI models that balance edge processing for privacy/responsiveness with cloud processing for complex operations and fleet management
Creating sophisticated network slicing capabilities to ensure fair resource allocation across different user segments and geographic areas
Developing telecom networks as platforms (app store model) where AI applications can be dynamically uploaded while maintaining trust and safety frameworks
Thought provoking comments
In the upcoming 6G technology, AI will no longer be an application layer. It will be intrinsic. The telecom networks will be AI native… telecom networks are no longer mere data carriers, but these are central pillar of India’s AI infrastructure.
Speaker
Shri Anil Kumar Lahoti (TRAI Chairman)
Reason
This comment fundamentally reframes the relationship between AI and telecom networks from AI being an add-on service to being the foundational architecture. It shifts the paradigm from networks carrying AI applications to networks being inherently intelligent.
Impact
This set the conceptual foundation for the entire discussion, establishing that the conversation wasn’t about integrating AI into existing networks, but about reimagining networks as AI-native infrastructure. All subsequent panelist discussions built upon this fundamental shift in thinking.
automated decisions taken by algorithms can affect millions of users simultaneously this makes trust the central pillar of AI adoption in telecommunication efficiency gains cannot come at the cost of transparency, accountability or consumer rights
Speaker
Shri Anil Kumar Lahoti (TRAI Chairman)
Reason
This insight highlights the unique scale challenge in telecom AI deployment – unlike other sectors where AI affects individual users, telecom AI decisions can simultaneously impact millions. It introduces the critical tension between efficiency and accountability.
Impact
This comment shifted the discussion from purely technical considerations to ethical and governance frameworks. It established trust as a non-negotiable requirement and influenced later discussions about security, transparency, and responsible deployment.
Who is able to gather like-minded technology players, regulators, government agencies to think together the opportunity, the risks, the challenges, that whole 360 of AI. I don’t believe that any player, even though they would be the smartest people on the planet, are able to fully capture the 360 by playing alone. Ecosystem it is.
Speaker
Mr Pasi Toivanen (Nokia)
Reason
This comment challenges the traditional competitive approach in telecom by arguing that AI’s complexity requires unprecedented collaboration. It suggests that the AI era demands a fundamental shift from competition to ecosystem thinking.
Impact
This reframed the discussion from individual company capabilities to collaborative ecosystem development. It influenced the conversation toward shared responsibility and collective problem-solving, moving away from vendor-specific solutions to industry-wide cooperation.
So the telecom network essentially becomes a platform where simple models, easy models can be actually uploaded and made accessible to all the users of the telecom network… like an app store model.
Speaker
Mr. Shantigram Jagannath (Tejas Networks)
Reason
This introduces a revolutionary business model concept – transforming telecom networks from connectivity providers to AI platform operators. It envisions networks as marketplaces connecting AI developers with end users, particularly focusing on India’s ‘bottom of the pyramid’ population.
Impact
This comment introduced an entirely new revenue model and democratization framework that hadn’t been discussed before. It shifted the conversation from network optimization to network transformation as a platform business, influencing the final audience question about scaling from 118 crore connections to potentially 500 crore AI agents.
Three, four years, five years down the road I do expect AI users to be starting to dominate. So the business model and the regulation has to support that kind of an evolution… Five years from now we might actually have 500 crores of AI agents which are doing various things but they are still communicating either with each other or with central data repositories.
Speaker
Mr. Shantigram Jagannath (Tejas Networks)
Reason
This projection fundamentally challenges current assumptions about network users, suggesting a shift from human-centric to AI-agent-centric networks. It quantifies the scale of transformation (from 118 crore human users to 500 crore AI agents) and highlights the regulatory and economic implications.
Impact
This comment provided a concrete vision of the future that required rethinking everything from pricing models to network capacity planning. It elevated the discussion from current AI integration challenges to future-state planning and policy framework development.
The concept of hybridization… working with our network operators is also not an easy concept. It’s always been a challenge to understand which of these experiences would be transitioned towards the cloud, how do you make that decisions, and what runs on the edge.
Speaker
Dr. Vinesh Sukumar (Qualcomm)
Reason
This comment identifies a critical technical and architectural challenge that goes beyond simple edge vs. cloud decisions. It introduces the complexity of dynamic, intelligent routing of AI workloads based on context, user needs, and network conditions.
Impact
This deepened the technical discussion by highlighting that AI in telecom isn’t just about deployment location, but about creating intelligent systems that can dynamically optimize where processing occurs. It influenced subsequent discussions about network intelligence and decision-making frameworks.
Overall assessment
These key comments fundamentally transformed the discussion from a technical implementation conversation to a strategic reimagining of telecom’s role in the AI era. The Chairman’s opening comments established AI-native networks as the new paradigm, while subsequent panelist insights built layers of complexity around ecosystem collaboration, platform business models, and the transition from human-centric to AI-agent-centric networks. The discussion evolved from ‘how to add AI to telecom’ to ‘how to rebuild telecom as AI infrastructure,’ with each insightful comment adding dimensions of scale, trust, collaboration, and future vision. The conversation successfully bridged technical capabilities with policy implications, business model innovation, and societal impact, particularly emphasizing India’s unique position to lead this transformation while serving its diverse population pyramid.
Follow-up questions
How will AI-driven automation reach level 4 autonomy by 2028 and what specific challenges need to be overcome?
Speaker
Magnus Ewerbring
Explanation
This represents a significant industry goal that requires detailed roadmap and implementation strategies
How can hybrid AI systems intelligently route workloads between edge and cloud in real-time during multi-turn conversations?
Speaker
Dr. Vinesh Sukumar
Explanation
This is identified as a major research challenge that needs breakthrough solutions for dynamic workload distribution
What specific security vulnerabilities will emerge from AI-native networks and how should they be addressed proactively?
Speaker
Mr Pasi Toivanen
Explanation
Security risks in AI-enabled networks will be fundamentally different and require new approaches for protection
How can telecom networks ensure equitable AI service distribution between urban and rural areas?
Speaker
Shri Ritu Ranjan Mittar
Explanation
This addresses critical ethical concerns about AI access equality across different geographic and economic segments
What business models and regulatory frameworks are needed to support AI agents as dominant network users?
Speaker
Mr. Shantigram Jagannath
Explanation
The transition from human-centric to AI agent-dominated networks requires fundamental rethinking of economics and governance
How can existing telecom infrastructure be upgraded to AI-native systems without service disruption to 1.18 billion connections?
Speaker
Audience member
Explanation
This represents a massive technical and operational challenge requiring careful migration strategies
What are the optimal architectures for AI-first versus bolt-on AI implementations in telecom networks?
Speaker
Mr. Shantigram Jagannath
Explanation
Network operators need guidance on whether to rebuild with AI-native architecture or enhance existing infrastructure
How will 6G networks achieve full level 5 autonomy and what standards need to be developed?
Speaker
Magnus Ewerbring
Explanation
This represents the next evolution beyond current AI implementations and requires international coordination
What specific mechanisms will enable telecom networks to function as AI application platforms similar to app stores?
Speaker
Mr. Shantigram Jagannath
Explanation
This new business model concept requires detailed technical and commercial frameworks
How can voice biometric authentication be implemented securely across telecom networks while protecting user privacy?
Speaker
Mr. Shantigram Jagannath
Explanation
This emerging application raises important questions about privacy, security, and implementation at scale
Disclaimer: This is not an official session record. DiploAI generates these resources from audiovisual recordings, and they are presented as-is, including potential errors. Due to logistical challenges, such as discrepancies in audio/video or transcripts, names may be misspelled. We strive for accuracy to the best of our ability.
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