GermanAsian AI Partnerships Driving Talent Innovation the Future

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

GermanAsian AI Partnerships Driving Talent Innovation the Future

Session at a glanceSummary, keypoints, and speakers overview

Summary

The panel examined how global digital transformation, particularly artificial intelligence, can be leveraged for small and medium-sized enterprises (SMEs) in Germany, India and other economies, stressing an inclusive, human-centered future of work [1-3]. The moderator introduced the distinguished participants – Dr Bärbel Kofler from the German Ministry of Economic Cooperation, Mr Gobind Jaswal from India’s Ministry of Education, and Jan Noether of the Indo-German Chamber of Commerce [6-12]. Dr Kusumita Arora then framed the session around building talent partnerships, policy scaling and keeping people at the core of AI development [26-29].


Dr Kofler acknowledged public anxiety about AI-driven job loss and argued that these concerns must be taken seriously while positioning AI as a reliable partner for all, especially SMEs, by narrowing power and access gaps [36-43]. She cited the AI Living Lab launched at the University of Mumbai, which embeds AI into university curricula and links students with small media enterprises for practical experience [46-53]. Responding, Mr Govind Jaiswal drew a parallel with the introduction of electricity, asserting that AI can raise living standards if the transition is managed through education and vocational training; he highlighted India’s National Education Policy 2020, new research parks, and dual-education initiatives as concrete steps [69-84][85-94]. Augustus Azariah warned that graduates often lack genuine AI skills, describing efforts to certify faculty, run large hackathons, and extend training to tier-2 and tier-3 cities to unlock broader talent pools [115-124][136-144].


Jan Noether identified key sectors where AI can add value-healthcare, agriculture, energy and skills development-and announced a joint master’s programme with Baden-Württemberg universities that will split instruction between India and Germany [155-164]. Dr Kofler returned to the theme of responsible AI, emphasizing data bias, language exclusion, and the need for international cooperation to bridge creator-user gaps, align AI with the Sustainable Development Goals, and deliver concrete outcomes such as the Mumbai Living Lab [214-224][230-244]. Arthur Rapp cautioned against dependence on non-European AI platforms, highlighting data-privacy and sovereignty risks and urging transparent governance to ensure inclusive AI ecosystems [170-180][185-190].


The moderator introduced the AI Academia-Industry Innovation Partnership in Asia, a GIZ-implemented network of living labs that unites universities, businesses and governments to co-create AI solutions and address the skill shortage [287-293][312-321]. Participants agreed that cross-border sandboxes and collaborations can accelerate SME adoption, generate jobs and turn intent into measurable commitments, citing Germany’s vocational training model and GDPR as best-practice examples [268-276][279-282]. The discussion concluded that coordinated international efforts, especially between Germany and India, are essential to make AI accessible, responsible and a driver of inclusive economic growth [226-233][285-286].


Keypoints


Major discussion points


Inclusive AI adoption and the need to bridge the “power gap” – Panelists stressed that fears about AI-driven job loss are legitimate and must be handled carefully, while ensuring that both large corporations and small- and medium-sized enterprises (SMEs) can access and benefit from AI technologies.  The German ministry’s aim is to make AI “applicable, useful for everybody” and to close the existing power and creator gaps [36-44][45-53][56-58][220-227][222-223].


Education, skills development and “living labs” as the backbone of the AI workforce – Germany’s AI Living Lab at the University of Mumbai was presented as a concrete model for embedding AI in curricula and giving students hands-on experience with real-world SME projects.  India’s recent policy moves (National Education Policy 2020, new research parks, dual-degree programmes with German universities) were highlighted as parallel efforts to re-orient higher-education and vocational training toward AI-enabled workplaces [46-53][84-95][96-99][161-164].


Industry-academia collaboration models to up-skill talent – Both private-sector representatives described active programmes: faculty certification in tools such as Microsoft Copilot, large-scale hackathons, and the creation of “sandboxes” where students and companies co-create solutions.  These initiatives are framed as the practical engine of the AI Academia-Industry Innovation Partnership [132-139][275-277][312-321].


International cooperation and responsible AI governance – Participants called for joint standards to address data bias, language exclusion, and dependence on non-European AI platforms, arguing that coordinated commitments (e.g., the Hamburg Sustainability Declaration) are needed to translate conference rhetoric into concrete outcomes [215-218][170-179][230-238].


Specific focus on SME integration in Germany and India – Given that SMEs constitute > 98 % of businesses in both countries, the discussion highlighted the need for low-risk, cost-effective AI pilots, cross-border talent exchanges, and joint sandbox environments to make AI adoption viable for these firms [268-272][275-277].


Overall purpose / goal of the discussion


The session was convened to explore how governments, industry, academia, and development partners can cooperate to make artificial intelligence an inclusive driver of economic growth.  Key objectives included (i) addressing workforce anxieties, (ii) building AI-ready talent pipelines through education and living-lab programmes, (iii) establishing concrete partnership models that link SMEs with research and training institutions, and (iv) shaping international governance frameworks that ensure equitable, responsible AI deployment.


Overall tone and its evolution


The conversation began with a formal, policy-oriented tone, emphasizing strategic priorities and the need for cooperation [1-5].  As panelists entered, the tone shifted to a more explanatory and optimistic register, highlighting concrete initiatives (living labs, curriculum reforms) and sharing success stories [46-53][84-95].  When discussing challenges such as job-loss fears, data bias, and dependence on foreign platforms, the tone became cautiously critical, underscoring risks that must be mitigated [36-44][170-179][215-218].  Towards the end, the tone returned to constructive optimism, focusing on actionable partnership models, commitments, and a forward-looking call to translate intent into measurable outcomes [275-277][312-321][293-304].  Overall, the discussion remained collaborative and solution-focused, with brief moments of concern that were quickly reframed as opportunities for joint action.


Speakers

Arthur Rapp


– Role/Title: Representative of the German Academic Exchange Service (DAAD)


– Area of Expertise: Academic research and international education programs [S1]


Mr. Jan Noether


– Role/Title: Director General, Indo-German Chamber of Commerce


– Area of Expertise: Indo-German economic and business cooperation [S2]


Dr. Kusumita Arora


– Role/Title: Moderator/Chair of the panel discussion (as introduced in the transcript)


– Area of Expertise:


Mr. Govind Jaiswal


– Role/Title: Joint Secretary, Ministry of Education, Government of India


– Area of Expertise: Higher education and skills development [S7]


Moderator


– Role/Title: Session moderator for the conference [S8][S9]


– Area of Expertise:


Dr. Bärbel Kofler


– Role/Title: Parliamentary State Secretary to the Federal Ministry of Economic Cooperation and Development (Germany)


– Area of Expertise: International development policy, AI governance and cooperation [S11][S12]


Dr. Augustus Azariah


– Role/Title: HR Leader for Asochem; works for Kindrel (IBM spinoff)


– Area of Expertise: Infrastructure management, industry-academia collaboration [S13]


Video Narrator


– Role/Title: Narrator of the promotional video


– Area of Expertise:


Additional speakers:


Mr. Gobind Jaswal


– Role/Title: Joint Secretary, Ministry of Education, Government of India (introduced in the opening remarks)


– Area of Expertise:


Mr. J. J. Stahl


– Role/Title: (remarks requested by moderator; specific title not provided)


– Area of Expertise:


Mr. Yan


– Role/Title: (addressed in the final Q&A; specific title not provided)


– Area of Expertise:


Full session reportComprehensive analysis and detailed insights

The moderator opened the session by noting that global digital transformation has made AI a central strategic priority for partner economies, notably Germany and India [1] add correct citation. Dr Kusumita Arora then framed the panel’s task: to discuss how governments, industry, academia and development partners can cooperate to make AI deployment innovative, inclusive and human-centred [2] add correct citation.


The panelists were introduced as follows: Dr Bärbel Kofler, Parliamentary State Secretary to the German Federal Ministry for Economic Cooperation and Development; Mr Govind Jaiswal, Joint Secretary at the Indian Ministry of Education; Jan Noether, Director General of the Indo-German Chamber of Commerce; and Dr Augustus Azariah, industry representative from Kindrel/IBM spinoff [3-6] add correct citation.


Dr Kofler addressed public concerns about AI-induced job loss, stressing that AI should be seen as a reliable partner and that the existing “power gap” must be closed so the benefits of new technology can be widely shared [7-9] add correct citation. She highlighted Germany’s commitment to “open source, open data”, climate-friendly computing, and regulatory frameworks that reduce energy and water consumption while ensuring responsible AI deployment [10-12] add correct citation.


A concrete illustration of the inclusive-AI approach is the AI Living Lab launched at Rattentata University (University of Mumbai). The Lab embeds AI modules into university curricula, giving students hands-on experience with real-world projects supplied by small media enterprises that would otherwise lack access to AI tools [13-18] add correct citation. This model exemplifies German-Indian collaboration that bridges the creator-user divide [19-21] add correct citation.


Mr Govind Jaiswal expanded on systematic reskilling, likening AI’s disruptive potential to the historic introduction of electricity and arguing that, if managed through education and vocational training, AI can raise living standards for marginalised workers [22-24] add correct citation. He cited India’s National Education Policy 2020, the creation of six new research parks at premier institutions, a dual-education system that combines classroom learning with mandatory apprenticeships, and recent budget-driven initiatives to establish five “educational cities” linked to industrial corridors [25-31] add correct citation.


Dr Augustus Azariah warned that many fresh graduates submit AI-generated CVs that lack genuine competence, reflecting a gap in faculty expertise. To address this, his organisation has certified over a thousand faculty members in tools such as Microsoft Copilot through large-scale hackathons involving more than 18 000 students, and is establishing endowment funds to enable faculty to develop AI-driven research and patents [32-38] add correct citation. He emphasized that while generative AI can produce draft material, human oversight is required to ensure originality and relevance [39-40] add correct citation. He also highlighted untapped talent in India’s tier-2 and tier-3 cities, noting a blind-selection hiring exercise that identified successful candidates from both IITs and smaller cities [41-45] add correct citation.


Jan Noether turned to sectoral opportunities, identifying healthcare, agriculture, water management, energy, and digital skills development as key domains where AI can generate sustainable impact [46-48] add correct citation. He announced a joint master’s programme with the University of Baden-Württemberg, with two-thirds of instruction delivered in India and one-third in Germany, exemplifying cross-border academic cooperation [49-51] add correct citation. He also advocated “sandboxes” that bring together young talent from both countries to co-create AI solutions for SMEs, stressing that such collaborative environments are essential for translating research into market-ready innovations [52-54] add correct citation.


Arthur Rapp cautioned that reliance on non-European AI platforms creates strategic vulnerabilities, including data bias, language exclusion and the risk that today’s free tools could become costly or inaccessible tomorrow, potentially compromising research confidentiality and intellectual-property security [55-58] add correct citation. He underscored the need for transparent governance and data-sovereignty safeguards [59-61] add correct citation.


Re-emphasising the border-less nature of AI, Dr Arora noted that AI “does not know any borders” and called for international programmes that embed AI skills across all education levels, ensuring an inclusive global workforce [62-64] add correct citation. She asked the panel to consider how cooperation can translate intent into concrete outcomes, particularly for SMEs [65-66] add correct citation.


The moderator then introduced the AI Academia-Industry Innovation Partnership in Asia, a GIZ-implemented network of Living Labs involving India, Germany and Vietnam[67-69] add correct citation. A video explained that the current challenge for companies is not access to technology but access to people with AI-ready skills, and that Living Labs provide structured spaces where students work on real industry problems, companies test innovations, and faculty strengthen curricula through direct engagement [70-74] add correct citation.


Across the discussion, the panel agreed on several points: inclusive AI for SMEs and the broader workforce is essential; bilateral Germany-India cooperation (and its extension to other Asian partners) is a cornerstone for scaling AI; capacity development through curricula, Living Labs and faculty up-skilling is critical; responsible AI governance must tackle bias, data-privacy and environmental impact; and Living-Lab-type sandboxes are the preferred mechanism for bridging academia-industry gaps [75-80] add correct citation.


While no overt conflict was expressed, the speakers displayed different emphases: Jan Noether highlighted the need for clear efficiency and cost-benefit evidence for SME adoption, whereas Dr Kofler stressed that policy should ensure AI accessibility for SMEs irrespective of immediate ROI [81-83] add correct citation. Arthur Rapp focused on platform-specific strategic risks, while Dr Kofler’s emphasis on open source and open data addressed broader collaborative frameworks [84-86] add correct citation. Finally, Mr Jaiswal foregrounded government-led skill development (NEP 2020, dual-education, research parks) and Dr Azariah highlighted industry-driven faculty certification and hackathon programmes [87-92] add correct citation.


In concluding remarks, Dr Kofler announced the launch of the AI Academia-Industry Innovation Partnership in Asia, noting that it aims to bridge the gap between the 1.3 million AI-related job opportunities identified by a World Bank/World Trade Organisation study (source uncertain) and the current shortage of skilled workers [93-95] add correct citation. Mr Jaiswal expressed confidence that the Living Lab will successfully align academic training with industry needs [96-98] add correct citation. The video narrator summed up the initiative’s ambition to develop AI-ready skills, support cross-border innovation and create a vibrant AI ecosystem that benefits both German SMEs and Asian partners [99-103] add correct citation. The moderator concluded by urging the panel to translate commitments into measurable actions and to report progress in follow-up meetings [104-105] add correct citation.


Four pillars emerged for realising inclusive AI: (i) addressing SME-specific concerns and closing the power gap; (ii) deepening Germany-India (and broader Asian) cooperation through joint programmes and sandboxes; (iii) implementing systematic capacity-development pathways-from elementary education to university curricula, faculty certification and Living Labs; and (iv) establishing responsible AI governance that mitigates bias, protects data sovereignty and aligns AI deployment with the Sustainable Development Goals, as reflected in the Hamburg Sustainability Declaration[106-108] add correct citation. These pillars capture the shared vision while recognising the nuanced emphases each speaker brought to the discussion.


Session transcriptComplete transcript of the session
Moderator

global digital transformation for partners such as Germany and India. The strategic priority is not longer solely the development of artificial intelligence, but very much its response limit effective deployment. And particularly for small and medium -sized enterprises, which in Germany and in India and other countries, these are the backbones of our economies, access to skills, innovation, ecosystems and trusted partnerships will determine whether AI becomes a driver of opportunity for all. Today’s panel will explore how cooperation amongst governments, industry, academia and development partners can address these challenges and shape a future of work that is innovative, inclusive and human -centered. It is now my great pleasure to introduce our distinguished panelists. We are deeply honored by the presence of Dr.

Bärbel Kofler, Parliamentary State Secretary to the Federal Ministry of Economic Cooperation and Development, whose leadership underscores Germans’ strong commitment to international cooperation and sustainable development. Ms. Kofler, please come up. There’s no signs. You can choose in the middle. Next panelist, I would really warmly welcome Mr. Gobind Jaswal, Joint Secretary at the Ministry of Education of the Government of India. He plays a very pivotal role in advancing higher education and skills development here in India. Also, it’s my great pleasure to welcome Jan Noether, the Director General of the Indo -German Chamber of Commerce, reflecting the strength of Indo -German economic and business cooperation. Please, Jan. Jan Noether, the Director General of the Indo -German Chamber of Commerce, reflecting the strength of Indo -German economic and business cooperation.

of Indo -German economic and business cooperation. Jan Noether, the Director General of the Indo -German Chamber of Commerce, reflecting the strength of Indo -German economic and business cooperation. Please, Jan. Jan Noether, the Director General of the Indo -German Chamber of Commerce, reflecting the strength of Indo -German economic and business cooperation. Please, Jan. Jan Noether, the Director General of the Indo -German Chamber of Commerce, reflecting the strength Please, Jan. Jan Noether, the Director General of the Indo -German Chamber of Commerce, reflecting the strength of Indo -German economic and business cooperation. Please, Jan. of Indo -German economic and business cooperation. Please, Jan. Jan Noether, the Director General of the Indo -German Chamber of Commerce, reflecting the strength of Indo -German economic and business cooperation.

Please, Jan. Jan Noether, the Director General of the Indo -German Chamber of Commerce, reflecting the strength Please, Jan. Jan Noether, the Director General of the Indo -German Chamber of Commerce, reflecting the strength Jan Noether, the Director General of the Indo -German Chamber of Commerce, reflecting the strength Today’s discussion will be moderated by

Dr. Kusumita Arora

Good morning. Good morning, everybody. Thank you to GIZ for this very special and important session. So we have been hearing, I think, about all aspects of AI in the last few days. And today… Close up? Okay. Okay, I will be, wait one minute. Okay. So this session, we want to talk about people in the age of AI and what partnerships are going to look like for talent, which is going to drive the innovation, and also completely the future of work as we know now. This forum where we are going to discuss policy intent, what is required for scaling, for startup needs, for infrastructure and other pragmatic issues which are going to drive the conversation ahead.

This includes and always have to include the people, personal growth, their dreams and their particular circumstances through which they will connect to AI and to each other. I would request all our panelists for their comments on the different issues. To Dr. Babel Kofler, I will ask you to just explain your views. Thank you. cooperation, public policy support between AI partnerships in industry, academia, as well as technology providers, how will this drive productivity and drive jobs? Because people are scared of the jobs.

Dr. Bärbel Kofler

Well, you’re quite right. Also, with your last remark, thank you for the question, and good morning, everybody. Start with that. Good morning, everybody, to all of you. Yes, people get afraid of that there might be a loss of jobs, and it’s also an issue then, and maybe we come later to that issue also, on how decent the work jobs are they can require. I think we have to take those feelings very carefully because there’s reason for that. I think we dive a little bit deeper in that in the next round. What we are doing, and I will start a little bit with a general remark, what we are doing is First thing is with our cooperation, we try to be a reliable partner in a very uncertain world.

We all know how power shifts around the globe are taking place, how the international order is redrawn somehow. And I think what we need, especially if it’s coming to technological transformation, which is really having a big impact on everybody’s life, we need to make sure that that technology, included in all the other changes which are going on on the planet, is really there for serving people, serving those who are in the workforces, serving enterprises, serving not only big enterprises, but as we are jointly thinking, small and medium -sized enterprises. Because only if we do that, if we overcome that power gap, which is still existing, the full… possibility of new technology can be spread and can be used by everybody.

And I think that’s the aim and the goal of my ministry and that’s the aim of the goal of the German government to make the new technology being applicable, useful for everybody. And I think we are very aligned with that with the Indian government, so I’m very happy that colleague, General Secretary, is here on the panel with us because at the end of the day we are discussing about open source, open data, we are discussing about computing possibilities about how we can make that all more climate friendly, reducing the costs of energy, reducing environmental impact, the use of water for example which is necessary for all those computing things there. So there are a lot of things we have to regulate, I would say, in the overall governmental framework.

to make it then being applicable in a very positive way for the people, for their companies. One concrete example of what we are doing is just coming back from Mumbai. We also met Mr. Newton and where we were opening AI Living Lab at University, Rattentata University at Mumbai. What is it all about? At the end of the day, it’s about making the new technology being part of a curricula of a university, offering students the chance to get close to that, but not doing that in something artificially made up, doing it with concrete working examples from small media enterprises who get the advantage then to have access to AI, which is also not always there. So bring those two groups.

Those groups who normally don’t have so much access. as a creator of AI, but sometimes, yeah, you may use it. You have to have GDP on your mobile, but not really as a creator and as somebody who is inventing the solutions which are needed in business, which are needed also for social interaction. Bring that together. That’s something we are doing as government, and I think that’s something we can talk a little bit more about it later on, but that’s something we want to foster in a global cooperation and in an overall momentum, we really strive to close the power gap. I was talking in another panel about the chances on getting access to competing data centers.

That’s totally different in the global north than in the global south. The access to venture capital, there are so many things surrounding the setting, but also then at the end of the day, how are the regulations on decent work, for example? So people are really suffering from that or are really participating. So those things are the overall topics we have to solve in government. Thank you.

Dr. Kusumita Arora

I will move to Sri Jeshwar, Joint Secretary. I think many universities, departments are already starting AI courses or some centers or departments. So how do we plan to have higher education and vocational training systems orient to or reorient to work closely, not only on the courses, but along with industry and innovation ecosystems so that workers, the graduates who are coming out of these systems are prepared for AI -enabled workplaces, because having the talent pool is one of the priorities. I think that is one of the priority areas nowadays. Thank

Mr. Govind Jaiswal

you. I’ll start. With some context of the first question, then I will link how are we preparing. Most of the time it has been asked about this afraid about the introduction of new technology. I’ll give one example because there are many person who might be interested into AI as a layman and they may not be aware. Any technology, whenever it comes, it creates disruption in the ecosystem. I’ll give one example. When electricity was introduced, discovered long time back, you’re just imagining one person who was manually doing the work of a fan for some elite or some rich person. When the electricity was introduced, the same kind of question might have arised whether he will lose his job or not.

But what happened, the electricity, when it was reduced, the consequence of that fan, freeze, the vehicle, everything, the electronic batteries, everything came into existence. But what technology does, if it has been used effectively, it ensures the person who never thought that he will have access to a fan and he will get fan, which he is doing manually after one or I think one or two centuries, he might be using the same thing. The quality of life, especially marginal people, increases with any technology. What is the role of government and the industry to ensure that when the transition takes place, they are effectively and efficiently trained for new skills and a new job role? I am 100 percent sure a person who was doing that kind of job a few centuries ago, after a few decades, he might be doing a better job and a new kind of thing.

That’s the challenge, actually. And when you said about the university and the ecosystem and the introduction of vocational training and the introduction of AI courses, we are keeping that in the mind because the transition which took place in one century that few centuries ago will take in few decades. So that transition it has to be very seamless. So no one is adversely affected. And any technology emotional agnostic it will not go with the emotion. It go with the hard core reality. So in government of India is already taking many steps to ensure that everyone is being trained about the new skills including AI. And in the last six seven years it has been introduced after the new education policy 2020 where we have enabled all the university ecosystem especially humanities also to include 50 % courses especially for the skill courses.

And it’s a very very organized and very very structured way that we are moving in last five to eight years especially last one decade. We introduced national education policy where focus on skill courses. We started six new research parks in the premium institution, especially in IITs. Before 2014, it was three. So now it is nine. We are still going for another nine. So, and all the courses of the civil engineering, mechanical engineering, we have introduced a certain component where the students who are getting through the, they are also equally trained with AI. If you see about the industry academy of collaboration, the recent budget also, where it was announced, five educational cities, the core word was it should be near to the industrial corridor.

It is so curated and if you go for the last ten years, it is so organized way that every student of this country is getting equipped with artificial intelligence. Not only this, either it is semiconductor or it is quantum theory. Everything we are trying to train our human resources equally. as we are also in negotiation and we are already having some cooperation with our German government for the introduction of AI courses and it has been launched also in some of our portal slowly slowly we will embed everything what I suggest with the industry academy of collaboration most of the time it was confined with the curriculum thing we are going ahead and we are requesting not only to involve for the curriculum they should be involved for the entrance they should be involved for the assessment they should be involved for the practical training as much as possible last month I was in Germany actually Stuttgart and Munich I have seen the dual education system very influential very effective way and we are also going aggressively to ensure that every student gets industry exposure internship was made mandatory apprenticeship embedded degree program was launched entire education landscape is changing drastically we have series of activity that we are doing.

And I’m very much sure that students, especially in higher education, we have around 40 million students enrolled and they will be equipped and they are equipped in the coming year. We will lead into AI sector also. Thank you.

Dr. Kusumita Arora

Thank you. That’s very encouraging. Very hopeful. Mr. Azaria, from the point of view of the Indian industry, what would be your comments as to what kind of partnership or collaboration models already exist? And how do you see the students coming into the young workforce and turning AI innovation into real productivity improvements and improvements for companies for the bottom line as well as for sustainability? Thank you

Dr. Augustus Azariah

very much. Truly delighted to be here this morning. Quick commercial about me and my company. All right. I work for Kindrel, which is an IBM spinoff, and we’re in the space of infrastructure management, which means that most of the transactions that you’re doing, banking to airline to various other things, are powered by our technology. That’s my day job. I also serve as the HR leader for South, for Asochem, and that’s the Industry Connect. Now, coming to your question, as I was riding in and my cab driver, I chatted up with him and asked him, what is it about this AI conference? AI, sir, AI means all Indian. Wonderfully put. Not to take the credit away from other countries, but you see.

At that level, the penetration. and the hope that AI sovereignty could happen right here where we are sitting. So that is from that level. Now, the other one I wanted to tell you is the industry -academia collaboration. And as a HR leader, the first thing I see is that there is some chaos and confusion among the laterals as well as the freshers. When you go to campus to hire, you don’t find real AI skills except that you see the CV developed by ChatGPT. And when I read through that verbosity, I know very well this is system -generated. This is AI -generated. And I tell them, look, we need some levels of originality. You get your ChatGPT or whatever, Gen AI.

The AI system wants to generate your stuff. But I want your involvement. So which means that the human element that we want to put here is to oversee what is actually being generated by generative AI. The other requirement from the pressures is that college freshers we’re talking about is not just an awareness of what’s Gen AI. They know that. But for them to know certain productivity tools like Copilot, OK, to use Copilot to develop small applications or, you know, have AI agents running. That’s the level. And I’m not saying that you can’t do that. And I would say that’s pretty basic. Is it available today for the industry? The answer is not as much as it should be.

Why? Because the faculty are not trained to impart that level. of AI awareness. And therefore, we saw this gap. And we said, hey, look, let us address this gap and go into colleges. The industry goes into colleges with partnerships, with large companies, call it NVIDIA, call it Google, call it IBM, call it Kindle. And we go beyond the guest lecture. We start with making it competitive to them. All right. And telling the faculty and certifying them. Recently in a hackathon with about 18 ,000 people in the southern city of Mangaluru, there were more than 18 ,000 students. And during that time, we got more than a thousand faculty certified in Copilot. And. I would say that our target is to go into the hundreds and thousands and millions for faculty to be trained and also to provide faculty.

an endowment fund so that they can innovate and they can come up with models and patents that they can file. And I suppose that is where we have a big gap. And if we are able to do it, we are going to the hinterlands, tier two, tier three cities in India. And that’s where the talent lies. And AI, while you can call it all India, it’s also about unlocking talent. The talent that is available in tier two and tier three cities is so humongous I will just take 30 seconds and tell you. When we did a hiring, we did what is called a blind selection. And in that blind selection of 10 people who were shortlisted or finalized for a job that was paying close to 30 lakhs per annum, which is for freshers.

Four of them were IITs. three of them were tier two tier one the rest were all tier two and three what does this tell me this tells me that the talent doesn’t just stay in our top tier institutes it’s also so common and it’s socialized right across the spectrum and that my dear friends is the challenge that we have the opportunity that we have and I think today it’s about making sure that they unlearn the past and learn about how to cope with AI for the

Dr. Kusumita Arora

that is a real wonderful to know I mean this is an example or demonstration of how industry is really engaging with academia and engaging on a long term basis at least a medium term basis and I’m sure this is going to yield results at the pace that industry and academia is looking at. Mr. Jan, can you please tell us where you see the strongest potential for cooperation in AI? And this translates directly into productivity, into gains, economic growth.

Mr. Jan Noether

Glad to do so. Now, of course, we need to bring people together. We yesterday had a tour around our German pavilion. And it’s amazing what’s going on in Germany as well when it comes to AI. So all India is great, but AI does not know any borders. And we need to bring people together. Now, when we talk about application, looking at India, the first thing which comes to my mind is healthcare. since if you look into a let’s say analysis of these millions and millions of records of we have of healthcare data if we look into disease management if we look into remote access to patients via AI systems that is going to be the future not only in India that’s going to be the future I mean across the world agriculture digital imaging satellite imaging and water is unfortunately not only used to cool systems water is the scarce raw material if you want to on our planet in the years to come how do we use that in a meaningful way and how do we protect this resource and how do we look at the agricultural development in certain areas.

So all of that could be done. Energy sustainability, very, very important. And AI will play a very crucial role in that segment, as does in the skills development, remote learning. We do have, and Secretary, I’m very happy to share with you, you were in Stuttgart, which is fantastic. We just signed an agreement with a dual university of Baden -Württemberg on a master’s program where like two -thirds is going to be handled in India, one -third is going to be handled in Germany. But these are the concepts of the future. So if you ask me where to apply, it’s across the board. It’s

Dr. Kusumita Arora

Okay. Thank you. Mr. Ross. I came up as a representative of DAAD, which has been supporting academic research for decades. What would be your comments as to how our programs would, on research, would integrate AI skills, new directions in AI, right from maybe schools and greater into universities and, in fact, lifelong learning, to equip leaders, learners with skills, the critical thinking, to use AI for their personal uses as well as to drive the economy? What would be the role in this direction?

Arthur Rapp

So the second study – oh, sorry, one important point, one very important conclusion was that there is a big risk of dependence on non -European AI platforms, and this is a threat to freedom of research and teaching. Now, this is, of course, very much centered on Europe, but this is also something that, of course, applies to India as well. When we use certain systems and the owners of these systems, they are not in our countries. They are somewhere else. There are people training this AI, you know, so AI is not neutral. It’s also biased. This is another interesting aspect, you know. Today, maybe this application is free of charge, so I’m using it. I’m putting my data inside, so at the same time I’m training that.

Tomorrow, this application might not be available anymore, and then me as a country or me as a company, I will get into trouble, right? Because suddenly I’m… Maybe I need to pay for something, and I can be excluded. But the whole aspect of data protection is also mentioned in that study because there was a lot of questions. For example, when people today write a research proposal and they use AI just to check the spelling, you know, and to make the sentences a bit more polished so it sounds nicer, right? They don’t understand, I think, the impact it has because where does this data go to? I might have a new great idea, right? This might be a revolution.

So it could be that someone has access to this, will extract this information at another end of the world and might use this, might even file a patent, right? So we don’t know that, right? So there’s also this dimension. So another study, another publication that was published that’s called University Student and Generative. If I am parroted. that’s about two years old already but I would say it’s still important and what I very much liked about it is that there’s basically three messages so I don’t know if you would be surprised but they found out a lot of young people today consult AI on their career choice on their choice of university and the subject I’m going to study so I don’t ask anymore maybe my teacher or my aunt I consult AI and then I take the decision what career I’m going to choose and then again not a big surprise four out of five people use AI so this is two years ago I’m sure this number is a lot higher today and another interesting fact is engineering used to be number one among the people asked and today it’s computer science and information systems so So this is where the tension is going now, of course, because people see there is an opportunity, right?

This is an interesting career path. So you can see there is a lot of different aspects. And we as an institution, we are, of course, also quite active. We do offer scholarships, so we support any field. And just about a week ago, we conducted interviews. So we conducted interviews. There was about 100 people participating in these interviews for PhD scholarship and research scholarships in Germany. So the conclusion the professors came to was almost all the applicants used AI. And you can see this. This was mentioned before, right, by the way it’s written. You can see it, okay? And then a lot of people did actually have AI in their research proposals. It was part of the title.

So we see that. We see that. That. That changed. and this is positive right because we will progress and there will be new opportunities and just maybe also to draw a little conclusion out of these two publications that I mentioned my personal conclusion is this is a disruptive technology it’s just like when robots were invented and when computers changed our world you might recall, those who are a bit older will recall that there was also a lot of fear there is going to be mass unemployment we need, as the minister has said, we need to listen to the people we need to educate people to tell them what AI actually is AI is not intelligence at least not at the moment this is statistical tools that are predicting predicting an outcome you know but we need to listen to these fears and there is a lot of opportunity opportunity to for the entire world, which has just been mentioned before, right?

When we look at, for example, how we do agriculture, how we do farming and so on. Thank you.

Dr. Kusumita Arora

Thank you. I think we have very interesting aspects of AI, what it already means for individuals, for people, and what it’s likely to mean in the future. As it has come out, AI is without borders. So a few questions now on what international cooperations are needed and what they will do for AI, for humanity as a whole. I think AI in India and AI, the actual circumstances are a little bit different, but the fundamentals of AI will be very, very potent and very important for all countries and all environments equally. So, Dr. Kotla. I’ll come to you first to ask that. how international cooperation programs should get involved for better integration of AI for skills and innovation initiatives and to ensure an inclusive workforce globally.

Well, maybe

Dr. Bärbel Kofler

I’ll start with an overall topic. I’m just coming from another panel, which was about responsible AI. I think we have to get involved in responsibility because, yes, it was said, it’s crystal clear, that’s not neutral. We have biases in data. We have languages where millions of mother language speakers are excluded because they cannot use it in their language. People who have challenges to read and write are still sometimes excluded. So there are still things we have to overcome. We have to overcome to be really inclusive. And as I was pointing out before, it’s also in the business sector that way, that there is not the same chance for a small, medium -priced enterprise to be included in using AI or making it available for their purposes than it is for a big company.

So what international cooperation should do is to overcome the gaps. We formulated always that there is still a power gap, a gap in being a creator of AI, a gap in using AI in certain parts of the world more than in others. Dependency was talked about before. So we have to overcome those gaps. That’s the first thing we have to do in international cooperation, and we have to do it in a meaningful way. So it’s really to the perspective. To the purpose of those who are using it, to the purpose of countries, to the purpose of individuals, to companies, and so on. I still think we should have a close look how those new technologies can support agreed, internationally agreed ideas like the sustainable development goals because there’s a lot of potential in that technology that could help us to reach those goals.

So we should do that in a general way, but we also have to be concrete because it doesn’t really help us if we have conference after conference and there’s no concrete outcome on the ground. So we have to make ourselves controllable, I would say, as a government. We have to have commitments also in an international cooperation. We have to stick to those commitments and we have to report them and discuss them with public how to develop them further. So that is quite important. And that’s, by the way, something we try to do with our Hamburg sustainability, our declaration on responsibility. I was at the Hamburg Sustainability Conference. with concrete commitments by all stakeholders, governments, industry, academia, NGOs, everybody who wants to join that to come out with very concrete outcomes.

There are outcomes in skilling people to be not only users but co -creators. There can be outcomes like we were debating a little bit before, for how to bridge academia, industry needs, and the needs of the young generation of students. There are concrete topics we are working on that. I was mentioning the Living Lab in Mumbai we were launching. That’s not my ministry only or government only. It’s a cooperation of government. It’s academia with University of Mumbai and University of Leipzig who is sharing their insights. And there are concrete stakeholders from industry, and especially underlying small and medium enterprises who need access. and need workforces who don’t have to be trained for years when they left university.

They need to come up with solutions immediately when they enter a company. So we have to bridge all those things. And I think a governmental approach has to be also, on the one hand, to set frameworks, to create a reliable setting so people can trust and know what they are doing. Privacy was one of the topics. But, on the other hand, we also have to bridge the gaps which are existing in the conversation in between the stakeholders. So, yes, I’m always saying I love that with AI and all India, but at the end of the day, it’s the whole world. We all have to bridge if we really want to be useful or make use of that technology.

I think that’s, for me, the most important thing for government.

Dr. Kusumita Arora

Thank you. And Mr. Jeshwal, would you have some quick comments as to whether there needs to be other avenues of cooperation? Dr. Kofler has already said how cooperation has started between India and Germany for education. Would you like to add something to that?

Mr. Govind Jaiswal

Yeah, I’ll just add one point that AI is primarily based on the pattern. So when both the countries are collaborating, normally they have a different pattern set depending upon the societal structure, industry, maturity. Small media enterprises challenges. So when we collaborate, we try to complement each other because as long as we take to train the entire system and train the entire ecosystem, especially you said about the commitment of a stakeholder, that’s the core thing. If you want to achieve and we are working on that aspect, both the country industry and academia are doing excellent in some other field. And we will definitely collaborate and complement each other aspect. That’s it.

Dr. Kusumita Arora

Thank you. Thank you. We are a little bit running out of time, but just. One last question to Mr. Yan. How do you think German and Indian SMEs can better integrate into this? effort that is starting in full force, in fact.

Mr. Jan Noether

Yeah, thank you. That is, I believe, a central, a very central question, since if you look into Germany, 98 .5 % of the German business setup is SME. And if you look into India, it’s similar. And what is important to an SME, it is basically, I have to develop myself into a scenario where I am efficient, I am saving costs, I am innovative. Otherwise, there’s a very fierce competition, which makes me very, very vulnerable. So if we now bring this long -term experience of these Germans, German mid -sized and small companies, and we talk about decades of experiences, if we bring that together… with the talent, the spirit, the creativity, and this innovation spirit of the Indian talents.

And very important, if we in Germany get used to the speed we have in India, then this is going to be unbeatable project teams. So we need to bring people together, and we need to bring people together across countries. We need to form sandboxes where young talents of both countries and no borders of European countries and India can really experiment and come up with solutions which is not geared towards one SME, but for an industry within the SME sector. That is basically how we need to go forward. German companies are cautious when it comes to spending. and they are not risk takers so there needs to be a benefit and they need to see the benefit whether it’s a financial benefit an operational benefit they need to see that benefit in order to act therefore I look forward to be a little bit working on the field of integration together of course with other entities we’ll have in India and in Germany Thank

Dr. Kusumita Arora

hank you, thank you everybody and I think we have all come together Can

Dr. Augustus Azariah

I just make one last observation sorry, here here one last observation, sorry this thought came to me in terms of how do we collaborate and cooperate and how well the EU has done GDPR okay, making sure that people have that security similarly there’s a lot to learn from Germany in terms of how they improved their vocational training from elementary levels right up to master’s and PhD levels. And I think today, like the Honorable Secretary also said, at the school level, we need that level of collaboration to ensure that AI is seeded at the elementary level, if not at the primary level. And my request, of course, is to this eminent panel to enable our educational institutions and provide them the expertise that they can mature in taking this at the elementary level.

Thank you. Yeah.

Dr. Kusumita Arora

Of course, that would make a world of difference. And I’m sure all the partners here are ready to see a conversion of intent to commitment in the very near future. And I wish everybody the best and look forward to the outcomes. Thank you. Thank you.

Moderator

Mr. Govind and Mrs. Kofler to stay here and thank you very much for the other panelists for the days and good luck and I wish you a good summit. So because Mr. Azaria asked for follow up and we will do a follow up, we now turn to an important initiative that exemplifies the next phase of German -Indian cooperation in the field of artificial intelligence the AI academia industry innovation partnerships in Asia commissioned by BMZ and implemented through GIZ and this exactly addresses the widening gap between widening demand for AI skills and the need for job ready talent We learned about the living labs. This will be all included, combining students, researchers, industry experts to co -create and test AI solutions in a real world setting.

So this is all about and it’s just my honor to ask to invite Babel Kofler to deliver her remarks on this initiative and followed by Mr. J. J. Stahl’s remarks, That’s for me. You can stand also. No. Okay.

Dr. Bärbel Kofler

It’s a little bit dynamic at the end of the day. And I’m very brief because there was said a lot about the necessity of cooperation, especially on the training sector and the training field. What we all know is we were talking about workplaces of the future. And we know there is a chance in create also new jobs through new technologies. You were mentioned. And also. So there’s a World Bank study about, or is it World Trade Organization, I think, to study about already the creation, job creation is 1 .3 million, but we don’t have really enough skilled workforces for that sphere. So there are almost 1 million job opportunities not really filled in with adequate people, which at the end of the day leads to personal loss, economic loss, and we want to bridge those things.

That’s why we are creating this academic approach together. We want to bridge that and offer those job opportunities, which are already there on the ground, to people around the globe, and that initiative should be a part of that. And that’s why we’re really happy, and I also have to read the title, that we created. We launched this project of Artificial Intelligence Academia Industry Innovation Partnership in Asia. through my ministry together with India, Indian partners and partners in Vietnam and I’m very happy that we can do that today. Thank you.

Mr. Govind Jaiswal

Actually when we started the collaboration for this project and when we got to know about this innovation, this living lab actually. So the name is very interesting. The lab which where you incubate your idea and create a prototype and living means it has should be all the attributes of a life. So I hope it will be able to solve the problem of the industry and academia and it’s about bringing the academic world closer to industry and industry closer to academic world and academic training to just come straight with the requirement of industry. That is the major objective. and I convey my wishes for this project I am very much sure it will achieve its objective and will have further collaboration in the future also.

Thank you Thank you Thank you

Moderator

So now we invite you to watch a brief video representing the initiative Okay

Video Narrator

AI and digital technologies are reshaping how businesses operate faster than ever before. For companies the challenge is no longer access to technology but access to people. People with the skills to adapt, innovate and work confidently with AI What is taught today is often no longer what industry needs tomorrow especially for German SMEs expanding into global and Asian markets At the same time, Asia is emerging as a powerful driver of growth, dynamic economies, new ideas, and a rising generation of digital talent ready to engage with the world. This is where German Development Cooperation, implemented by GIZ, brings together German and Asian universities, businesses, and governments in a new AI Academia Industry Innovation Partnership. The question is simple.

How do we develop AI -ready skills, support innovation, and grow across borders? The answer lies in learning and innovation spaces, living labs. Living labs are structured learning and innovation spaces where universities and companies collaborate on real, industry -driven challenges. Students work on real business problems. Companies test ideas. Innovate and access emerging talent in a low -risk environment. Faculty strengthen curricula through direct engagement with industry And institutions build long -term, meaningful partnerships For students, this means hands -on experience, global collaboration, and improved employability For businesses, it means access to future -ready talent, fresh perspectives, a vibrant AI ecosystem, and a testing ground for innovation More than a program, this is a partnership at eye level Combining German expertise with Asian entrepreneurial energy and drive to innovate This is the AI Innovation Partnership Uniting academia, industry, and governments across Germany and Asia to shape what’s next Developing skills, enabling innovation, building an AI -driven future together

Moderator

Thank you. Thank you. Thank you.

Related ResourcesKnowledge base sources related to the discussion topics (17)
Factual NotesClaims verified against the Diplo knowledge base (7)
Confirmedhigh

“The moderator opened the session by noting that global digital transformation has made AI a central strategic priority for partner economies, notably Germany and India.”

The knowledge base states that global digital transformation for partners such as Germany and India makes AI a strategic priority, confirming the moderator’s framing of AI as central to the partnership [S1].

Additional Contextmedium

“The moderator of the session was Anandi Iyer, Head of Fraunhofer in India.”

While the report does not name the moderator, the knowledge base identifies the session moderator as Anandi Iyer, providing additional detail about the moderator’s background [S48].

Confirmedhigh

“Dr Bärbel Kofler is Parliamentary State Secretary to the German Federal Ministry for Economic Cooperation and Development.”

Multiple sources list Bärbel Kofler in exactly this role, confirming the report’s description [S21] and [S12].

Additional Contextmedium

“Dr Kofler highlighted Germany’s commitment to “open source, open data”, climate‑friendly computing, and regulatory frameworks that reduce energy and water consumption while ensuring responsible AI deployment.”

The knowledge base notes that German development policy emphasizes climate-friendly computing, responsible AI, and sustainability, which adds nuance to the claim though it does not explicitly mention open-source or water-saving regulations [S97].

Additional Contextmedium

“Govind Jaiswal’s discussion of systematic reskilling aligns with India’s broader effort to train millions of young people in AI through government‑industry university partnerships.”

A source reports a government initiative to train 10 million young people in AI and to expand industry-university collaborations, providing background that supports Jaiswal’s emphasis on large-scale reskilling [S102].

Additional Contextlow

“The concern that fresh graduates submit AI‑generated CVs lacking genuine competence reflects a wider issue of AI misuse in academia.”

An incident of a university student using AI to complete an essay is documented, illustrating the broader problem of AI-generated academic work and lending context to the claim about AI-generated CVs [S101].

Additional Contextmedium

“The discussion exemplifies Indo‑German AI collaboration, with both sides contributing expertise and resources.”

The knowledge base repeatedly references Indo-German AI cooperation, noting joint research, data sharing, and mutual investment, which reinforces the report’s framing of the partnership [S48] and [S45].

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AI Impact Summit 2026: Global Ministerial Discussions on Inclusive AI Development — And this requires proactive and coherent policy responses. First, people must be at the center of AI strategy, as we hea…
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Open Microphone Taking Stock — International cooperation is vital as technologies develop across national borders
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AI Transformation in Practice_ Insights from India’s Consulting Leaders — Future skills requirements emphasise working with technology rather than coding, with increasing importance placed on ps…
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Searching for Standards: The Global Competition to Govern AI | IGF 2023 — Collaboration with industry was deemed essential in the regulation of AI. Industry was seen as a valuable source of reso…
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WS #294 AI Sandboxes Responsible Innovation in Developing Countries — Mariana Rozo-Pan: Thank you, Sophie. And hi, everyone. Good morning, good afternoon, good evening. We are very excited a…
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Global AI Policy Framework: International Cooperation and Historical Perspectives — The speakers demonstrate significant consensus on key principles including the need for inclusive governance, building o…
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WS #189 AI Regulation Unveiled: Global Pioneering for a Safer World — Gautam brought attention to the lack of capacity in developing nations to implement or create AI standards, highlighting…
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AI Algorithms and the Future of Global Diplomacy — The panelists discussed how great powers like the US and China compete at the frontier level of AI development, while mi…
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AI Impact Summit 2026: Global Ministerial Discussions on Inclusive AI Development — Ante este panorama, los países del sur global debemos priorizar estrategias y normativas para un uso ético y responsable…
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GermanAsian AI Partnerships Driving Talent Innovation the Future — And I think that’s the aim and the goal of my ministry and that’s the aim of the goal of the German government to make t…
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IndoGerman AI Collaboration Driving Economic Development and Soc — The strategic rationale for this partnership lies in the complementary strengths of both nations. India accounts for 15%…
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AI Meets Cybersecurity Trust Governance & Global Security — These key comments fundamentally shaped the discussion by challenging conventional assumptions about AI security and gov…
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Building Climate-Resilient Systems with AI — Academic speakers unexpectedly emphasize moving beyond research and pilots to immediate deployment, showing alignment wi…
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Building Public Interest AI Catalytic Funding for Equitable Compute Access — Dr. Gitau’s compute demand index and AI Investment Readiness Index provide practical tools that other regions can adapt….
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AI Meets Agriculture Building Food Security and Climate Resilien — This insight distinguishes AI deployment from traditional technology rollouts, emphasizing iterative improvement over pe…
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Comprehensive Report: European Approaches to AI Regulation and Governance — The speakers showed mutual respect for each other’s approaches, with neither claiming their method was superior but rath…
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Main Session 2: The governance of artificial intelligence — Both speakers, despite different backgrounds, agree that not all bias is problematic and that efforts should focus on ad…
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Laying the foundations for AI governance — Low to moderate disagreement level. The speakers largely agreed on problem identification but differed on solutions and …
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UN report highlights AI opportunities for small businesses — AI is increasinglyhelping entrepreneurs in developing countrieslaunch, manage, and grow their businesses, according to a…
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Responsible AI in India Leadership Ethics & Global Impact — “I’m sure every organization today has a legal team, has a compliance team”[59]. “Legal teams have to re‑opt to talk abo…
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Empowering Inclusive and Sustainable Trade in Asia-Pacific: Perspectives on the WTO E-commerce Moratorium — To ensure successful integration, bridging the gap between academia and industry is essential. Due to the rapid advancem…
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Global Standards for a Sustainable Digital Future — A key innovation proposed by Kalogeropoulos was the concept of “evidence sandboxes” – controlled environments where stak…
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Artificial intelligence — Privacy and data protection
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Comprehensive Report: Preventing Jobless Growth in the Age of AI — And that’s been lagging much more. We can close that gap and boost the productivity, that will make a big difference. Le…
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Comprehensive Report: AI’s Impact on the Future of Work – Davos 2026 Panel Discussion — Economic | Future of work Skills Gap and Workforce Development
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GermanAsian AI Partnerships Driving Talent Innovation the Future — And it’s a very very organized and very very structured way that we are moving in last five to eight years especially la…
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AI 2.0 The Future of Learning in India — “…we want to be creative nations now this time the opportunity is phenomenal so we need to have a system where people …
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AI Transformation in Practice_ Insights from India’s Consulting Leaders — Capacity development | Artificial intelligence Talent development, education and future skills
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AI-Powered Chips and Skills Shaping Indias Next-Gen Workforce — Continue government support for training initiatives under India Semiconductor Mission 2.0 Expand hands-on training fac…
S82
WS #294 AI Sandboxes Responsible Innovation in Developing Countries — Mariana Rozo-Pan: Thank you, Sophie. And hi, everyone. Good morning, good afternoon, good evening. We are very excited a…
S83
Searching for Standards: The Global Competition to Govern AI | IGF 2023 — Collaboration with industry was deemed essential in the regulation of AI. Industry was seen as a valuable source of reso…
S84
Strengthen Digital Governance and International Cooperation to Build an Inclusive Digital Future — The WSIS Plus 20 Forum brought together representatives from governments, international organisations, enterprises, and …
S85
AI Governance Dialogue: Steering the future of AI — ## Concrete Commitments and Outcomes
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Open Forum #71 Advancing Rights-Respecting AI Governance and Digital Inclusion through G7 and G20 — Eugenio Garcia: with growing polarisation or geopolitical tensions, ideological divides. And you see that President Lula…
S87
The impact of AI on jobs and workforce — The ILO’s webinar was triggered by the recent impact of ChatGPT on our society and jobs. OpenAI’s ChatGPT, in particular…
S88
How AI Is Transforming Indias Workforce for Global Competitivene — This comment shifted the discussion toward practical deployment strategies and cross-sector integration. It reinforced t…
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International multistakeholder cooperation for AI standards | IGF 2023 WS #465 — Florian Ostmann:Thank you, Matilda. So with that set out in terms of what kinds of standards we are focused on and why w…
S90
The Global Power Shift India’s Rise in AI & Semiconductors — -Moderator: Role not specified in detail, appears to be the session moderator who introduced the panelists and managed t…
S91
Leaders’ Plenary | Global Vision for AI Impact and Governance Morning Session Part 1 — Agar kisi machine ko sir paper clip banane ka alak de diya jaye to wo uska ek kaam ke liye duniya ke saare resources ko …
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The Future of Innovation and Entrepreneurship in the AI Era: A World Economic Forum Panel Discussion — This World Economic Forum panel discussion brought together global leaders to examine how artificial intelligence is tra…
S93
WS #462 Bridging the Compute Divide a Global Alliance for AI — The discussion revealed that the challenge extends beyond inequitable distribution to an overall supply-demand gap affec…
S94
How to make AI governance fit for purpose? — ## Panel Participants – **Gabriela Ramos**: Moderator of the panel discussion, mentioned as running for a position at U…
S95
High-level AI Standards panel — Coordinated approach and strong partnerships are key to bringing coherence for governments and industry
S96
OpenAI for Germany to modernise public sector with AI — SAP SE and OpenAI haveannounced the launch of OpenAI for Germany, a partnership to bring advanced AI solutions to the pu…
S97
Planetary Limits of AI: Governance for Just Digitalisation? | IGF 2023 Open Forum #37 — Another speaker argues that digitalisation and technology should promote sustainable development goals and uphold human …
S98
AI Governance: Ensuring equity and accountability in the digital economy (UNCTAD) — Inclusion emerged as a recurring theme, with speakers stressing the importance of involving all stakeholders in the AI d…
S99
Open Internet Inclusive AI Unlocking Innovation for All — Anandan presented concrete evidence of India’s success with this approach, highlighting multiple companies achieving bre…
S100
Exploring Blockchain’s Potential for Responsible Digital ID | IGF 2023 — Students had hands-on experience Students had hands-on experience with the project.
S101
AI cheating scandal at University sparks concern — Hannah, a university student,admits to using AIto complete an essay when overwhelmed by deadlines and personal illness. …
S102
https://dig.watch/event/india-ai-impact-summit-2026/keynote-rishad-premji — Government initiatives to train 10 million young people in AI, along with industry partnerships with universities, are e…
S103
https://dig.watch/event/india-ai-impact-summit-2026/indogerman-ai-collaboration-driving-economic-development-and-soc — And circular economy. that government, academia, and industry work hand -in -hand. By promoting research and development…
S104
https://dig.watch/event/india-ai-impact-summit-2026/digital-democracy-leveraging-the-bhashini-stack-in-the-parliamen — Dear Mr. Naack, dear partners, distinguished guests, it is a great pleasure to welcome you to this launch today. We pres…
Speakers Analysis
Detailed breakdown of each speaker’s arguments and positions
D
Dr. Bärbel Kofler
5 arguments143 words per minute1618 words678 seconds
Argument 1
Emphasises public fear of AI‑driven job loss and the need to make AI inclusive for all workers, especially SMEs (Kofler)
EXPLANATION
Kofler points out that many people are anxious that AI will eliminate jobs and stresses that policies must address these concerns by ensuring AI benefits are accessible to all workers, particularly small and medium‑sized enterprises. She calls for a careful, inclusive approach to AI deployment.
EVIDENCE
She acknowledges that people are afraid of losing jobs and that this fear is legitimate, urging careful handling of these feelings [36-38]. She then explains that AI must serve everyone, especially SMEs, to close the power gap and make the technology usable by all stakeholders [42-44][53-54].
EXTERNAL EVIDENCE (KNOWLEDGE BASE)
The ILO webinar highlights widespread public concern about AI-driven job displacement and the need for policy responses [S17]; a separate analysis notes that AI tends to transform tasks rather than eliminate roles, underscoring the relevance of addressing fear [S31]; the power-gap framing in multistakeholder discussions reinforces the call for inclusive AI for SMEs [S21].
MAJOR DISCUSSION POINT
Job displacement concerns
AGREED WITH
Moderator, Mr. Jan Noether, Video narrator
Argument 2
Announces the AI Living Lab in Mumbai and integration of AI modules into university curricula (Kofler)
EXPLANATION
Kofler describes the launch of an AI Living Lab at a university in Mumbai, designed to embed AI topics directly into curricula so that students gain hands‑on experience. The initiative links students with small media enterprises that typically lack AI access, creating a practical learning environment.
EVIDENCE
She reports returning from Mumbai where the AI Living Lab was opened at Rattentata University, explaining its purpose to make AI part of university curricula and to involve small media enterprises that otherwise have limited AI access [46-50][51-53].
EXTERNAL EVIDENCE (KNOWLEDGE BASE)
The German-Asian AI Partnerships briefing describes the launch of an AI Living Lab at Tata University in Mumbai and its curriculum integration [S1].
MAJOR DISCUSSION POINT
Practical AI education
AGREED WITH
Mr. Jan Noether, Video narrator, Moderator
Argument 3
Highlights German‑Indian cooperation through the AI Living Lab and Hamburg sustainability commitments, stressing concrete outcomes (Kofler)
EXPLANATION
Kofler emphasizes that Germany and India are jointly developing AI initiatives such as the Living Lab and the Hamburg Sustainability Declaration, aiming for tangible results rather than abstract discussions. The cooperation brings together government, academia, industry, and SMEs to ensure responsible AI deployment.
EVIDENCE
She refers to responsible AI, the Hamburg sustainability commitments, and the need for concrete outcomes in international cooperation [214-218]. She further details the Living Lab involving German and Indian universities, industry partners, and SMEs to bridge gaps and create immediate solutions [241-247].
EXTERNAL EVIDENCE (KNOWLEDGE BASE)
The partnership briefing notes the Hamburg Sustainability Conference as a concrete commitment within the German-Indian AI cooperation framework [S1]; the Hamburg Declaration on Responsible AI for the SDGs provides further detail on measurable outcomes [S20]; broader governance discussions echo the need for concrete results [S21].
MAJOR DISCUSSION POINT
Bilateral AI cooperation
AGREED WITH
Moderator, Dr. Kusumita Arora, Mr. Jan Noether, Dr. Augustus Azariah
Argument 4
Argues that closing the “power gap” is essential so that small and medium enterprises can both use and create AI solutions (Kofler)
EXPLANATION
Kofler argues that democratizing AI requires reducing the existing power imbalance between large corporations and SMEs, enabling the latter to both adopt and develop AI technologies. This is presented as a core objective of her ministry’s AI strategy.
EVIDENCE
She discusses the necessity of overcoming the power gap so that technology can be spread and used by everyone, especially small and medium-sized enterprises [42-44][53-54].
EXTERNAL EVIDENCE (KNOWLEDGE BASE)
Multistakeholder partnership reports explicitly state that the challenge is a “power gap” rather than an innovation gap and call for frameworks to empower SMEs [S21].
MAJOR DISCUSSION POINT
SME empowerment
Argument 5
Calls for responsible AI governance, climate‑friendly computing, open data, and regulatory frameworks to ensure ethical use (Kofler)
EXPLANATION
Kofler calls for AI to be governed responsibly, highlighting the importance of climate‑friendly computing, open data, and robust regulatory frameworks to ensure AI serves people ethically and sustainably. She links these measures to broader sustainability goals.
EVIDENCE
She mentions making new technology climate-friendly, reducing energy and water consumption, and the need for regulation within governmental frameworks [44-46][214-218].
EXTERNAL EVIDENCE (KNOWLEDGE BASE)
The Hamburg Declaration outlines responsible AI principles, including climate-friendly computing and open data mandates [S20]; governance and regulatory needs are highlighted in multistakeholder AI ecosystem discussions [S21]; bias and language inclusion concerns are raised in sector-specific AI work [S12].
MAJOR DISCUSSION POINT
Ethical AI deployment
AGREED WITH
Arthur Rapp, Dr. Augustus Azariah
D
Dr. Kusumita Arora
2 arguments101 words per minute818 words485 seconds
Argument 1
Stresses the need for clear policy intent and scalable frameworks to embed AI skills across education levels (Arora)
EXPLANATION
Arora emphasizes that effective policy direction and scalable mechanisms are required to integrate AI competencies throughout the education system, from schools to higher education, ensuring that people can benefit from AI in the future of work.
EVIDENCE
She outlines that the forum will discuss policy intent, scaling, and pragmatic issues that drive the conversation, highlighting the inclusion of people, personal growth, and their connection to AI [27-29].
EXTERNAL EVIDENCE (KNOWLEDGE BASE)
Upskilling initiatives for the AI era emphasize scalable policy frameworks for education and lifelong learning [S27]; broader policy response reports call for coherent, proactive measures to embed AI skills [S26]; international cooperation contexts underline the need for clear intent [S28].
MAJOR DISCUSSION POINT
Policy framework for AI education
AGREED WITH
Dr. Bärbel Kofler, Mr. Govind Jaiswal, Dr. Augustus Azariah, Video narrator
Argument 2
Highlights that AI transcends national borders and therefore international cooperation is required to develop shared standards, joint research and equitable access to AI technologies.
EXPLANATION
Arora points out that AI is “without borders” and asks how international cooperation programmes can support skill development and innovation, implying the need for coordinated policies and standards across countries.
EVIDENCE
She remarks, “AI is without borders” and subsequently asks how international cooperation programs should get involved for better integration of AI for skills and innovation initiatives [209-212].
EXTERNAL EVIDENCE (KNOWLEDGE BASE)
Calls for coordinated international AI standards appear in multiple forums stressing cross-border cooperation [S28]; discussions on AI regulation stress the necessity of global collaboration [S29]; an open forum on building an international AI cooperation ecosystem underscores shared standards [S30].
MAJOR DISCUSSION POINT
Need for cross‑border AI governance and cooperation
M
Mr. Govind Jaiswal
4 arguments157 words per minute1056 words402 seconds
Argument 1
Uses the electricity analogy to argue AI will raise living standards and outlines India’s policy measures for reskilling the workforce (Jaiswal)
EXPLANATION
Jaiswal compares AI to the historic introduction of electricity, arguing that while new technologies cause disruption, they ultimately improve living standards and create new job opportunities. He then outlines India’s comprehensive policy measures aimed at reskilling the workforce for an AI‑driven economy.
EVIDENCE
He illustrates the analogy by describing how concerns about job loss were raised when electricity arrived, yet the technology led to new devices and higher quality of life [71-74]. He follows with details of India’s National Education Policy 2020, the expansion of research parks, dual-education systems, apprenticeships, and AI-focused training initiatives that aim to equip millions of students [84-95][96-98].
EXTERNAL EVIDENCE (KNOWLEDGE BASE)
The German-Asian AI Partnerships transcript records Jaiswal’s electricity analogy and his overview of India’s reskilling policies [S1].
MAJOR DISCUSSION POINT
Reskilling for AI
AGREED WITH
Dr. Kusumita Arora, Dr. Bärbel Kofler, Dr. Augustus Azariah, Video narrator
Argument 2
Details India’s National Education Policy, new research parks, dual‑education system, apprenticeships, and large‑scale student outreach (Jaiswal)
EXPLANATION
Jaiswal provides a detailed overview of India’s strategic education reforms, including the 2020 National Education Policy, the creation of new research parks, a dual‑education model, and extensive apprenticeship programmes, all designed to embed AI skills across the student population.
EVIDENCE
He cites the NEP 2020’s emphasis on 50 % skill-oriented courses, the establishment of six new research parks (now nine) at premier institutions, the launch of five educational cities linked to industrial corridors, and the goal of equipping around 40 million students with AI competencies in the coming years [84-95][96-98].
EXTERNAL EVIDENCE (KNOWLEDGE BASE)
The partnership briefing details India’s NEP-2020 targets, the creation of new research parks, dual-education models and apprenticeship programmes [S1]; policy response documents further highlight these reforms as part of AI-focused skill development [S26].
MAJOR DISCUSSION POINT
National AI education strategy
Argument 3
Notes complementary patterns between the two countries and ongoing bilateral projects in education and skill development (Jaiswal)
EXPLANATION
Jaiswal observes that Germany and India have different AI development patterns, and that collaboration allows each country to complement the other’s strengths, enhancing skill development and ecosystem building through joint projects.
EVIDENCE
He explains that both countries have distinct patterns and that collaboration helps complement each other, especially in education and skill development initiatives [257-262].
EXTERNAL EVIDENCE (KNOWLEDGE BASE)
The same German-Asian AI Partnerships discussion notes complementary AI development patterns and joint education projects between Germany and India [S1].
MAJOR DISCUSSION POINT
Bilateral complementarity
Argument 4
Advocates making industry internships and apprenticeship programmes mandatory within AI‑focused curricula to ensure a seamless transition from education to work.
EXPLANATION
Jaiswal stresses that embedding compulsory industry exposure, internships and apprenticeship‑linked degree programmes will align graduates’ skills with labour‑market needs and reduce disruption during the AI transition.
EVIDENCE
He notes that “every student of this country is getting equipped with artificial intelligence” and that “internship was made mandatory, apprenticeship embedded degree program was launched” as part of a broader effort to integrate industry exposure into education [96-98].
EXTERNAL EVIDENCE (KNOWLEDGE BASE)
Policy briefs on AI workforce development stress mandatory industry exposure, internships and apprenticeship-linked degree programmes as key levers for transition [S26]; the partnership transcript also references mandatory internships in India’s AI strategy [S1].
MAJOR DISCUSSION POINT
Mandatory industry exposure for AI talent development
M
Mr. Jan Noether
5 arguments123 words per minute595 words289 seconds
Argument 1
Points out that German and Indian SMEs require clear efficiency and cost‑benefit gains from AI to adopt it (Noether)
EXPLANATION
Noether stresses that SMEs dominate both economies and will only invest in AI if they can see concrete efficiency improvements, cost reductions, and innovation benefits. Without clear ROI, SMEs remain vulnerable to competition.
EVIDENCE
He notes that 98.5 % of German businesses are SMEs and a similar share exists in India, and that SMEs need efficiency, cost savings, and innovation to stay competitive [269-272][274-276].
EXTERNAL EVIDENCE (KNOWLEDGE BASE)
Multistakeholder AI ecosystem reports highlight that SMEs need demonstrable efficiency and cost-benefit improvements before investing in AI solutions [S21].
MAJOR DISCUSSION POINT
SME AI adoption incentives
AGREED WITH
Dr. Bärbel Kofler, Moderator, Video narrator
Argument 2
Introduces a joint German‑Indian master’s programme and cross‑border degree structure (Noether)
EXPLANATION
Noether announces a formal agreement with a university in Baden‑Württemberg to create a joint master’s programme, with two‑thirds of the coursework delivered in India and one‑third in Germany, exemplifying cross‑border academic collaboration.
EVIDENCE
He mentions the signed agreement with the dual university of Baden-Württemberg for a master’s programme split between India and Germany [161-164].
EXTERNAL EVIDENCE (KNOWLEDGE BASE)
The German-Asian AI Partnerships briefing announces a formal agreement for a joint master’s programme split between India and Germany [S1].
MAJOR DISCUSSION POINT
Cross‑border AI education
AGREED WITH
Moderator, Dr. Bärbel Kofler, Dr. Kusumita Arora, Dr. Augustus Azariah
Argument 3
Reports a formal agreement with Baden‑Württemberg for a joint master’s programme and proposes sandbox environments for joint SME innovation (Noether)
EXPLANATION
Beyond the master’s programme, Noether proposes creating sandbox environments where young talent from both countries can experiment together on AI solutions tailored for the SME sector, fostering collaborative innovation without being tied to a single company.
EVIDENCE
He references the same master’s agreement [161-164] and adds that sandboxes should be formed for joint talent to develop SME-focused AI solutions [275-276].
MAJOR DISCUSSION POINT
Collaborative innovation spaces
AGREED WITH
Dr. Bärbel Kofler, Video narrator, Moderator
Argument 4
Emphasises that SMEs form the backbone of both economies and need demonstrable financial/operational benefits to invest in AI (Noether)
EXPLANATION
Reiterating his earlier point, Noether underscores that SMEs are the economic backbone and will adopt AI only when clear financial or operational advantages are evident, reinforcing the need for demonstrable ROI.
EVIDENCE
He repeats that the majority of businesses are SMEs in both Germany and India and that they require efficiency, cost savings, and innovation to survive competition [269-272][274-276].
EXTERNAL EVIDENCE (KNOWLEDGE BASE)
SME-centric discussions reiterate that SMEs constitute the economic backbone and will adopt AI only when clear financial or operational ROI is evident [S21].
MAJOR DISCUSSION POINT
SME ROI
Argument 5
Identifies key application areas—healthcare, agriculture, water management, energy—where AI can drive sustainable outcomes (Noether)
EXPLANATION
Noether highlights specific sectors where AI can have transformative, sustainable impacts, including health data analysis, remote patient care, digital and satellite imaging for agriculture, water resource management, and energy efficiency.
EVIDENCE
He cites AI applications in healthcare data analysis, disease management, remote patient access, as well as agriculture, digital imaging, satellite imaging, water scarcity solutions, and energy sustainability [155-162][159-160].
MAJOR DISCUSSION POINT
AI for sustainable sectors
D
Dr. Augustus Azariah
4 arguments127 words per minute886 words418 seconds
Argument 1
Highlights a gap between graduate AI knowledge and industry needs, urging originality and faculty up‑training (Azariah)
EXPLANATION
Azariah observes that many graduates list AI skills generated by tools like ChatGPT without genuine competence, creating a mismatch between academic credentials and industry expectations. He calls for originality in work and better training of faculty to teach practical AI tools.
EVIDENCE
He notes that CVs often contain AI-generated content lacking originality and that faculty are not yet equipped to teach practical AI tools, leading to a skills gap [115-124][130-138].
EXTERNAL EVIDENCE (KNOWLEDGE BASE)
Upskilling for the AI era reports a mismatch between graduate AI credentials and industry expectations, calling for faculty development and originality in curricula [S27]; broader ecosystem analyses echo the need for faculty up-training [S21].
MAJOR DISCUSSION POINT
Skills mismatch
Argument 2
Describes industry‑led faculty certification (e.g., Copilot), large hackathons, and endowment funds to boost academic AI capability (Azariah)
EXPLANATION
Azariah outlines industry initiatives that certify faculty in AI productivity tools such as Copilot, citing a large hackathon that certified over a thousand faculty members and plans to scale this effort while providing endowment funds for faculty‑driven innovation.
EVIDENCE
He reports a hackathon with about 18,000 participants where more than a thousand faculty were certified in Copilot, and states the target to reach hundreds of thousands of certified faculty and to provide endowment funds for innovation [136-140][141-144].
EXTERNAL EVIDENCE (KNOWLEDGE BASE)
The upskilling briefing details a large hackathon that certified over a thousand faculty members in AI productivity tools and outlines plans for endowment-funded faculty innovation [S27].
MAJOR DISCUSSION POINT
Faculty upskilling
AGREED WITH
Dr. Kusumita Arora, Dr. Bärbel Kofler, Mr. Govind Jaiswal, Video narrator
Argument 3
Suggests leveraging German expertise in data protection (GDPR) and vocational training to strengthen Asian AI ecosystems (Azariah)
EXPLANATION
Azariah proposes that Europe’s experience with GDPR and its comprehensive vocational training system—from elementary to doctoral levels—can serve as a model for Asian countries to develop robust, rights‑respecting AI ecosystems.
EVIDENCE
He references Germany’s GDPR framework and its vocational training system that spans from primary education to PhD levels as best practices for Asian partners [279-282].
EXTERNAL EVIDENCE (KNOWLEDGE BASE)
Multistakeholder partnership notes cite Germany’s GDPR framework and its comprehensive vocational training system as best-practice models for Asian AI ecosystem development [S21].
MAJOR DISCUSSION POINT
Transfer of best practices
AGREED WITH
Dr. Bärbel Kofler, Arthur Rapp
Argument 4
Points out industry initiatives to bring AI tools to tier‑2/3 city talent and to certify faculty who support SME innovation (Azariah)
EXPLANATION
Azariah highlights efforts to extend AI training and tools to talent in tier‑2 and tier‑3 cities, and to certify faculty who can support SME innovation, demonstrating that high‑quality AI talent exists beyond elite institutions.
EVIDENCE
He mentions targeting tier-2/3 cities, a blind hiring exercise where four out of ten high-paying fresh-graduate hires came from non-IIT backgrounds, illustrating the breadth of talent available [141-144][145-147].
MAJOR DISCUSSION POINT
Expanding AI talent pool
A
Arthur Rapp
4 arguments151 words per minute782 words309 seconds
Argument 1
Warns of dependence on foreign AI platforms, bias, and data‑privacy risks that could affect employment prospects (Rapp)
EXPLANATION
Rapp cautions that reliance on AI services owned outside Europe creates strategic vulnerabilities, including biased outcomes, language exclusion, and potential data leakage, which could undermine research, innovation, and job security.
EVIDENCE
He cites a study showing the risk of dependence on non-European AI platforms, the presence of bias, and the possibility that data used to train AI could be exploited, leading to future access or cost issues [170-179][180-186].
EXTERNAL EVIDENCE (KNOWLEDGE BASE)
Sector-specific AI work highlights bias, language exclusion and data-leakage risks associated with non-European AI services [S12]; the Hamburg Declaration stresses responsible AI to mitigate such risks [S20]; governance discussions call for safeguards against foreign platform dependence [S21].
MAJOR DISCUSSION POINT
Sovereign AI and data risks
AGREED WITH
Dr. Bärbel Kofler, Dr. Augustus Azariah
Argument 2
Calls for AI literacy, critical thinking, and awareness of AI’s influence on research and career choices (Rapp)
EXPLANATION
Rapp notes that many students already consult AI for career decisions and embed AI in research proposals, indicating growing AI literacy but also highlighting the need for critical awareness of AI’s influence on personal and professional pathways.
EVIDENCE
He references studies showing that a large proportion of young people use AI to decide on careers and universities, and that most PhD applicants incorporate AI into their proposals, demonstrating widespread AI usage in academic contexts [188-196][197-203].
EXTERNAL EVIDENCE (KNOWLEDGE BASE)
Upskilling initiatives stress AI literacy, critical thinking and awareness as essential components of AI education [S27]; broader policy responses underline the need for AI-aware citizens and workers [S26].
MAJOR DISCUSSION POINT
AI awareness among youth
Argument 3
Raises concerns about reliance on non‑European AI services and advocates for sovereign, unbiased AI development (Rapp)
EXPLANATION
Rapp reiterates the strategic risk of depending on AI platforms outside Europe, urging the development of sovereign, unbiased AI ecosystems to protect national interests and ensure equitable access.
EVIDENCE
He again points to the study highlighting dependence risks, bias, and potential future costs or exclusion associated with foreign AI services, emphasizing the need for independent AI capabilities [170-179][180-186].
EXTERNAL EVIDENCE (KNOWLEDGE BASE)
The Hamburg Declaration calls for sovereign, unbiased AI ecosystems to protect national interests [S20]; multistakeholder governance reports echo the strategic risk of dependence on foreign AI platforms [S21].
MAJOR DISCUSSION POINT
AI sovereignty
Argument 4
Highlights risks of bias, language exclusion, and data leakage when using foreign AI platforms, urging safeguards (Rapp)
EXPLANATION
Rapp underscores that AI systems can embed biases, marginalize speakers of less‑represented languages, and expose personal data, calling for safeguards to ensure inclusive and secure AI use.
EVIDENCE
He mentions bias in data, exclusion of millions of mother-language speakers, challenges for illiterate users, and the necessity to overcome these gaps to achieve inclusive AI [216-220][221-226].
EXTERNAL EVIDENCE (KNOWLEDGE BASE)
Research on AI bias and language marginalisation points to exclusion of many mother-tongue speakers and potential data leakage, calling for robust safeguards [S12].
MAJOR DISCUSSION POINT
Inclusive and safe AI
M
Moderator
4 arguments110 words per minute626 words340 seconds
Argument 1
Emphasises that global digital transformation partnerships between Germany and India are essential for leveraging AI benefits.
EXPLANATION
The moderator frames the discussion around a joint digital transformation agenda, stating that cooperation with partners such as Germany and India is crucial to harness AI for development.
EVIDENCE
In the opening remarks the moderator mentions a “global digital transformation for partners such as Germany and India” and sets the stage for collaborative action [1].
EXTERNAL EVIDENCE (KNOWLEDGE BASE)
The German-Asian AI Partnerships briefing underscores the strategic importance of Germany-India collaboration for AI talent and innovation [S1]; the Global Digital Compact session stresses inclusive AI for the digital economy [S18]; policy response documents highlight the role of such partnerships in digital transformation [S26].
MAJOR DISCUSSION POINT
International partnership for digital transformation
Argument 2
Argues that the strategic priority has shifted from AI development to effective deployment and response.
EXPLANATION
The moderator notes that the focus is no longer solely on creating AI, but on ensuring its responsible and efficient use, highlighting the need for deployment strategies.
EVIDENCE
He states that “the strategic priority is not longer solely the development of artificial intelligence, but very much its response limit effective deployment” [2].
EXTERNAL EVIDENCE (KNOWLEDGE BASE)
AI policy briefs note a shift from pure AI creation to responsible deployment and impact mitigation as a strategic priority [S26].
MAJOR DISCUSSION POINT
Shift from AI creation to deployment
Argument 3
Claims that access to skills, innovation ecosystems and trusted partnerships for SMEs will decide whether AI becomes an inclusive driver of opportunity.
EXPLANATION
The moderator stresses that small and medium‑sized enterprises need adequate skills, ecosystems and partnerships to benefit from AI, otherwise the technology will not serve the broader economy.
EVIDENCE
He highlights that “access to skills, innovation, ecosystems and trusted partnerships will determine whether AI becomes a driver of opportunity for all” especially for SMEs in Germany, India and elsewhere [3].
EXTERNAL EVIDENCE (KNOWLEDGE BASE)
Multistakeholder discussions stress that SME inclusion depends on skills, ecosystems and trusted partnerships [S21]; upskilling reports highlight the centrality of skills for SME AI adoption [S27]; partnership briefings note concrete SME-focused initiatives [S1].
MAJOR DISCUSSION POINT
SME inclusion in AI benefits
Argument 4
Calls for multi‑stakeholder cooperation to shape an innovative, inclusive and human‑centered future of work.
EXPLANATION
The moderator outlines the panel’s purpose: to explore how governments, industry, academia and development partners can jointly address AI challenges and create a future of work that is inclusive and human‑centred.
EVIDENCE
He says the panel will explore cooperation among governments, industry, academia and development partners to address challenges and shape a future of work that is innovative, inclusive and human-centered [4].
EXTERNAL EVIDENCE (KNOWLEDGE BASE)
Various forums call for multistakeholder AI governance, inclusive standards and cross-sector collaboration to shape the future of work [S21]; open-mic and forum sessions emphasize the need for coordinated international responses and stakeholder engagement [S28][S29][S30].
MAJOR DISCUSSION POINT
Collaborative governance for future of work
V
Video Narrator
3 arguments119 words per minute280 words141 seconds
Argument 1
States that the main challenge for companies is access to people with AI‑ready skills rather than the technology itself.
EXPLANATION
The narrator points out that while AI technologies are rapidly evolving, firms struggle more with finding skilled personnel who can apply these tools effectively.
EVIDENCE
The narration says, “For companies the challenge is no longer access to technology but access to people. People with the skills to adapt, innovate and work confidently with AI” [313-314].
EXTERNAL EVIDENCE (KNOWLEDGE BASE)
Upskilling for the AI era identifies the talent shortage, not technology availability, as the primary bottleneck for firms [S27]; policy briefs echo the need for skilled personnel to drive AI adoption [S26].
MAJOR DISCUSSION POINT
Skills gap as bottleneck for AI adoption
Argument 2
Describes living labs as structured spaces where universities and companies co‑create AI solutions, arguing they are essential for hands‑on learning and innovation.
EXPLANATION
Living labs are presented as practical environments that bring together academia and industry to work on real‑world challenges, thereby accelerating skill development and innovation.
EVIDENCE
The video explains that “Living labs are structured learning and innovation spaces where universities and companies collaborate on real, industry-driven challenges” and that they enable students to work on real business problems while companies test ideas [319-322].
EXTERNAL EVIDENCE (KNOWLEDGE BASE)
The German-Asian AI Partnerships briefing describes living labs as collaborative innovation spaces linking academia and industry for real-world AI projects [S1].
MAJOR DISCUSSION POINT
Living labs as innovation ecosystems
Argument 3
Advocates a partnership model that combines German expertise with Asian entrepreneurial energy to accelerate AI‑driven development.
EXPLANATION
The narrator argues that merging Germany’s technical know‑how with Asia’s dynamic talent pool creates a powerful engine for AI skill development, innovation and economic growth.
EVIDENCE
The narration states that the partnership “combines German expertise with Asian entrepreneurial energy and drive to innovate” and positions it as a way to shape the AI-driven future [315-322].
EXTERNAL EVIDENCE (KNOWLEDGE BASE)
The partnership briefing highlights the synergy of German technical know-how with Asian entrepreneurial dynamism as a catalyst for AI talent development [S1]; the Global Digital Compact session frames such cross-regional collaboration as essential for inclusive AI progress [S18].
MAJOR DISCUSSION POINT
Cross‑regional collaboration for AI advancement
Agreements
Agreement Points
Inclusive AI for SMEs and the broader workforce, addressing job‑loss fears and the need for skilled people
Speakers: Dr. Bärbel Kofler, Moderator, Mr. Jan Noether, Video narrator
Emphasises public fear of AI‑driven job loss and the need to make AI inclusive for all workers, especially SMEs (Kofler) Claims that access to skills, innovation, ecosystems and trusted partnerships for SMEs will decide whether AI becomes an inclusive driver of opportunity (Moderator) Points out that German and Indian SMEs require clear efficiency and cost‑benefit gains from AI to adopt it (Noether) States that the main challenge for companies is access to people with AI‑ready skills rather than the technology itself (Video narrator)
All speakers underline that AI can only deliver inclusive benefits if small- and medium-sized enterprises and workers have the necessary skills; they acknowledge public anxiety about job loss and stress that policies must make AI accessible and demonstrably beneficial for SMEs [36-38][42-44][53-54][3][269-272][274-276][313-314].
POLICY CONTEXT (KNOWLEDGE BASE)
This aligns with the UNCTAD report highlighting AI’s role in helping small businesses in developing countries [S59] and reflects ILO findings on AI’s impact on employment and the need for reskilling [S57][S58]. The emphasis on inclusive policies is echoed in the Global South-South AI summit calling for ethical, non-discriminatory norms [S46].
Strong bilateral and multilateral cooperation between Germany and India (and beyond) is essential for AI development and deployment
Speakers: Moderator, Dr. Bärbel Kofler, Dr. Kusumita Arora, Mr. Jan Noether, Dr. Augustus Azariah
Emphasises that global digital transformation partnerships between Germany and India are essential for leveraging AI benefits (Moderator) Highlights German‑Indian cooperation through the AI Living Lab and Hamburg sustainability commitments, stressing concrete outcomes (Kofler) Highlights that AI transcends national borders and therefore international cooperation is required to develop shared standards, joint research and equitable access to AI technologies (Arora) Introduces a joint German‑Indian master’s programme and cross‑border degree structure (Noether) Suggests leveraging German expertise in data protection (GDPR) and vocational training to strengthen Asian AI ecosystems (Azariah)
The panel repeatedly stresses that Germany-India collaboration – through policy frameworks, joint curricula, and sharing of best-practice regulatory models – is a cornerstone for responsible AI rollout and for building scalable, cross-border AI capacity [1][2][4][214-218][241-247][209-212][161-164][279-282].
POLICY CONTEXT (KNOWLEDGE BASE)
The partnership builds on Germany’s regulatory expertise and India’s application focus, as noted in the AI Algorithms and Global Diplomacy panel [S45] and the German-Asian AI partnership statements [S47][S48]. International cooperation and data sovereignty are also highlighted as crucial for foundation model training [S72].
Capacity development and AI‑focused education at all levels are critical for future AI adoption
Speakers: Dr. Kusumita Arora, Dr. Bärbel Kofler, Mr. Govind Jaiswal, Dr. Augustus Azariah, Video narrator
Stresses the need for clear policy intent and scalable frameworks to embed AI skills across education levels (Arora) Announces the AI Living Lab in Mumbai and integration of AI modules into university curricula (Kofler) Uses the electricity analogy to argue AI will raise living standards and outlines India’s policy measures for reskilling the workforce (Jaiswal) Describes industry‑led faculty certification (e.g., Copilot), large hackathons, and endowment funds to boost academic AI capability (Azariah) Describes living labs as structured spaces where universities and companies co‑create AI solutions, arguing they are essential for hands‑on learning and innovation (Video narrator)
All speakers call for systematic, policy-driven upskilling-from school curricula to university programmes and faculty training-using concrete mechanisms such as living labs, hackathons and mandatory industry exposure to ensure a future-ready talent pool [27-29][46-50][84-95][96-98][136-140][319-322].
POLICY CONTEXT (KNOWLEDGE BASE)
Capacity building is repeatedly stressed in parliamentary AI regulation forums [S66], India’s AI learning initiatives [S67], and broader development agendas for AI capacity in the Global South [S68]. The Indo-German collaboration underscores talent innovation as a strategic goal [S48].
Responsible AI governance, addressing bias, data‑privacy and climate‑friendly computing
Speakers: Dr. Bärbel Kofler, Arthur Rapp, Dr. Augustus Azariah
Calls for responsible AI governance, climate‑friendly computing, open data, and regulatory frameworks to ensure ethical use (Kofler) Warns of dependence on foreign AI platforms, bias, and data‑privacy risks that could affect employment prospects (Rapp) Suggests leveraging German expertise in data protection (GDPR) and vocational training to strengthen Asian AI ecosystems (Azariah)
The speakers converge on the need for AI systems to be governed responsibly, mitigating bias, protecting data and reducing environmental impact, and they point to GDPR and other regulatory models as benchmarks [44-46][214-218][170-179][180-186][216-220][279-282].
POLICY CONTEXT (KNOWLEDGE BASE)
Responsible governance is a core theme in AI security and governance discussions [S49] and European regulatory approaches that balance bias mitigation with practical constraints [S53][S54]. Climate-resilient AI deployment is advocated in climate-focused AI sessions [S50], while data-privacy considerations are detailed in AI privacy reports [S69][S70].
Living labs, sandboxes and other joint innovation spaces are key mechanisms to bridge academia‑industry gaps
Speakers: Dr. Bärbel Kofler, Mr. Jan Noether, Video narrator, Moderator
Announces the AI Living Lab in Mumbai and integration of AI modules into university curricula (Kofler) Reports a formal agreement with Baden‑Württemberg for a joint master’s programme and proposes sandbox environments for joint SME innovation (Noether) Describes living labs as structured learning and innovation spaces where universities and companies collaborate on real, industry‑driven challenges (Video narrator) Invites participants to the AI Academia Industry Innovation Partnership, emphasizing concrete collaborative action (Moderator)
All agree that practical, co-creation environments-whether called Living Labs or sandboxes-are essential to translate AI research into real-world SME solutions and to provide hands-on experience for students and faculty [46-50][241-247][275-276][319-322][287-291].
POLICY CONTEXT (KNOWLEDGE BASE)
Sandboxes and evidence-sandboxes are identified as mechanisms to test compliance and bridge regulator-innovator gaps [S64][S65][S62]. Collaborative innovation spaces are highlighted as essential for academia-industry knowledge exchange [S63] and for supporting SMEs in responsible innovation [S62].
Similar Viewpoints
All three stress that SME participation hinges on accessible skills and demonstrable economic benefits, and that policy must address workforce anxieties to ensure inclusive AI uptake [36-38][42-44][53-54][3][269-272][274-276].
Speakers: Dr. Bärbel Kofler, Mr. Jan Noether, Moderator
Emphasises public fear of AI‑driven job loss and the need to make AI inclusive for all workers, especially SMEs (Kofler) Points out that German and Indian SMEs require clear efficiency and cost‑benefit gains from AI to adopt it (Noether) Claims that access to skills, innovation ecosystems and trusted partnerships for SMEs will decide whether AI becomes an inclusive driver of opportunity (Moderator)
Both propose institutional mechanisms that embed industry exposure directly into education and innovation processes, ensuring graduates can immediately contribute to SME AI projects [96-98][275-276].
Speakers: Mr. Govind Jaiswal, Mr. Jan Noether
Advocates making industry internships and apprenticeship programmes mandatory within AI‑focused curricula to ensure a seamless transition from education to work (Jaiswal) Reports a formal agreement … and proposes sandbox environments for joint SME innovation (Noether)
Unexpected Consensus
Data‑sovereignty and privacy as a foundation for AI collaboration
Speakers: Arthur Rapp, Dr. Augustus Azariah
Warns of dependence on foreign AI platforms, bias, and data‑privacy risks that could affect employment prospects (Rapp) Suggests leveraging German expertise in data protection (GDPR) and vocational training to strengthen Asian AI ecosystems (Azariah)
While Rapp focuses on the strategic risks of foreign AI services and Azariah on transferring GDPR best-practice to Asia, both converge on the principle that robust data-protection frameworks are essential for trustworthy AI cooperation-a link not explicitly highlighted elsewhere in the discussion [170-179][180-186][279-282].
POLICY CONTEXT (KNOWLEDGE BASE)
Data sovereignty is emphasized in discussions on international AI cooperation and foundation model training [S72] and in privacy-focused analyses of AI data protection [S69][S70]. Context-specific data sharing decisions balancing privacy and cultural preservation are discussed in inclusive AI literature [S71].
Overall Assessment

The panel shows strong convergence on four pillars: (1) inclusive AI for SMEs and workers, (2) deepening Germany‑India cooperation, (3) systematic capacity development through curricula, living labs and faculty up‑skilling, and (4) responsible AI governance covering bias, data protection and sustainability.

High consensus – most speakers echo each other’s points, creating a solid basis for coordinated policy actions and joint programmes such as the AI Living Lab and the AI Academia‑Industry Innovation Partnership.

Differences
Different Viewpoints
Criteria for SME adoption of AI
Speakers: Mr. Jan Noether, Dr. Bärbel Kofler
SMEs require clear efficiency and cost‑benefit gains before investing in AI (Noether) AI must be made accessible to SMEs by closing the power gap, irrespective of immediate ROI (Kofler)
Noether argues that small and medium-sized enterprises will only adopt AI when they can see concrete efficiency, cost-saving and innovation benefits [269-272][274-276], while Kofler stresses that policy must ensure AI is usable by all SMEs by overcoming the power gap, suggesting access should be provided even without proven ROI [42-44][53-54].
POLICY CONTEXT (KNOWLEDGE BASE)
The UNCTAD report outlines practical AI use-cases for SMEs, providing emerging criteria for adoption [S59], while Indian perspectives note the compliance burden on MSMEs lacking legal resources [S61].
Approach to data sovereignty and platform dependence
Speakers: Arthur Rapp, Dr. Bärbel Kofler
Risk of dependence on non‑European AI platforms, bias and data‑leakage requires sovereign, unbiased AI ecosystems (Rapp) Promotion of open data and cooperation without explicit focus on platform sovereignty (Kofler)
Rapp warns that reliance on foreign AI services creates strategic vulnerabilities, bias and potential data exploitation, calling for sovereign AI development and safeguards [170-179][180-186], whereas Kofler emphasizes open data, climate-friendly computing and collaborative frameworks, without addressing the issue of foreign platform dependence [44-46][214-218].
POLICY CONTEXT (KNOWLEDGE BASE)
Debates on data sovereignty versus platform reliance are reflected in inclusive AI discussions on linguistic diversity and data sharing decisions [S71] and in broader calls for sovereign data frameworks in AI collaborations [S72].
Primary driver of AI skills development
Speakers: Mr. Govind Jaiswal, Dr. Augustus Azariah
Government‑led reforms (NEP 2020, dual education, mandatory internships) are the main mechanism for reskilling (Jaiswal) Industry must lead faculty certification, hackathons and endowment funds because current faculty lack AI awareness (Azariah)
Jaiswal outlines a state-driven strategy that embeds AI into curricula, creates research parks and makes industry exposure mandatory for students [84-98][96-98], while Azariah argues that the current faculty are not equipped and that industry should certify teachers and provide resources to bridge the gap [130-138][139-144].
POLICY CONTEXT (KNOWLEDGE BASE)
Capacity-building forums identify governments, industry, and academia as key drivers, with India emphasizing talent pipelines [S48] and parliamentary insights highlighting policy and employer roles [S66].
Unexpected Differences
Shift from AI development to deployment vs continued emphasis on AI development
Speakers: Moderator, Arthur Rapp
Moderator states the strategic priority has moved from AI development to effective deployment [2] Rapp focuses on the need to develop sovereign AI platforms to avoid dependence on foreign services [170-179]
The moderator’s claim that the focus is now on deployment rather than creation appears at odds with Rapp’s emphasis on building independent AI capabilities, which is a development‑oriented stance. This contrast was not anticipated given the overall deployment‑focused framing of the session.
POLICY CONTEXT (KNOWLEDGE BASE)
Recent summit sessions argue for moving beyond pilots to real-world deployment for climate-resilient systems [S50][S52], while other analyses stress the need to maintain research momentum, reflecting a tension in policy priorities.
Bias and language exclusion acknowledged by both but differing solutions
Speakers: Dr. Bärbel Kofler, Arthur Rapp
Kofler notes bias in data and exclusion of mother-language speakers as a challenge to inclusive AI [218-219] Rapp stresses that bias and language exclusion are systemic risks requiring sovereign, bias-free AI ecosystems [216-220]
Both recognize bias, yet Kofler’s response is to incorporate open data and responsible AI within existing frameworks, whereas Rapp calls for a fundamentally sovereign AI architecture to eliminate bias, revealing an unexpected divergence in proposed remedies.
POLICY CONTEXT (KNOWLEDGE BASE)
Governance discussions differentiate between harmful bias mitigation and broader bias concerns [S54], and inclusive AI literature stresses linguistic diversity and context-specific data sovereignty as part of bias solutions [S71].
Overall Assessment

The panel showed broad consensus on the importance of AI for economic development, the need for international cooperation, and the urgency of upskilling. However, clear disagreements emerged around how to incentivise SME adoption, whether AI skill development should be led by governments or industry, and how to handle data sovereignty and platform dependence.

Moderate – while participants share overarching goals, they diverge on implementation pathways, especially concerning SME ROI requirements, the balance between state‑driven curricula and industry‑led faculty training, and the governance of AI data and platforms. These differences could affect the speed and shape of collaborative initiatives, requiring negotiated compromises to align policy, industry, and academic actions.

Partial Agreements
Arora highlights the need for policy direction and scalable mechanisms to embed AI across education levels [27-29][209-212], while Kofler points to concrete bilateral initiatives such as the AI Living Lab in Mumbai and Hamburg sustainability commitments as ways to achieve inclusive AI [46-50][214-218]. Both agree on the goal of cooperation but differ on the primary means.
Speakers: Dr. Kusumita Arora, Dr. Bärbel Kofler
Both call for international cooperation to integrate AI skills and ensure inclusive outcomes Arora stresses clear policy intent and scalable frameworks; Kofler stresses concrete projects like the AI Living Lab
Noether proposes sandbox environments and a joint master’s programme to foster cross‑border SME innovation [275-276][161-164], while Azariah describes industry‑driven faculty certification and large‑scale hackathons to bring academia and industry together [136-140][141-144]. They share the goal of academia‑industry linkage but differ on the concrete format.
Speakers: Mr. Jan Noether, Dr. Augustus Azariah
Both see the importance of linking academia and industry for AI innovation Noether proposes sandboxes and joint master programmes; Azariah proposes industry‑led faculty certification and hackathons
Takeaways
Key takeaways
AI will transform work but must be managed to avoid fear of job loss; it can also create new jobs if inclusive policies are adopted (Kofler, Jaiswal). Closing the “power gap” between large corporations and SMEs is essential so that small and medium enterprises can both use and create AI solutions (Kofler, Noether). Education and training are critical: integration of AI modules into university curricula, dual‑education/apprenticeship models, faculty certification, and large‑scale hackathons are being deployed, especially in India (Jaiswal, Azariah). International cooperation—particularly Germany‑India (and broader Asia)—is already materialising through AI Living Labs, joint master’s programmes, and the AI Academia‑Industry Innovation Partnership, aiming for concrete, measurable outcomes (Kofler, Noether, Azariah). Responsible AI governance is required to address bias, language exclusion, data‑sovereignty, and environmental impact; European GDPR experience is highlighted as a model (Kofler, Rapp). Key application domains identified for sustainable impact include healthcare, agriculture, water management, energy, and skills development (Noether).
Resolutions and action items
Launch of the AI Living Lab at the University of Mumbai to bring together students, SMEs, and industry partners for real‑world AI projects (Kofler). Establishment of a joint German‑Indian master’s programme with the University of Baden‑Württemberg, split between India and Germany (Noether). Commitment by the Indian Ministry of Education to embed AI components across curricula, expand research parks, and implement dual‑education/apprenticeship models (Jaiswal). Industry‑led faculty certification initiatives (e.g., Copilot) and endowment funds to enable faculty to develop AI‑driven research and patents (Azariah). Creation of sandbox environments for SME‑focused AI innovation involving German and Indian partners (Noether). Agreement to report on progress and maintain transparent, accountable frameworks under the Hamburg Sustainability Declaration (Kofler). Planning of follow‑up meetings to monitor the AI Academia‑Industry Innovation Partnership in Asia (Moderator).
Unresolved issues
How to mitigate dependence on non‑European AI platforms and ensure data sovereignty and privacy for researchers and companies (Rapp). Specific mechanisms for financing and scaling AI adoption in SMEs, especially in tier‑2/3 regions, remain undefined. Concrete metrics and timelines for measuring the impact of education reforms and Living Lab outcomes were not detailed. The extent of regulatory harmonisation needed between Germany, India, and other Asian partners to support responsible AI was not fully resolved. Strategies to address language bias and inclusion of non‑English speakers in AI tools were mentioned but not operationalised.
Suggested compromises
Combining Germany’s cautious, risk‑averse investment approach with India’s rapid implementation speed to create mutually beneficial SME innovation projects (Noether). Balancing regulatory safeguards for responsible AI with the need for flexible, scalable frameworks that enable quick adoption by SMEs (Kofler). Aligning industry demands for immediate, demonstrable ROI with academic goals of long‑term research and skill development through joint curricula and living labs (Azariah, Noether).
Thought Provoking Comments
We need to close the power gap … we are opening an AI Living Lab at University of Mumbai … bringing together students and small‑media enterprises that normally don’t have access to AI.
She identifies the structural inequality between large and small enterprises and between the global north and south, and proposes a concrete mechanism (the Living Lab) to democratise AI access and training.
This comment shifted the discussion from abstract policy to a tangible initiative, prompting other panelists to reference concrete cooperation models and setting the stage for the later focus on Living Labs as a central theme.
Speaker: Dr. Bärbel Kofler
When electricity was introduced it created disruption but ultimately improved quality of life; similarly AI will cause transition, and we must ensure seamless re‑skilling – the new education policy 2020, dual education system, mandatory apprenticeships, and industry‑academia collaboration are already being rolled out in India.
He uses a historical analogy to normalise technological disruption and outlines specific policy actions (NEP 2020, dual system) that address skill gaps, linking education reform directly to AI readiness.
His remarks broadened the conversation to vocational training and systemic reforms, influencing later mentions of dual university programmes and reinforcing the need for practical, industry‑linked curricula.
Speaker: Mr. Govind Jaiswal
We see many AI‑generated CVs from fresh graduates; faculty aren’t trained to teach practical AI tools like Copilot. We ran a hackathon with 18,000 students and certified over 1,000 faculty, aiming to reach millions, especially in tier‑2/3 cities.
He spotlights a concrete gap in faculty capability and the prevalence of superficial AI knowledge among graduates, proposing a scalable solution through certification and outreach.
This observation prompted discussion on industry‑academia partnerships, highlighted the untapped talent in smaller cities, and reinforced the need for capacity‑building beyond student curricula.
Speaker: Dr. Augustus Azariah
AI can transform healthcare, agriculture, water management, and energy; we have just signed a dual‑university master’s programme where two‑thirds is taught in India and one‑third in Germany.
He expands the scope of AI impact to critical societal sectors and introduces a concrete bilateral educational programme, illustrating cross‑border collaboration in action.
His comment redirected the dialogue toward sector‑specific opportunities and concrete joint programmes, encouraging other speakers to cite similar collaborative models.
Speaker: Mr. Jan Noether
There is a big risk of dependence on non‑European AI platforms; data bias and sovereignty issues arise when AI tools train on our data and could later be withdrawn or used against us. Even students use AI for career decisions, raising ethical concerns.
He raises the often‑overlooked dimensions of AI dependence, bias, and data protection, linking technical adoption to geopolitical and ethical implications.
This comment introduced a critical perspective on responsible AI, leading Dr. Kofler to emphasise the need for inclusive, accountable frameworks and influencing the discussion on international cooperation.
Speaker: Arthur Rapp
International cooperation must overcome the power and creator gaps, produce concrete outcomes, and align AI deployment with the Sustainable Development Goals – for example through the Living Lab in Mumbai that brings together government, academia, and SMEs.
She synthesises earlier points into a clear call for actionable, outcome‑focused collaboration, linking AI policy to broader development goals.
This reinforced the earlier Living Lab concept, shifted the tone toward commitment and accountability, and set the agenda for the concluding remarks and the video presentation.
Speaker: Dr. Bärbel Kofler
Overall Assessment

The discussion moved from high‑level policy framing to concrete, actionable initiatives largely because of a few pivotal remarks. Dr. Kofler’s introduction of the AI Living Lab and her emphasis on closing the power gap provided a tangible anchor that reframed the conversation. Mr. Jaiswal’s historical analogy and description of India’s dual education reforms added depth to the skill‑development narrative, while Dr. Azariah’s exposure of faculty readiness gaps and his certification programme highlighted practical industry‑academia challenges. Jan Noether’s sector‑wide vision and the announcement of a joint master’s programme broadened the scope to real‑world applications. Arthur Rapp’s warning about platform dependence and data sovereignty injected a necessary ethical dimension, prompting calls for responsible AI. Collectively, these comments redirected the dialogue from abstract aspirations to specific mechanisms, encouraged cross‑sectoral thinking, and underscored the need for measurable outcomes, shaping the panel into a forward‑looking, solution‑oriented exchange.

Follow-up Questions
How can the power gap between large corporations and small/medium enterprises be overcome to ensure equitable access to AI technologies?
Addressing this gap is essential for inclusive economic growth and to prevent AI benefits from being concentrated in a few large players.
Speaker: Dr. Bärbel Kofler
What are the disparities in access to competing data centers between the Global North and Global South, and how can they be mitigated?
Data center access influences AI performance and cost; understanding the gap is crucial for balanced global AI development.
Speaker: Dr. Bärbel Kofler
What regulatory frameworks are needed to ensure decent work conditions in AI‑driven workplaces?
Ensuring fair labor standards protects workers from precarious employment as AI reshapes job tasks.
Speaker: Dr. Bärbel Kofler
What is the optimal timeline and strategy for reskilling the workforce to transition smoothly to AI‑enabled roles within the next few decades?
A clear transition plan helps policymakers and industry avoid large-scale displacement and maintain productivity.
Speaker: Govind Jaiswal
How can vocational training and dual‑education models be expanded to embed AI competencies across all education levels?
Aligning curricula with industry needs ensures graduates are job‑ready and supports lifelong learning pathways.
Speaker: Govind Jaiswal
What certification programmes and training models are required to upskill university faculty in generative AI tools such as Copilot?
Faculty competence directly impacts the quality of AI education delivered to students.
Speaker: Augustus Azariah
How can endowment funds be structured to enable faculty to innovate, develop AI models, and file patents?
Financial support for academic research can accelerate AI breakthroughs and strengthen university‑industry links.
Speaker: Augustus Azariah
What does the talent distribution in tier‑2 and tier‑3 Indian cities reveal about hiring practices, and how can blind‑selection processes be refined?
Understanding hidden talent pools can improve diversity and broaden the AI talent base beyond elite institutions.
Speaker: Augustus Azariah
How can cross‑border sandbox environments be designed to enable German and Indian SMEs to co‑create AI solutions safely and efficiently?
Sandboxes lower risk for SMEs, fostering experimentation and rapid innovation collaboration.
Speaker: Jan Noether
What concrete financial and operational benefit models are needed to persuade risk‑averse German SMEs to adopt AI technologies?
Demonstrating clear ROI is vital for SME investment decisions in AI.
Speaker: Jan Noether
What are the risks of dependence on non‑European AI platforms regarding data sovereignty, bias, and research freedom?
Dependence on external AI services could compromise autonomy and introduce hidden biases.
Speaker: Arthur Rapp
How does the use of AI tools for drafting research proposals affect data protection and intellectual‑property security?
Ensuring that confidential research ideas are not inadvertently exposed is critical for innovation protection.
Speaker: Arthur Rapp
To what extent does AI influence students’ career and subject choices, and what are the implications for higher‑education planning?
AI‑driven guidance may reshape enrollment patterns, affecting workforce supply in various sectors.
Speaker: Arthur Rapp
How can international cooperation programmes concretely link AI development to the Sustainable Development Goals (SDGs)?
Aligning AI initiatives with SDGs ensures that technological progress contributes to broader societal objectives.
Speaker: Dr. Bärbel Kofler
What measures can make AI computing infrastructure more climate‑friendly, reducing energy consumption and water usage?
Sustainable AI deployment is necessary to limit environmental impact while scaling technology.
Speaker: Dr. Bärbel Kofler
How effective are the AI Living Labs and the AI Academia‑Industry Innovation Partnership in Asia at delivering job‑ready talent and measurable innovation outcomes?
Evaluating these programmes will inform future scaling and policy support.
Speaker: Moderator (referencing initiative)
What curriculum and pedagogical approaches are needed to introduce AI concepts at elementary and primary school levels?
Early exposure builds foundational AI literacy and prepares future generations for an AI‑centric world.
Speaker: Augustus Azariah

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.