IndoGerman AI Collaboration Driving Economic Development and Soc
20 Feb 2026 14:00h - 15:00h
IndoGerman AI Collaboration Driving Economic Development and Soc
Session at a glance
Summary
This discussion focused on the Indo-German collaboration in artificial intelligence and innovation, featuring speakers from government, research institutions, and industry leaders. The session was organized by Fraunhofer Institute, which has been operating in India for 18 years and has earned over 70 million euros from research contracts with Indian organizations. Georg Enzweiler highlighted the recent India-Germany AI Pact launched by Ministers Vaishnaw and Wildberger, emphasizing collaboration across government, industry, research, and innovation with a focus on AI for social good.
Dr. Thomas Kuhn from Fraunhofer presented their approach to “augmented intelligence,” emphasizing trustworthy AI, industrial AI applications, and data spaces that can handle up to 10,000 transactions per second. He discussed virtual colleagues that preserve company knowledge, uncertainty wrappers for AI responses, and federated training systems. Dr. Rajkumar Upadhyay from CDOT outlined India’s AI achievements, including ranking third globally in AI competitiveness, investing $2 billion in AI missions, and developing indigenous 4G and 5G technologies. He emphasized areas for collaboration including smart manufacturing, agriculture, cybersecurity, quantum communication, and fraud prevention systems.
Professor Dr. Kristina Sinemus shared Germany’s strategy of technology serving people, highlighting their $60 billion funding program supporting 170 AI startups in healthcare, agriculture, and other sectors. Industry leaders from SAP, Bosch, Mercedes-Benz, and Averio discussed practical AI implementations, from autonomous driving and enterprise applications to manufacturing efficiency improvements. The consensus emerged that Indo-German AI collaboration represents a significant opportunity, combining Germany’s precision engineering expertise with India’s scale and speed, built on shared democratic values and mutual technological strengths.
Keypoints
Major Discussion Points:
– Indo-German AI Partnership and Collaboration: The discussion centered around the newly launched India-Germany AI Pact, emphasizing how both countries can leverage their complementary strengths – Germany’s precision engineering expertise and India’s scale and talent pool – to create mutually beneficial AI solutions.
– Trustworthy and Responsible AI Development: Multiple speakers emphasized the importance of developing AI that is reliable, explainable, and ethical, with focus on preserving human intelligence at the core (“augmented intelligence”) and ensuring AI serves people rather than the other way around.
– AI Applications Across Key Sectors: Extensive discussion of AI implementation in manufacturing (Industry 4.0), agriculture (crop monitoring, yield optimization), healthcare (diagnostics, personalized medicine), and cybersecurity, with specific examples from both German and Indian organizations.
– Data Spaces and Knowledge Preservation: Significant attention given to creating secure, rule-based data sharing platforms and using AI to preserve institutional knowledge, particularly when experienced workers retire, through “virtual colleagues” and federated learning approaches.
– Scaling AI Innovation from Startups to Enterprise: Discussion of how to bridge the gap between research and market implementation, with examples of government funding programs, startup ecosystems, and enterprise-level AI deployment challenges and opportunities.
Overall Purpose:
The discussion aimed to explore and promote concrete collaboration opportunities between India and Germany in AI development and deployment, focusing on how their partnership can drive both economic growth and social good while addressing shared challenges in manufacturing, agriculture, healthcare, and cybersecurity.
Overall Tone:
The tone was consistently optimistic and collaborative throughout, characterized by mutual respect and enthusiasm for the partnership potential. Speakers demonstrated genuine excitement about technological possibilities while maintaining a pragmatic focus on real-world applications and responsible development. The discussion maintained a professional yet warm atmosphere, with participants showing appreciation for each other’s expertise and contributions to the Indo-German relationship.
Speakers
Speakers from the provided list:
– Anandi Iyer – Head of Fraunhofer in India (18 years), moderator of the session
– Georg Enzweiler – Government official, delivered special address on Indo-German AI collaboration
– Dr. Thomas Kuhn – Head of Division of Embedded Systems at Fraunhofer IESE, expert in AI and data spaces
– Dr. Rajkumar Upadhyay – CEO of Center for Development of Telematics (CDOT), expert in telecommunications, quantum communication, and cybersecurity
– Prof. Dr. Kristina Sinemus – Minister for Digitalization and Innovation (Germany), background in biotechnology
– Dattatri Salagame – Representative from Robert Bosch Software Solutions, expert in manufacturing and AI deployment
– Sindhu Gangadharan – CEO (company not explicitly mentioned but appears to be SAP based on context), expert in enterprise application software
– Anshuman Awasthi – CTO of Mercedes-Benz Research and Development Center, expert in automotive AI and innovation
– Prashant Doreswamy – Representative from Averior (formerly Continental), expert in automotive technology and R&D
Additional speakers:
– Murali Nair – From Bertelsmann Stiftung (think tank), mentioned as producing knowledge papers on India-Germany relations but did not speak in the provided transcript
Full session report
This comprehensive discussion on Indo-German collaboration in artificial intelligence and innovation brought together senior government officials, research leaders, and industry executives to explore concrete partnership opportunities and address the transformative potential of AI across multiple sectors. The session was moderated by Anandi Iyer, Head of Fraunhofer India, which has maintained a significant presence in India for 18 years and generated over 70 million euros from research contracts with Indian organisations, demonstrating the depth of existing Indo-German technological cooperation.
Strategic Partnership Framework and Policy Foundation
The discussion was anchored by the recent launch of the India-Germany AI Pact, announced by Ministers Vaishnaw and Wildberger only two days prior to the session. Georg Enzweiler emphasised that this partnership represents a natural evolution of longstanding scientific cooperation between the two nations, with AI predicted to contribute between $5-15 trillion to global GDP by 2030. The pact focuses on implementation-driven collaboration across government, industry, research, skill development, and innovation, with particular emphasis on AI for social good, manufacturing applications, talent mobility, and joint research infrastructure.
The strategic rationale for this partnership lies in the complementary strengths of both nations. India accounts for 15% of the global AI talent pool and maintains the highest AI skill penetration rate worldwide, ranking third globally in AI research and development after the United States and China. Germany brings precision engineering expertise, robust regulatory frameworks, and substantial investment in AI innovation. As Anandi Iyer noted, Fraunhofer alone produces “two patents per working day” and has created foundational technologies like “MP3, white LEDs” that demonstrate German innovation capabilities.
Technological Approaches and Research Focus
Dr. Thomas Kuhn from Fraunhofer IESE presented a distinctive approach to AI development, emphasising “augmented intelligence” rather than artificial intelligence, positioning human intelligence at the core of AI systems. This philosophical framework shaped much of the subsequent discussion, moving away from concerns about AI replacement towards collaborative human-AI interaction models.
Fraunhofer’s research priorities centre on several critical areas addressing practical industry needs. Their work on trustworthy AI includes developing uncertainty wrappers that provide confidence levels for AI responses, essential for safety-critical applications. The concept of “virtual colleagues” represents an innovative approach to knowledge preservation, where AI systems learn from experienced workers to retain institutional knowledge when employees retire—a particular challenge for small and medium-sized enterprises.
The organisation’s data spaces technology enables secure, rule-based data sharing capable of handling up to 10,000 transactions per second. This infrastructure supports federated learning approaches where multiple organisations can collaboratively train AI models without sharing sensitive raw data, addressing both privacy concerns and the need for diverse training datasets.
Sector-Specific Applications and Implementations
Healthcare and Social Innovation
Prof. Dr. Kristina Sinemus outlined Germany’s substantial investment in AI for social good, with $60 billion allocated to funding programmes supporting 170 AI startups. She emphasised the principle that “technology must serve people, not the other way around.” Notable healthcare applications include DEMO, a load-bearing robotic wheelchair that transforms into a walking robot capable of navigating stairs and uneven terrain, which received 1.8 million euros in funding. The RISCA project focuses on cardiovascular risk assessment through AI-powered ECG analysis, enabling early disease detection and preventive interventions.
These examples illustrate Germany’s commitment to ensuring AI development is guided by principles of social coherence, fairness, and trust, with emphasis on practical projects delivering tangible benefits for citizens and businesses.
Agriculture and Environmental Applications
Agricultural applications represent a significant opportunity for Indo-German collaboration. German projects include early detection of plant diseases through automated satellite data analysis, enabling farmers to identify crop stress and disease at early growth stages. This approach can simultaneously reduce pesticide usage whilst increasing productivity, addressing both environmental and economic concerns.
The integration of AI with space technology for agricultural monitoring demonstrates sophisticated technical capabilities being developed, with potential for significant impact on food security and sustainable farming practices.
Manufacturing and Industry 4.0
The manufacturing sector showcased extensive AI implementation across multiple dimensions. Bosch, represented by Dattatri Salagame, operates on two fronts: deploying AI-centric products such as autonomous driving systems and AI cockpits, whilst simultaneously using AI to transform internal engineering and software development processes. Salagame candidly acknowledged that “we have anxiety” about paradigm shifts in engineering and software development using AI, reflecting broader industry concerns about transforming established processes.
Averio (formerly Continental) reported achieving over 20% efficiency improvements in research and development through AI implementation. Their applications include quality enhancement in manufacturing processes, automated code generation using GitHub Copilot for development workflows, and requirements engineering using RecNet to address complexity challenges in automotive technology development.
Anshuman Awasthi from Mercedes-Benz highlighted their pioneering role as the first automotive company to integrate AI into vehicles in 2019, with much of this development occurring at their Bangalore research centre. Their current focus encompasses both customer experience enhancement and operational efficiency improvements.
Cybersecurity and Digital Infrastructure
Dr. Rajkumar Upadhyay from CDOT presented India’s remarkable achievements in AI-powered cybersecurity, processing data at rates of 10 terabytes per second to identify and block cyber attacks in real-time. India’s systems handle the challenge of distinguishing between 1 million regular international calls versus 15 million spoofed calls per day, with sophisticated capabilities including the ability to make authentication decisions within 5 milliseconds at national scale.
The SanchalSathi.gov.in platform represents an innovative digital intelligence system that integrates multiple stakeholders—telecommunications, banking, and law enforcement—to create comprehensive fraud prevention capabilities. The Financial Risk Indicator (FRI) system evaluates transaction safety in real-time, preventing fraudulent money transfers before they occur.
Enterprise AI Implementation and Challenges
Sindhu Gangadharan from SAP addressed the unique challenges of implementing AI across enterprise applications used by 87% of global businesses. With SAP systems embedded in workflows across 12 industries, the company bears significant responsibility for ensuring AI implementation maintains compliance, security, and ethical standards whilst delivering business value.
The enterprise AI challenge involves embedding AI across core business processes—from lead-to-cash and total risk management to design-to-operate and strategic sourcing—whilst maintaining explainability, transparency, fairness, and auditability. This becomes particularly critical in autonomous workflows where human oversight is reduced but accountability requirements remain high.
Innovation Ecosystems and Talent Development
The discussion revealed significant evolution in Indo-German collaboration models, moving from traditional client-service relationships to genuine co-creation partnerships. Anandi Iyer emphasised that current collaborations involve joint problem-solving for India-specific challenges rather than simple technology transfer, representing a maturation of the bilateral relationship.
India’s demographic dividend, combined with rapid innovation capabilities, complements Germany’s engineering precision and regulatory expertise. This synergy is evident in concrete outcomes such as Mercedes-Benz’s 2019 AI automotive applications being developed primarily in Bangalore, demonstrating how innovation centres have shifted globally.
Future Technology Development and Collaboration Areas
Several emerging technology areas were identified as prime candidates for enhanced collaboration. India’s successful development of indigenous 4G and 5G technologies, launched by the Prime Minister on September 25 with nearly 170,000 base stations generating 5 petabytes of data, demonstrates significant technical capabilities. However, for 6G development, India explicitly seeks collaborative approaches with international partners, representing a strategic opportunity for Indo-German cooperation.
Quantum communication technology presents another collaboration frontier, with both countries recognising the urgency of developing quantum-safe encryption methods. Dr. Upadhyay chairs India’s National Quantum Communication Hub alongside IIT Madras, providing institutional infrastructure for bilateral cooperation.
Challenges and Considerations
Despite the optimistic tone, speakers acknowledged significant challenges in AI implementation. The discussion highlighted the critical importance of ensuring AI development remains inclusive. Dr. Upadhyay emphasised concerns about AI potentially widening inequalities, while Prof. Dr. Sinemus noted that AI will not automatically lead to better outcomes—success depends on deliberate choices regarding funding priorities, regulatory frameworks, and inclusion strategies.
Technical challenges include developing practical testing criteria for trustworthy AI, creating scalable cross-border data sharing mechanisms whilst maintaining security and sovereignty, and addressing workforce transformation concerns as AI reshapes traditional roles and processes.
Conclusion and Strategic Implications
The session demonstrated remarkable consensus among participants regarding the strategic value of Indo-German AI collaboration and the importance of human-centred AI development. The discussion moved beyond theoretical possibilities to practical implementation frameworks, with multiple examples of successful deployments across sectors.
The partnership leverages complementary strengths—Germany’s precision engineering, regulatory expertise, and substantial AI investments combined with India’s scale, talent pool, innovation speed, and practical deployment experience. Both nations share democratic values that provide a foundation for ethical AI development focused on social good and inclusive growth.
The newly launched India-Germany AI Pact provides an institutional framework for sustained collaboration, whilst existing successful partnerships like the Fraunhofer-CDOT MOU demonstrate proven models for co-creation. The discussion revealed significant opportunities in manufacturing, agriculture, healthcare, cybersecurity, quantum technologies, and next-generation telecommunications.
Success will require continued attention to ensuring AI development serves human needs, maintains trustworthiness and explainability, addresses workforce transformation challenges, and creates inclusive growth that benefits all segments of society. The partnership’s emphasis on practical applications delivering tangible benefits, combined with shared commitment to responsible AI development, positions Indo-German collaboration as a model for international cooperation in the AI era.
Session transcript
And we are delighted to note that many of the activities that were outlined in the MOU have already kick -started. So I think to that extent, it’s fantastic that Dr. Padhya is here today and will share with us his ideas on where an Indo -German collaboration in innovation and AI can have a value proposition for both countries. I’m also delighted to have my own colleague, Dr. Thomas Kuhn, who has come all the way from Germany from the Fraunhofer Institute of Experimental Software. He will be sharing some of our experiences and competencies in AI, particularly with focus on workplace changing, which is, of course, one of the biggest topics we have today, but also manufacturing, agriculture, and health.
And I think one of the key topics that Fraunhofer can present to the Indian audience is data space. We have created a secure data space in the cloud for many years where challenges remain. Industries are brought in and discussed. So you will hear a lot from him. I’m particularly delighted that four Bangalorean CEOs are here today. We have with us Sindhu Gangadharan, who needs no introduction, I think. Anyone knows that, and who doesn’t know is coming under a stone. So we also have Anshuman Eversi for CTO of Mercedes -Benz Research and Development Center in Bangladesh. And we have Dattatri Salagame from Robert Bosch Software Solutions. And, of course, Prashant, he’s sitting at the back, Doris Rami from Averior, which was earlier Continental.
So as you can see, a power -packed industry captain session, which will follow after the panel. And last but not the least, we have Murali Nair from Bretelsmann Stiftung, which is a think tank, which has been producing products. A lot of knowledge papers around India and Germany, and actually positioning India as a positive partner for Germany long before Germany even started looking at us so seriously. So thank you for that, Murali. I’m standing here in front of you today as the head of Raunafa in India for the last 18 years extremely proud that Raunafa is one of the largest applied research ecosystems in the world we have 76 institutes in Germany we are present in more than 80 countries we produce two patents for every working day we are also the inventors of the LP3 white LEDs and many such inventions but what really singles us out in this innovation corridor is the fact that we take research from lab to the market in the shortest period of time so this is an area where India really needs support and we are delighted that we were one of the first movers coming into India 18 years ago and even today there are very few international R &D organizations that are active in India you We have been earning more than 70 million euros in the last 10 years from research contracts with Indian industry, government and research organizations.
And we have an absolutely amazing network of partners in India, including organizations like CDOT, but also with Indian industry, which is really gearing up for innovation. So I think we came in at the right time and took advantage of this, you know, innovation trajectory that has picked up in India. And it’s exciting to see what’s unfolding before us. I have to say that today what we are bringing together is really a thought -provoking session. We only have 15 minutes, so I would like to get on with business immediately. And we hope that some of the thoughts that we are leaving behind with you today will resonate and will actually trigger a long -term dialogue and engagement so that we can take the Indo -German innovation collaboration to greater heights.
I would now like to invite Mr. Georg Enzweiler. To kindly deliver his special address.
Good morning, ladies and gentlemen. I don’t know whether it’s maybe that we are just as crazy as you are sometimes that we are big fans of Karnova here in India. It’s always delightful to be invited by you here in India. You do amazing work here in the country. I wish we’d have several Anandi ears here in the country to multiply your efforts. So thank you very much for the invitation. And I’m impressed by the distinguished panel of speakers today and of panelists here. Thank you. All of the panelists and speakers here today are drivers of technology, and so it makes sense that you are also at the forefront of innovative AI technology. From increasing crop revenue, improving diagnostics, or minimizing errors in manufacturing, AI undoubtedly has huge potential for social and economic good.
AI is predicted to contribute between $5 and $15 trillion to the global GDP by 2030. But there are also questions, of course. How can we ensure that this growth is inclusive? How do we minimize negative effects for people and the planet? What kind of effect would this have on labor markets, for example? After all, the motto of this summit is actually welfare for all. India, for one, has ambitious goals to create massive computing infrastructure in big parts powered by green energy. Germany is investing in so -called AI lighthouses, which foster AI innovations for climate and environmental protection. Since 2020, Germany has funded over 60 projects leveraging AI for sustainability. The topics range from wildfire prevention and renewable energy to biodiversity monitoring.
And circular economy. that government, academia, and industry work hand -in -hand. By promoting research and development, creating a clear regulatory environment, and investing in the training of skilled workers, we can unlock a broader potential for AI. India shows huge potential here. It accounts for 15 % of the global AI talent pool and has the highest AI skill penetration rate. In terms of research and development in AI, India ranks third in the world after the U.S. and China. India and Germany, with their longstanding scientific partnership, are natural partners in creating solutions that are sustainable and inclusive for all. Hence, only two days left. Ministers Vaishnaw and Wildberger launched the India -Germany IA Pact, a new partnership focused on implementation -driven collaboration across government, industry, research, skill development, and innovation.
It will include aspects as AI for industry and manufacturing, talent, skills, and mobility, joint research, innovation, and infrastructure, and overall AI for social good. So I very much look forward to the discussion of today’s session, that it can give fruit for thought on this most relevant topic. Thank you again for the invitation, and I wish all of us an interesting discussion on, many of these pertinent topics. Thank you.
Thank you for your kind words, Mr. Ensweiler. We now have the pleasure of hearing Dr. Thomas Kuhn, who heads the Division of Embedded Systems in Fraunhofer IESE. As you all know, we talk artificial intelligence. In Fraunhofer, we call it augmented intelligence, which means that human intelligence is still at the core of what we’re talking in terms of AI. It’s a pleasure to bring Dr. Thomas Kuhn to India. The floor is all yours.
Yes, thank you for inviting me. Yeah, what is Fraunhofer? doing in the field of AI. You all know AI is on one hand large language models, mostly driven by the US. We have huge open source models coming from China. So what is Fraunhofer contributing? And our goal is to bridge industry and academia to support the industry in creating new products and bringing ideas into practice. And I’ve chosen here a few highlights on what we can do as Fraunhofer. So for example, when you speak about AI, the question is, how reliable is the result of AI? Do you get concrete, do you get reliable information? That’s one topic, trustworthiness. How can we achieve trustworthy responses? That’s something that Fraunhofer is researching on.
For sustainable growth, it’s also very important to preserve knowledge that is available in the company, especially if people retire. Small and medium -sized enterprises have exactly this problem that knowledge is leaving, and you cannot get this knowledge back because the new employees simply don’t have this knowledge anymore. So how can you preserve knowledge? We call this industrial AI, so bringing AI models into practice, creating specific, specialized AI models based on company data, based on sensitive data, not personal data of persons, but sensitive data for business, sensitive data for companies, so that they can support these companies with these needs that the AI can provide. For example, it creates a colleague that is an expert for a specific device and that can help.
humans to work more efficiently with this device. And last but not least, as my colleague already did introduce, data spaces are a key technology for that because for AI, we need to be able to train AI and therefore we need a reliable access to data and a way to share data, but based on rules. And that’s what data spaces are meant for. With Raumhofer, we have the ability to instantiate data spaces that scale up to 10 ,000 transactions per second, and that’s quite a lot. So Raumhofer is structured into alliances, and the most important one is the big data and the artificial intelligence. Alliances, so more than 30 institutes that team up with AI, in creating AI strategies, each institute with its own field of competence, but all of us support best practices, studies, expert opinions, and we support all companies in creating new solutions.
And as you can see, we have a lot of fields that our institutes are working on, life sciences, healthcare when it comes to diagnostics, to personalized medication, logistics and mobility for optimizing supply chains, making them more resilient, production and industry for that all, very important. We also need to keep production in Europe to remain resilient, so it must be economic to produce in Europe. Energy and environment. Renewable energies. Smart goods. self -managing grids. That’s very important topics also for AI and big data, business and finance, security, to make sure that our data is well protected. I cannot go into detail for all of these topics, so we’ve collected a few samples on web we are doing from embedded AI, image analysis, collaborative transport, cancer diagnostics, swarm intelligence, of course also defense topics.
I cannot go too much into detail here but AI has a huge impact and why the ways regarding general purpose LLMs is not our ways. The applications that can be created with AI, the transformation of industry that is very well our knowledge. to bring AI to use. for industry. And therefore also to use for all, and then we are back at the topic of this event here, welfare for all and happiness for all, because every industrial revolution in the end did yield much more wealthiness for everybody. I have brought some highlights, and I promise I won’t go into details here, no worries. So the virtual colleague is one thing that is important when people leave companies.
We have expert knowledge, we have experience, and this is very hard to preserve. So just imagine a virtual colleague that follows the people in the field, that learns from them, and that keeps the knowledge in the company once this person leaves, so that the company knowledge is retained. That’s one project. One project that Fraunhofer is doing in the field of AI. to support industry and also smaller medium -sized industries. Trustworthiness, that’s very important. We all know that AI is not perfect, and probably some of you have already tweaked the AI maybe a little bit on purpose to see, okay, how far can I go? With that, AI basically is stupid. It has a lot of knowledge, but it has no understanding of that.
Trustworthy AI means we observe, for example, the field of use for a specific AI model and track whether we are leaving this field. We have an uncertainty wrapper that gives you, with each response of the AI, an uncertainty value that says, okay, how trustworthy is this particular response? And that’s very important when we want to apply AI also in safety -relevant environments, for medical. for medical diagnostics, for image recognition, and also in traffic. That’s very important then to know how safe is the assumption of the AI here. Moderated AI training is very important when we want to work together. For example, a robot that can grab something that every human can do, grab into a box and take something out.
For a robot, that’s a very big challenge. We can train robots to do that, but it’s much more effective when we can do a federated training where everybody can contribute data, and in the end we receive one model, but one model where you cannot derive any sensitive data back because nobody wants to share their sequence. So that’s where data stays. That’s where data stays. That’s what data spaces are meant for. We share data based on rules. We can provide data just for training of AI. Everybody will see the raw data for that. We just use it for training of AI. And here, Fraunhofer is researching both government policies and efficient implementations of cross -company data spaces.
So that’s my pitch, my motivation. I hope I was able to give you some ideas, some insights in what we can do with AI besides of open AI. I think Sam Altman is not here, so I can’t say that. They’re doing great things, but there are also great things that can be done in addition to that. Thank you.
Thank you very much, Thomas. This was just an appetizer. As you know, we have a lot of time. We have very limited time, but anybody who’s interested can visit our stand, where we’ve also demonstrated some of the use cases in AI and agriculture, and also AI and medicine, and AI in manufacturing. You’re welcome to visit us at the German Pavilion. I would now like to invite the CEO of Center for Development of Telematics to share a few thoughts on where India and Germany can work together. Thank you so much.
Thank you so much, Anandi. Thank you, Anandi. Quite pervasive, it is being applied to almost all the sectors. And where it is not applied, it will be applied sooner or later. because it underpins the competitiveness, productivity, and societal resilience. And for India and Germany together, Germany being the precision engineering expert, you know, for years, and India bringing the scale, I think there is a very good way that India and Germany work together and align how the AI is taken forward in a very responsible, ethical way, so that not only it helps us economic progress, but also meets the social good challenges. As we know, India’s AI system is quite vibrant, ranking third globally for its competitiveness and developer activities.
Our AI mission is already in progress. We are investing more than $2 billion. We have given to all the startups 38 ,000 GPUs, and this will be further increased. It could bring $1 .7 trillion value to India’s economy by 2035. And the tech sector particularly is productive to contribute $280 billion revenue this year itself. Germany’s AI market is also rapidly expanding, driven by strong industrial integration, particularly in manufacturing and substantial public -private investment. Germany’s AI market is expected to be nearly 30 billion euros by 2030. I wrote that there are more than 600 startups in Germany, and we also have a lot of startups. I think together, how the startup ecosystem could work together for the benefit of not only both the countries for bringing global good to this sector.
So there will be a fair win -and -win situation for India and Germany, partnership between two great countries given our diplomatic relations, given the, as I said, the precision in engineering which Germany is famous, and the scale what we bring in India. I think there are many areas where we can contribute, but I thought I will focus on two, three. One, I think it was mentioned in smart manufacturing. Germany’s leadership in industry 4 .0 and India’s expanding manufacturing. I think this government has invested heavily on the manufacturing under various schemes like PLI and DLI schemes, and it is going to go forward. And therefore, how do we use AI in smart manufacturing? We would like to work with Germany in terms of developing smart manufacturing standards, cross -border industrial data flows and safeguards, enable energy efficiency.
And since we arrived late in this manufacturing space, a lot of our manufacturing plants have actually started using AI. In fact, recently there was a science evenistic wherein the Tata’s, how they are going to bring down the cost. using AI in manufacturing. The second point I think Anandi brought up for agriculture, I think Parnasar is already working in India for agriculture, and agriculture being one of the key partners for economy, the service sector and the economy, agriculture sector, I think it also makes us a good partner for how do we improve the productivity, the yield, the income for farmers, the efficiency gain, productivity gain, and that will be another area we can work together. I would also like to say that, you know, given, especially given the economic progress we are making, and we see a lot of challenges coming from the cyber security area.
We get millions of attacks in our country, millions of attacks. and I’m not sure what is happening in that part. So, therefore, AI in cybersecurity is very, very important, which we use to some extent and we would like to learn from Germany, whatever, and we would like to share these, what we are doing, how we are using AI to, you know, come to a pattern because the kind of data flow, for example, in India, we receive data rate at 10 terabyte per second. 10 terabyte per second is the data rate. And how do you process this data in real time and be able to tell that, that how and where the attacks are happening? As far as the CDOT is concerned, we work in various areas.
I don’t know, some of you may be aware. I would like to place it on record that India made its own 4G and 5G, and it was launched by Honorable Prime Minister in September 25. And this was a journey we took alone. For 6G, we would like this journey to be together with the world because we were actually not. We never developed 1G, 2G, 3G. Suddenly, our Honorable Prime Minister said, you know, 4G and 5G, and we did in two, three years, and it is launched today. Thank you. close to 170 ,000 base stations are radiating, generating a data rate of 5 petabyte. And we’ll go further. So 5G, 6G is one area. The second area is quantum because as all we know, many of the leading economies have put a sunset date to the current level of encryptions which will be broken because of the quantum computers.
And therefore, there is a need to work on quantum together. We, particularly in CBOX, we work only in quantum communication. In fact, I am the chair of the National Quantum Communication Hub along with IIT Madras. We fund a lot of research into quantum to start up. So this could be another area where we could work together. The third thing is I’m not aware what kind of frauds happen in Germany or happen or don’t happen. In India, there were, you know, there were a series of frauds, cyber fraud calls or this type of calls. So we developed the system at India scale and are successful today in blockage. For example, I’m just giving example of we used to get 1 million regular international calls, and we were getting 15 million spoofed calls, 15 million spoofed calls per day.
And at my gateway, in 5 milliseconds, I have to decide whether this call is a real call or spoof call. Today we have deployed the system in India, which in 5 milliseconds tells you this is a spoof call, drop it, and this is a real call, send it. So at India scale. We have developed a platform called SanchalSathi .gov .in. I don’t know how many of you are aware. This is, again, a digital intelligence platform. One side is exposed to the customers, the citizens, and in back it is connected to all kinds of stakeholders. What used to happen is, as Telecom, I have declared one, let me first check the time. So, yeah, I think it’s happening.
So one is that, you know, in telecom I have said this number is turbulent. Bank doesn’t know. Police doesn’t know. So what we did, we integrated all of the stakeholders in one single platform, which we called digital intelligence platform. Once I declared this number to be a turbulent number, for any reason, it is flagged in bank, police, everywhere, so that everybody takes care. Today, when the actual financial transition happened between A to B, there is a module which we have developed called FRI, financial risk indicator. Before the money is transferred, the bank digs through the database and says, this money is going to be, is it safe? So my system tells me, no, it is not safe.
This guy is not a safe guy. So the transition is stopped. So we have done the fraud management at scale. We would like to work with Germany to further enhance it and how to use AI into this. And going forward, I would only say my time is over. AI should not widen inequalities. It should strengthen inclusion. Productivity and resilience. Let us ensure AI becomes a pillar of sustainable economic growth and social good. As Anandi said, we already have an MOU with Fraunhofer, which we are working together. I would be very happy that, you know, if this partnership goes, must go on. And we together are going forward, given the geopolitical changes happening, we’re going forward together.
We will go and develop the technologies together for the wider social good. Thank you so much.
Thank you, Dr. Upadhyay. I can only underscore that it has been an absolute privilege working with you. And it is not something where German technology is being given to CDOT, but we are actually co -creating. There are India -specific challenges for which teams from CDOT, who are extremely qualified and they have amazing infrastructure, I would really appreciate at some point, Dr. Upadhyay, some of our German delegates, maybe not this time, but next time, come and see the kind of work you’re doing. Because I think that brings a different level of understanding inside and trust. Without wasting any time, I’m sure all of you are waiting to listen to Professor Dr. Sinemus. We had her yesterday, and I must tell you that it’s really amazing to see, first of all, a woman as a head of innovation and digitalization in Germany.
to get the leg between research and then bringing it really to society and to economy growth. And at the end of the day, I’m coming out of the biotechnology area, and I think we really have lost a lot of chances in coming from the research really bringing to economic growth. And I think AI has the opportunity, and as well quantum, as you mentioned, to do this. And, Your Excellencies and gentlemen, ladies, it is a great honor for me to deliver a special address. I never have given a special address, and try to do this in the German National Technology Forum. I’m coming out of a region where we are working together. struggle together with Fraunhofer.
Even in my hometown, we have two, and one of this is the Fraunhofer Cyber Security, where we have the digital hub of cybersecurity as well. So we built a startup ecosystem in the area on the interface of cybersecurity and digital and AI. So many of this could be an anchor point afterwards. As Minister for Digitalization and Innovation, I very much welcome the opportunity to discuss how we can turn AI into concrete public value in manufacturing, in agriculture, in healthcare, and through trustworthy AI, as Thomas Kuhn has pointed it out. The AI Summit, all is about AI and we see how dynamic the process is going on. AI is already transforming how we produce, how we heal, how we grow food, and how we govern.
And the key question therefore is not if AI will shape our society. The question is how. And whether economic development and social growth move forward together rather than separate directions. And looking at the dynamic AI is going forward, we really have to be careful that we balance these economic development and the social part. And I think this is something which has to do with trust or trust as well. In my region developed a clear strategy. And our digital strategy and AI strategy are based. On a simple but a helpful demanding principle. That is. technology must serve people, not the other way around. Our AI -made Innocent Agenda combines innovation with responsibility. And we want AI that strengthens competitiveness, but also social coherence, fairness, and trust.
And this is the way we invest not only in research and infrastructure, but also in practical projects that deliver tangible benefits for citizens and business. So we have a funding program. It’s over $60 billion. And we invest in people who have a vision in how AI can benefit humans. So we invest in 170 startups till now, and I want to give you some examples so that you get an idea. And I start with healthcare, because healthcare is one of the most promising fields for social good. We support innovative projects that would be very difficult to realize without our public backing. And we fund, for instance, one project, it’s called DEMO, and DEMO, it’s not DEMO, it’s DEMO, and it’s a small group, three people who are working on PhD at the Technical University of Darmstadt by Alma Mater, and I was happy to give them 1 .8 million euro, because they are developing a load -bearing roboting wheelchair.
A session to a walking robot. that can safely and autonomously overcome barriers such as stairs or uneven ground using advanced robotics and AI. This project will help for the people with mobility impairments. That is no science fiction. It is a difference between dependence and independence participation on everyday life. And I think that is really a good example of how we invest our funds. Another example is RISCA, Risk Certification in Cardiology Using AI. The goal is to build a clinic decision support system that analyzes patterns in ECG recordings with AI and detects cardiovascular diseases at an early stage. so you can go through prevention. That are two of a lot of AI and healthcare startups we are giving our funds in.
And a second field I would like to highlight is agriculture. Even for India, agriculture is a very important area and a rich and crucial pillar of economic development and food security. Again, we support a project and there is only one of a lot on the early detection of plant diseases. Using automated analysis of satellite data, so we even connected with the space, AI models can recognize signs of plant stress and disease at every early stage of growth circles. So, you can really reduce pesticides and analyze before. So, you can really reduce pesticides and analyze before. So, you can really reduce pesticides and analyze before. AI can, at the end of the day, make agriculture more sustainable and more productive at the same time, and I think this is something where we have to go through.
Another example in our strategic agenda in the future of AI is that we set up an AI innovation lab at Hessian AI, co -funded by my ministry, and we provide a high -performance computing environment specifically for AI applications, along with advisory services for science, business, and public administration. So this is a particular focus in the startup and SME. We want to enable smaller players to develop innovation AI solutions, and we want to be, at the end of the day, in a position that trustworthy AI is not a slogan but a practical challenge. And this is why we established an AI quality and testing hub in Hess, a public -private company that develops methods and tools to test.
AI systems. So at the end of the day, the aim is to translate the idea of trustworthy AI into testable criteria and practical producers that business can use real deployments. So you can say that is trust for AI and we give it as trustworthy criteria. To the end of the day, I think, looking on the discussions we have had in the last days, that the inner German dimension is particularly important. India has shown with initiatives like other UPI and IndiaStack how digital public infrastructure can scale and enable innovation at massive levels. Thank you. Germany can bring expertise in regulation, not too much, data protection. and quality assurance. Together we can build bridges between scale and safeguard, between innovation and rights, between economy, development, and social good.
Because we are thinking grounded on the same values, democratic values. And let me close with a conviction that guides our work. AI will not automatically lead to better outcomes. It depends on the choices we make, what we fund, how we regulate, which ecosystems we build, and whom we include. So let us proceed on our democratic values and go forward with a union, German collaboration in AI. Thank you.
Thank you so much, Dr. Sinemus. It was really a thought -provoking and a very concrete talk on what we can do with AI. There is the scope for the German collaboration. Without much ado, let me take forward what we all on the topics that you have mentioned by inviting the four CEOs to please come on stage. Can I request Atatürk, Sinemus, Anshuman and Prashant to kindly come up? We have a few minutes to really get into the topics. And I would like to just start by very quickly saying that In the last few days, I’ve attended quite a few of the sessions here, and there are so many nomenclatures for AI. It’s been defined as a transformational technology that comes once in several years.
People give examples of the steam engine and of the Internet revelation and Industry 4 .0, which dramatically changed the way we live and work. We are now riding an AI transformation, but complete with anxiety, with speculations, with admiration, but also with excitement. So let me take the audience through to what makes a CEO stay up at night. And let me start with Pratatvi. Vosh has been a pioneer in India. You know, you came here a century ago, and you’ve stayed on, resilient to the Indian market, which has not always been very easy. And you’ve been a pioneer in India. You cover a lot of base, signing across manufacturing, software, medical, and many more sectors. How does the vision board of Bosch lead in terms of the AI development and deployment?
Maybe the first question not keeps me awake in the night. The fact that you are 100 -year -old and, you know, if you are 100 -year -old, the effort that you make changes also higher. So that keeps me awake at night. As Bosch, I think we are working on two sides of it. So one side, you know, if you see we are deploying AI into the market. You know, if you see autonomous driving, AI cockpit, or AI in healthcare. So these are our products which go into the market. So fundamentally, we are, you know, navigating. We are navigating a new business model, new behavior of consumer, and developing new products for that. on the other side now we are using AI to disrupt long held beliefs of how we build software how we do engineering so this is my say while we are huge excitement to position our AI centric products in the market I would be dishonest if I say we don’t have anxiety how to get over the paradigm shift of engineering and software development using AI so these are the two edges at which we are operating now thank you so much
Sindhu let me come to you you are the world’s leader in enterprise application software one time I remember you telling me that 97 % of companies worldwide use SAP it is already embedded in your workflows even before people are talking and wondering how to go about it but how do you see you have a responsibility because you have a captive client base How do you see these innovative technologies being rolled out to companies while they still have anxieties about their business? And how do you go about assuaging those fears and building in a model that works on layers that are already set and, like he said, very difficult to move right now in retrospect?
Well, first of all, Anandi, it’s great to be here. It’s an absolute honor to be amongst this very distinguished panelist and you and all the previous speakers who have been saved. Yeah, I think, like Zazadeh said, we live in times which are changing, particularly in technology, right? I mean, literally every day you have to open up and see what is the new LLM that’s out there, right? So that’s the way it’s been and which we are now in the open. So in that sense, this is… The assurance, when we say, when we are talking about the world’s largest enterprise application, the failure from SAP, it’s a lot of… The cost that our customers place on us, many of them sitting right here at those tables, are too hard.
And so then when we say 87 % of the world’s business partners, such as an SAP system, it’s a matter of lack of responsibility and a lack of trust, which we continue to take forward, given the 1226 industries and across 12 portfolios that we have, right? But at the same time, given the pace at which technology is changing, our responsibility also from an AI perspective is embedding AI across the core of our business processes. So if you take a process like a lead to cash or a total risk force management or a design to upgrade, strategic sourcing or procurement, this is where we show our customers that we’re not helping you to make your best, we’re doing the most compliant and the most ethical and the most responsible manner, right?
And so I want to make me say, to make sure and protect the choice. Customers also have the choice to run in a secure manner, in a compliant manner, in an ethical manner, and in a responsible manner, and which means also translating from several of our speakers. You need to be able to explain to this decision that you are making or asking the user to make, giving that explainability, the transparency, the fairness of that decision -making, the auditability, right? And that becomes even more important because today when we are talking about urgentity, we are talking about autonomous workflows, we are talking about decision -making. Yes, the human is in the room, but part of the workflow is completely autonomous, right?
And so that’s a lot of the work that’s keeping us busy at night and mornings, I would say, and really making sure that when we say customers can run at their best, in a compliant, in a secure manner, in a responsible manner? How do we make sure that they are to go through the research?
Tim, what is the holdup and failure for customers to run in a safe manner? Whether you’re up at night or not, you’re certainly on your toes. The only person who’s been up at night for several months now, and I can personally vouch for this, Krishan, Continental is now a Romeo. It has not only changed in name, but also in terms of its core processes, some of its business sectors. So innovation is not only in the name, it’s also what you do. What is a Romeo now up to and what is happening?
First of all, thank you, Amandeep and Neera. I’m sure all the panelists agree. I think in India, we have moved from support apps to center of excellence. I think in Aumavio, we’re focusing on two things. One is certainty of allocation for our customers, for the audience, innovation for the stakeholders. And the second was customer -centric innovation. Just to let you know, one year I think we’ve been able to gain efficiency improvement in R &D in excess of 20%. How did we do this? Actually, there are cool things. I think we do with one strength and core technology, be it in the plants in terms of how do we enhance the quality using air, be it in terms of, you know, any product that’s done in products, there’s always a lot of false calls, which requires a lot of efforts for you to redo that.
So that’s where the implementation of AI in terms of enhancing the quality. And the second is certainly government development, I think using the GitHub, what I was talking about, where this work is coding and also for developing different test scenarios and a test situation. So these are the things that we are really focusing on. One, as I said, how do we really enhance the velocity in engineering and R &D? We’re using this automated technology, and also we do something called ReckNet. In automotive, the biggest challenge today with so much of complexity of technology is coming with the requirements, because always you leave one or two, which is very, very important. Like how do you really enhance this in terms of the ReckNet?
And second is the attention of the agent, which is used in the enterprise in the simple function like finance, controlling, or market analytics. So this helps us in giving a very good summary for the leadership to take decision based on the data. And third is certainly on the product innovations. And we have a place here in the Jammu and Pakistan, where we have a display, which is dollar code here in India, one of the autonomous mobility, which is called Enhanced Light Fusion. We have been able to, with the use of AI, enhance only the… the camera vision for easier detection and the second is something called e -travel companion where you seamlessly interact with the vehicle and the third is like our driver will seamlessly access with the car.
These are the three which was developed innovations which is displayed in the German stand as well. So in summary, I think we continue to drive because of what we call our increase to seamless integration into the operation.
Thank you so much Prashant. Let me go to Mercedes. Mercedes -Benz is a North Star in innovation. I mean you’ve been around several decades. It’s also been a very competitive environment in manufacturing which a lot of people say that Germany has lost its edge. But it’s an old school field. So how do you bring in AI into the second kind of operations, which is reflected in your standardized operations and very, I said, old school procedures? Is it challenging to get AI into it now?
Good morning, everybody, and thank you for having me here. Yes, Mercedes has been one of the most important companies and we plan to remain virtual. We’ve been doing this for many, many years. AI as a technology is not a challenge. We don’t see any technology as a challenge. And we have been using it. I think if I’m not wrong, Mercedes -Benz was the first automotive company to bring AI into the cars in 2019 itself. So if you’re driving cars from 2019, you must have experience AI. It’s a challenge. It’s a challenge. Today our focus is monthly forward works. For example, we want to offer technology to our customers. That means you drive the car and you experience a lot in the car, how the car behaves, whether you direct the car, etc.
And whether the parts are behaving in the seat. And the second part that we also are planning to do is tighten our operations using AI. We want to bring operational efficiency using AI. And many things have been mentioned. One such thing we are trying to implement. Now it can bring benefit to all of us. So, we have technology. It’s very exciting. Because sometimes you feel that it’s making so many things evident. I think that’s now and then has been the same thing and same impact so we are looking further to going ahead with using it.
And ,of course, NBRDI in Bangalore is playing a very big role into this process?
Yes. The 2019 application was largely developed NBRDI that was sent to by our AI experts back then.
It was so good we have another 5 minutes and I’d like to use this for a rapid round talking about India so we’ve always heard about the big combination of manufacturing excellence in Germany, engineering, scale and speed in India Let me ask Dr. Tri, is this heightened German interest in India, is it offensive or defensive?
I would say it was long overdue. I think given the capabilities of the two countries and the common interests that we share, for reasons unknown, we play it very low, while many others play it significantly high. So I think now we can collaborate on many dimensions, not just the precision engineering of Germany, which more than that. Because the pragmatic approach Germany brings in to solve problems from the first principles is what is required now. Because we are in a phase where we have to cut the norms of AI and to marry the substance of AI. So for this, I think we, I mean, we all know in this room… Thank you very much. So it might be a moment generally, you know, how we can remain together.
So it’s an opportunity that I see. Thank you.
That’s very diplomatically put. Sindhu, I come to you. You have just inaugurated a huge campus and your ambitions of taking more than 14 ,000, 15 ,000 people. The controversial question that I’d like to put to you is they say that the cost of the charge is four is to one. That is, for one German, you can get four Indians in it. Not only one. Not even one. Srinu, I will write answers. No, of course. The value of the charge is just the opposite, right? So we are looking at a demographic dividend in India, but there is a huge aspect of inclusion, productivity, skilling, reskilling. How does that play into your vision for India?
You started off by talking about the campus, right? And we were talking only about the campus. What’s the charge point of view? I don’t know. I don’t know. I don’t know. I don’t know. I don’t know. campus, right? So that, and I think we just had Chancellor Max coming to campus as well, and looking at India as a constant detachment, it’s clear of my hands. I don’t see it in any of these conversations. Let’s not be, today when we look at, and we mentioned GitHub for pilot, and if I just look at CloudCode, the latest SOPAs that we have, the speed at which you can do things is of course changing, but what it means is experts can focus on high -value SOPAs, right, and high -value domain use cases, and this is the beauty of the pace at which innovation is being, and this is also why companies like SAP or anybody else here in SOPA are invested in India, because they want to build a system of countries and research question is always the right.
So we really want to bring this and the pace at which our people here in India are able to take forward, understand the context, drive forward things, drive that leadership, creation of IC, it’s the change we’re talking about.
Thank you, Sindhu. I have actually two more provocative questions for my other two, but due to the paucity of time, we’ll have to take a break. Because we still have Morley talking about his study, but I think the fact is clear that the opportunity you have in India has been an opportunity like never before. It’s an influential point. It is an opportunity for cooperation and working together in a very, like Mr. said, with shared values, a very clear agenda. And I thank you for being here and for sharing your thoughts. And can we really quickly come and talk about your study, please? because we just have two minutes and we have to close so quickly come Thank you so much.
Anandi Iyer
Speech speed
129 words per minute
Speech length
1839 words
Speech time
852 seconds
MOU and vision for long‑term collaboration
Explanation
Anandi highlights that the activities outlined in the Indo‑German AI MOU have already begun and expresses hope that the dialogue will evolve into a sustained partnership for innovation and value creation between the two countries.
Evidence
“And we are delighted to note that many of the activities that were outlined in the MOU have already kick -started.” [1]. “And we hope that some of the thoughts that we are leaving behind with you today will resonate and will actually trigger a long -term dialogue and engagement so that we can take the Indo -German innovation collaboration to greater heights.” [7].
Major discussion point
Indo‑German AI Partnership & Strategic Framework
Topics
The enabling environment for digital development | Artificial intelligence | Data governance
Demonstrations and co‑creation at the German Pavilion
Explanation
She invites participants to visit the German Pavilion where use‑case demonstrations in AI for agriculture, medicine and manufacturing are showcased, emphasizing that the collaboration is based on co‑creation rather than simple technology transfer.
Evidence
“We have an absolutely amazing network of partners in India, including organizations like CDOT, but also with Indian industry, which is really gearing up for innovation.” [114]. “And we have an absolutely amazing network of partners in India, including organizations like CDOT, but also with Indian industry, which is really gearing up for innovation.” [76]. “And it is not something where German technology is being given to CDOT, but we are actually co -creating.” [103]. “You’re welcome to visit us at the German Pavilion.” [101].
Major discussion point
Innovation Ecosystem & Funding
Topics
The enabling environment for digital development | Artificial intelligence
Georg Enzweiler
Speech speed
100 words per minute
Speech length
470 words
Speech time
280 seconds
IA Pact, AI lighthouse projects and inclusive growth agenda
Explanation
Georg outlines the India‑Germany IA Pact, the investment in AI lighthouse projects for climate and environmental protection, and stresses the need to ensure that AI‑driven growth benefits all sections of society.
Evidence
“Germany is investing in so -called AI lighthouses, which foster AI innovations for climate and environmental protection.” [16]. “Ministers Vaishnaw and Wildberger launched the India -Germany IA Pact, a new partnership focused on implementation -driven collaboration across government, industry, research, skill development, and innovation.” [17]. “How can we ensure that this growth is inclusive?” [18]. “Since 2020, Germany has funded over 60 projects leveraging AI for sustainability.” [22]. “It will include aspects as AI for industry and manufacturing, talent, skills, and mobility, joint research, innovation, and infrastructure, and overall AI for social good.” [23].
Major discussion point
Indo‑German AI Partnership & Strategic Framework
Topics
Artificial intelligence | The enabling environment for digital development | Social and economic development
Dr. Rajkumar Upadhyay
Speech speed
148 words per minute
Speech length
1358 words
Speech time
547 seconds
Joint standards and cross‑border data flows for smart manufacturing
Explanation
Dr. Rajkumar stresses the need to develop common standards and secure cross‑border industrial data flows to enable AI‑driven smart manufacturing and energy efficiency across India and Germany.
Evidence
“We would like to work with Germany in terms of developing smart manufacturing standards, cross -border industrial data flows and safeguards, enable energy efficiency.” [31]. “And therefore, how do we use AI in smart manufacturing?” [33]. “We share data based on rules.” [36]. “And here, Fraunhofer is researching both government policies and efficient implementations of cross -company data spaces.” [37].
Major discussion point
Indo‑German AI Partnership & Strategic Framework
Topics
Data governance | Artificial intelligence | The enabling environment for digital development
Sector‑specific AI opportunities (smart manufacturing, agriculture, etc.)
Explanation
He highlights AI’s potential to boost productivity and resilience in manufacturing, agriculture and broader social good, positioning AI as a pillar of sustainable economic growth.
Evidence
“Productivity and resilience.” [4]. “As Anandi said, we already have an MOU with Fraunhofer, which we are working together.” [5]. “We will go and develop the technologies together for the wider social good.” [6]. “Let us ensure AI becomes a pillar of sustainable economic growth and social good.” [19]. “Germany’s AI market is also rapidly expanding, driven by strong industrial integration, particularly in manufacturing and substantial public -private investment.” [78]. “focus on workplace changing, which is, of course, one of the biggest topics we have today, but also manufacturing, agriculture, and health.” [71].
Major discussion point
Sector‑Specific AI Opportunities
Topics
Artificial intelligence | Social and economic development | Environmental impacts
Prof. Dr. Kristina Sinemus
Speech speed
116 words per minute
Speech length
1131 words
Speech time
584 seconds
Democratic values, joint AI quality hub and shared regulatory principles
Explanation
Prof. Sinemus calls for AI collaboration grounded in shared democratic values and announces the establishment of an AI quality and testing hub to develop methods and tools for trustworthy AI.
Evidence
“So let us proceed on our democratic values and go forward with a union, German collaboration in AI.” [41]. “Because we are thinking grounded on the same values, democratic values.” [42]. “And this is why we established an AI quality and testing hub in Hess, a public -private company that develops methods and tools to test.” [43].
Major discussion point
Indo‑German AI Partnership & Strategic Framework
Topics
Human rights and the ethical dimensions of the information society | Artificial intelligence | Data governance
Funding programme and AI innovation labs
Explanation
She outlines a €60 bn German AI funding programme, the creation of AI innovation labs such as the one at Hessian AI, and support for AI‑driven startups and research projects.
Evidence
“So we have a funding program.” [10]. “I’m coming out of a region where we are working together.” [12]. “We set up an AI innovation lab at Hessian AI, co‑funded by my ministry, and we provide a high‑performance computing environment specifically for AI applications, along with advisory services for science, business, and public administration.” [21]. “It’s over $60 billion.” [89]. “That are two of a lot of AI and healthcare startups we are giving our funds in.” [90].
Major discussion point
Innovation Ecosystem & Funding
Topics
Financial mechanisms | The enabling environment for digital development | Artificial intelligence
AI quality testing hub and certification of trustworthy systems
Explanation
She emphasizes that the AI quality and testing hub will translate trustworthy AI principles into testable criteria and practical tools for businesses.
Evidence
“And this is why we established an AI quality and testing hub in Hess, a public -private company that develops methods and tools to test.” [43]. “So you can say that is trust for AI and we give it as trustworthy criteria.” [46]. “So at the end of the day, the aim is to translate the idea of trustworthy AI into testable criteria and practical producers that business can use real deployments.” [61].
Major discussion point
Trustworthy & Responsible AI, Data Infrastructure
Topics
Artificial intelligence | Human rights and the ethical dimensions of the information society
Dr. Thomas Kuhn
Speech speed
122 words per minute
Speech length
1096 words
Speech time
537 seconds
Trustworthiness through uncertainty wrappers and knowledge preservation
Explanation
Dr. Kuhn describes an uncertainty wrapper that attaches a confidence score to each AI response and stresses the importance of preserving organisational knowledge for sustainable growth.
Evidence
“We have an uncertainty wrapper that gives you, with each response of the AI, an uncertainty value that says, okay, how trustworthy is this particular response?” [50]. “Trustworthiness, that’s very important.” [51]. “Trustworthy AI means we observe, for example, the field of use for a specific AI model and track whether we are leaving this field.” [54]. “So just imagine a virtual colleague that follows the people in the field, that learns from them, and that keeps the knowledge in the company once this person leaves, so that the company knowledge is retained.” [57]. “For sustainable growth, it’s also very important to preserve knowledge that is available in the company, especially if people retire.” [59].
Major discussion point
Trustworthy & Responsible AI, Data Infrastructure
Topics
Artificial intelligence | Human rights and the ethical dimensions of the information society | Data governance
Fraunhofer alliance of 30 institutes, SME support and industrial AI services
Explanation
He notes that more than 30 Fraunhofer institutes collaborate on AI, providing best‑practice guidance, research and support for SMEs and industrial AI deployments.
Evidence
“Alliances, so more than 30 institutes that team up with AI, in creating AI strategies, each institute with its own field of competence, but all of us support best practices, studies, expert opinions, and we support all companies in creating new solutions.” [93]. “So what is Fraunhofer contributing?” [95]. “That’s something that Fraunhofer is researching on.” [97]. “And I’ve chosen here a few highlights on what we can do as Fraunhofer.” [100].
Major discussion point
Innovation Ecosystem & Funding
Topics
Financial mechanisms | The enabling environment for digital development | Artificial intelligence
Sindhu Gangadharan
Speech speed
160 words per minute
Speech length
672 words
Speech time
250 seconds
Explainability, transparency and compliance for enterprise AI
Explanation
Sindhu stresses that AI decisions must be explainable, transparent, fair and auditable to meet ethical and regulatory requirements.
Evidence
“You need to be able to explain to this decision that you are making or asking the user to make, giving that explainability, the transparency, the fairness of that decision -making, the auditability, right?” [60].
Major discussion point
Trustworthy & Responsible AI, Data Infrastructure
Topics
Artificial intelligence | Human rights and the ethical dimensions of the information society
Embedding AI across core SAP processes while ensuring ethical outcomes
Explanation
She points out that, given rapid technological change, SAP must embed AI throughout its core business processes while guaranteeing ethical, compliant and transparent outcomes.
Evidence
“But at the same time, given the pace at which technology is changing, our responsibility also from an AI perspective is embedding AI across the core of our business processes.” [108].
Major discussion point
Corporate Implementation Challenges & Strategies
Topics
Artificial intelligence | Human rights and the ethical dimensions of the information society | The digital economy
Prashant Doreswamy
Speech speed
148 words per minute
Speech length
449 words
Speech time
180 seconds
Leveraging AI for 20 % R&D efficiency gain and product innovation
Explanation
Prashant reports that AI has already delivered more than a 20 % improvement in R&D efficiency and is being used to accelerate engineering velocity and enhance product quality.
Evidence
“Just to let you know, one year I think we’ve been able to gain efficiency improvement in R &D in excess of 20%.” [83]. “One, as I said, how do we really enhance the velocity in engineering and R &D?” [84]. “So that’s where the implementation of AI in terms of enhancing the quality.” [44].
Major discussion point
Corporate Implementation Challenges & Strategies
Topics
Artificial intelligence | The digital economy | Human rights and the ethical dimensions of the information society
Anshuman Awasthi
Speech speed
113 words per minute
Speech length
255 words
Speech time
135 seconds
AI integration in automotive products and operational efficiency
Explanation
Anshuman notes that AI was first introduced in cars by Mercedes‑Benz in 2019 and that his organization is now using AI to tighten operations and improve vehicle functionalities.
Evidence
“I think if I’m not wrong, Mercedes -Benz was the first automotive company to bring AI into the cars in 2019 itself.” [77]. “And the second part that we also are planning to do is tighten our operations using AI.” [112]. “One such thing we are trying to implement.” [14].
Major discussion point
Sector‑Specific AI Opportunities
Topics
Artificial intelligence | The digital economy | Social and economic development
Dattatri Salagame
Speech speed
139 words per minute
Speech length
341 words
Speech time
146 seconds
Navigating new AI‑centric business models and paradigm shift in engineering
Explanation
Dattatri acknowledges the anxiety around shifting engineering and software development to AI‑centric models and stresses the need to develop new business models and consumer behaviours.
Evidence
“we have anxiety how to get over the paradigm shift of engineering and software development using AI so these are the two edges at which we are operating now thank you so much” [106]. “We are navigating a new business model, new behavior of consumer, and developing new products for that.” [107].
Major discussion point
Corporate Implementation Challenges & Strategies
Topics
Artificial intelligence | The digital economy | Capacity development
AI applications in autonomous driving, healthcare and industrial products
Explanation
He highlights AI use cases ranging from autonomous driving and AI cockpits to healthcare decision‑support systems, illustrating the breadth of AI’s impact across sectors.
Evidence
“you know, if you see autonomous driving, AI cockpit, or AI in healthcare.” [80]. “The goal is to build a clinic decision support system that analyzes patterns in ECG recordings with AI and detects cardiovascular diseases at an early stage.” [82]. “And we are deploying AI into the market.” [116].
Major discussion point
Sector‑Specific AI Opportunities
Topics
Artificial intelligence | Social and economic development | Health
Agreements
Agreement points
AI must be developed and deployed responsibly with human-centered approach
Speakers
– Georg Enzweiler
– Prof. Dr. Kristina Sinemus
– Sindhu Gangadharan
Arguments
AI predicted to contribute $5-15 trillion to global GDP by 2030, requiring inclusive growth strategies
Technology must serve people, not the other way around, with AI strengthening competitiveness and social coherence
SAP embeds AI across core business processes while ensuring compliance, security, and ethical decision-making
Summary
All speakers emphasized the importance of developing AI that serves human needs while ensuring ethical, responsible, and inclusive implementation that benefits society as a whole
Topics
Artificial intelligence | Human rights and the ethical dimensions of the information society | Social and economic development
India-Germany collaboration leverages complementary strengths for mutual benefit
Speakers
– Georg Enzweiler
– Dr. Rajkumar Upadhyay
– Dattatri Salagame
– Sindhu Gangadharan
Arguments
Germany and India are natural partners with longstanding scientific partnership for creating sustainable and inclusive AI solutions
India-Germany collaboration combines German precision engineering expertise with India’s scale for responsible AI development
Indo-German collaboration represents long-overdue opportunity to combine capabilities and solve problems from first principles
India’s demographic dividend and innovation speed complement German engineering precision for mutual benefit
Summary
Speakers consistently highlighted how Germany’s precision engineering and regulatory expertise combines effectively with India’s scale, talent pool, and innovation speed to create mutually beneficial partnerships
Topics
The enabling environment for digital development | Artificial intelligence | Capacity development
AI applications in healthcare, agriculture, and manufacturing deliver concrete social and economic benefits
Speakers
– Georg Enzweiler
– Prof. Dr. Kristina Sinemus
– Dr. Thomas Kuhn
– Prashant Doreswamy
Arguments
Germany is investing in so-called AI lighthouses, which foster AI innovations for climate and environmental protection
AI in agriculture enables early detection of plant diseases through satellite data analysis, reducing pesticides while increasing productivity
AI enables virtual colleagues that preserve expert knowledge when employees leave companies
Averior achieved 20% efficiency improvement in R&D through AI implementation in quality enhancement and automated development
Summary
Multiple speakers provided concrete examples of AI delivering measurable benefits across key sectors, demonstrating practical value creation beyond theoretical potential
Topics
Artificial intelligence | Social and economic development | Environmental impacts
Secure data infrastructure and trustworthy AI systems are essential for successful implementation
Speakers
– Dr. Thomas Kuhn
– Dr. Rajkumar Upadhyay
– Sindhu Gangadharan
Arguments
Data spaces are key technology for AI training, enabling secure data sharing based on rules with up to 10,000 transactions per second
India developed AI systems for cybersecurity that process 10 terabytes per second and block millions of spoofed calls daily
SAP embeds AI across core business processes while ensuring compliance, security, and ethical decision-making
Summary
Speakers agreed on the critical importance of building secure, trustworthy AI infrastructure that can handle massive data flows while maintaining privacy and security standards
Topics
Building confidence and security in the use of ICTs | Data governance | Artificial intelligence
Similar viewpoints
Both speakers emphasized substantial government investment in AI development with focus on practical applications that deliver social benefits, particularly in healthcare and other critical sectors
Speakers
– Prof. Dr. Kristina Sinemus
– Dr. Rajkumar Upadhyay
Arguments
Germany invests over $60 billion in AI projects including healthcare robotics and early disease detection systems
India-Germany collaboration combines German precision engineering expertise with India’s scale for responsible AI development
Topics
Financial mechanisms | Artificial intelligence | Social and economic development
Industry leaders shared similar approaches to AI implementation, focusing on both product development and internal process transformation while emphasizing practical, measurable outcomes
Speakers
– Dr. Thomas Kuhn
– Dattatri Salagame
– Prashant Doreswamy
Arguments
Fraunhofer focuses on trustworthy AI, industrial AI for knowledge preservation, and specialized AI models based on company data
Bosch operates on two fronts: deploying AI products like autonomous driving and using AI to disrupt traditional engineering processes
Averior achieved 20% efficiency improvement in R&D through AI implementation in quality enhancement and automated development
Topics
Artificial intelligence | The digital economy | Social and economic development
Both speakers highlighted the evolution from traditional client-service relationships to genuine co-creation partnerships, with India moving from technology recipient to collaborative innovation partner
Speakers
– Anandi Iyer
– Dr. Rajkumar Upadhyay
Arguments
Fraunhofer has earned over 70 million euros in 10 years from Indian research contracts, demonstrating successful collaboration model
India developed indigenous 4G and 5G technology and seeks collaborative approach for 6G development with global partners
Topics
The enabling environment for digital development | Capacity development | Information and communication technologies for development
Unexpected consensus
Knowledge preservation through AI as critical business need
Speakers
– Dr. Thomas Kuhn
– Prof. Dr. Kristina Sinemus
Arguments
AI enables virtual colleagues that preserve expert knowledge when employees leave companies
Germany invests over $60 billion in AI projects including healthcare robotics and early disease detection systems
Explanation
The consensus on using AI specifically for knowledge preservation in organizations was unexpected, as this represents a more nuanced application beyond typical productivity or automation discussions. Both academic and policy perspectives aligned on this specific use case
Topics
Artificial intelligence | Capacity development | Social and economic development
Federated learning and data sovereignty as practical solutions
Speakers
– Dr. Thomas Kuhn
– Dr. Rajkumar Upadhyay
Arguments
Federated AI training allows collaborative model development without sharing sensitive raw data
Digital intelligence platform integrates all stakeholders to flag fraudulent activities across banking, police, and telecom systems
Explanation
The alignment between technical research perspectives and practical government implementation on federated approaches was unexpected, showing convergence between theoretical best practices and real-world deployment strategies
Topics
Data governance | Building confidence and security in the use of ICTs | Artificial intelligence
Early automotive AI leadership from emerging markets
Speakers
– Anshuman Awasthi
– Anandi Iyer
Arguments
Mercedes-Benz was first automotive company to bring AI into cars in 2019, focusing on customer experience and operational efficiency
Fraunhofer has earned over 70 million euros in 10 years from Indian research contracts, demonstrating successful collaboration model
Explanation
The revelation that cutting-edge automotive AI was developed in India (NBRDI Bangalore) rather than traditional automotive centers was unexpected, demonstrating the shift in global innovation centers
Topics
Artificial intelligence | The enabling environment for digital development | Social and economic development
Overall assessment
Summary
Strong consensus emerged around human-centered AI development, the strategic value of India-Germany collaboration leveraging complementary strengths, practical AI applications delivering measurable benefits across sectors, and the critical importance of secure, trustworthy AI infrastructure
Consensus level
High level of consensus with remarkable alignment between government, academic, and industry perspectives. The implications suggest a mature, implementation-ready partnership framework that moves beyond theoretical discussions to practical collaboration models with concrete outcomes and shared values around responsible AI development
Differences
Different viewpoints
Approach to AI development – human-centered vs. technology-driven
Speakers
– Prof. Dr. Kristina Sinemus
– Dr. Thomas Kuhn
Arguments
Technology must serve people, not the other way around, with AI strengthening competitiveness and social coherence
Fraunhofer focuses on trustworthy AI, industrial AI for knowledge preservation, and specialized AI models based on company data
Summary
Prof. Sinemus emphasizes a human-centered philosophy where technology serves people, while Dr. Kuhn focuses more on technical solutions and industrial applications without explicitly addressing the human-centered approach
Topics
Artificial intelligence | Human rights and the ethical dimensions of the information society
AI development strategy – collaborative vs. independent approach
Speakers
– Dr. Rajkumar Upadhyay
– Dattatri Salagame
Arguments
India developed indigenous 4G and 5G technology and seeks collaborative approach for 6G development with global partners
Indo-German collaboration represents long-overdue opportunity to combine capabilities and solve problems from first principles
Summary
Dr. Upadhyay describes India’s shift from independent development (4G/5G) to collaborative approach for future technologies, while Salagame suggests the collaboration was always needed and overdue
Topics
The enabling environment for digital development | Capacity development
Unexpected differences
Timeline and urgency of collaboration
Speakers
– Dattatri Salagame
– Georg Enzweiler
Arguments
Indo-German collaboration represents long-overdue opportunity to combine capabilities and solve problems from first principles
India-Germany AI Pact launched focusing on implementation-driven collaboration across government, industry, research, and innovation
Explanation
While Enzweiler presents the AI Pact as a timely new initiative, Salagame characterizes similar collaboration as ‘long overdue’, suggesting different perspectives on the timing and urgency of bilateral cooperation
Topics
The enabling environment for digital development | Artificial intelligence
Overall assessment
Summary
The discussion showed remarkably high consensus among speakers with minimal disagreements. Most differences were subtle variations in emphasis rather than fundamental disagreements. Speakers generally agreed on the importance of AI for economic growth, the value of Indo-German collaboration, and the need for responsible AI development.
Disagreement level
Very low level of disagreement. The speakers represented a cohesive view of AI development priorities and Indo-German cooperation. The few disagreements were mainly about approach and emphasis rather than fundamental goals, suggesting strong alignment on core objectives and strategies for AI development and bilateral collaboration.
Partial agreements
Partial agreements
Both agree on the importance of AI for economic growth and social benefit, but Enzweiler focuses on ensuring inclusive growth globally while Sinemus emphasizes specific German investments and projects
Speakers
– Georg Enzweiler
– Prof. Dr. Kristina Sinemus
Arguments
AI predicted to contribute $5-15 trillion to global GDP by 2030, requiring inclusive growth strategies
Germany invests over $60 billion in AI projects including healthcare robotics and early disease detection systems
Topics
Artificial intelligence | Social and economic development | Financial mechanisms
Both recognize the importance of secure data sharing and integration, but Upadhyay focuses on fraud prevention applications while Kuhn emphasizes AI training infrastructure
Speakers
– Dr. Rajkumar Upadhyay
– Dr. Thomas Kuhn
Arguments
Digital intelligence platform integrates all stakeholders to flag fraudulent activities across banking, police, and telecom systems
Data spaces are key technology for AI training, enabling secure data sharing based on rules with up to 10,000 transactions per second
Topics
Data governance | Building confidence and security in the use of ICTs | Artificial intelligence
Similar viewpoints
Both speakers emphasized substantial government investment in AI development with focus on practical applications that deliver social benefits, particularly in healthcare and other critical sectors
Speakers
– Prof. Dr. Kristina Sinemus
– Dr. Rajkumar Upadhyay
Arguments
Germany invests over $60 billion in AI projects including healthcare robotics and early disease detection systems
India-Germany collaboration combines German precision engineering expertise with India’s scale for responsible AI development
Topics
Financial mechanisms | Artificial intelligence | Social and economic development
Industry leaders shared similar approaches to AI implementation, focusing on both product development and internal process transformation while emphasizing practical, measurable outcomes
Speakers
– Dr. Thomas Kuhn
– Dattatri Salagame
– Prashant Doreswamy
Arguments
Fraunhofer focuses on trustworthy AI, industrial AI for knowledge preservation, and specialized AI models based on company data
Bosch operates on two fronts: deploying AI products like autonomous driving and using AI to disrupt traditional engineering processes
Averior achieved 20% efficiency improvement in R&D through AI implementation in quality enhancement and automated development
Topics
Artificial intelligence | The digital economy | Social and economic development
Both speakers highlighted the evolution from traditional client-service relationships to genuine co-creation partnerships, with India moving from technology recipient to collaborative innovation partner
Speakers
– Anandi Iyer
– Dr. Rajkumar Upadhyay
Arguments
Fraunhofer has earned over 70 million euros in 10 years from Indian research contracts, demonstrating successful collaboration model
India developed indigenous 4G and 5G technology and seeks collaborative approach for 6G development with global partners
Topics
The enabling environment for digital development | Capacity development | Information and communication technologies for development
Takeaways
Key takeaways
Indo-German AI collaboration represents a strategic partnership combining German precision engineering with India’s scale and speed for mutual benefit
AI is positioned as a transformational technology requiring responsible development with human-centric approaches, where technology serves people rather than the other way around
The India-Germany AI Pact provides a concrete framework for implementation-driven collaboration across government, industry, research, and innovation sectors
Trustworthy AI with explainability, transparency, and auditability is essential for enterprise adoption, particularly in safety-critical applications
Data spaces and secure data sharing mechanisms are fundamental infrastructure requirements for effective AI development and deployment
Industry leaders are successfully implementing AI across manufacturing, healthcare, agriculture, and cybersecurity with measurable efficiency gains
India’s demographic dividend and AI talent pool (15% globally) complement Germany’s engineering expertise and regulatory frameworks
Both countries share democratic values that provide a foundation for ethical AI development focused on social good and inclusive growth
Resolutions and action items
Continue implementation of activities outlined in the Fraunhofer-CDOT MOU for co-creation of India-specific AI solutions
Develop joint standards for smart manufacturing combining Germany’s Industry 4.0 leadership with India’s expanding manufacturing sector
Collaborate on 6G technology development as India seeks international partnership after successfully developing indigenous 4G and 5G
Establish cross-border industrial data flows and safeguards for AI-enabled manufacturing
Share cybersecurity AI technologies and best practices given India’s experience with large-scale fraud prevention systems
Expand startup ecosystem collaboration between German and Indian AI companies for global benefit
Develop practical testing criteria and procedures for trustworthy AI through Germany’s AI quality and testing hub
Unresolved issues
Specific mechanisms for ensuring AI development remains inclusive and doesn’t widen inequalities
Detailed regulatory frameworks for cross-border data sharing while maintaining security and privacy
Concrete measures to address workforce displacement concerns as AI transforms traditional manufacturing and engineering processes
Standardization approaches for AI applications across different sectors and countries
Funding models and investment structures for sustained Indo-German AI collaboration beyond current initiatives
Integration challenges for AI implementation in legacy enterprise systems and established workflows
Scalability questions for extending successful pilot projects to nationwide or global implementations
Suggested compromises
Balanced approach between innovation speed and regulatory compliance, ensuring AI development proceeds responsibly without stifling progress
Federated learning models that enable collaborative AI development while keeping sensitive company data secure and local
Hybrid human-AI workflows that preserve human expertise and decision-making while leveraging AI capabilities for efficiency gains
Gradual AI integration strategies that allow traditional industries to adapt existing processes rather than complete overhauls
Shared investment models where both countries contribute resources and expertise rather than one-sided technology transfer
Flexible regulatory frameworks that can adapt to rapid AI technological changes while maintaining safety and ethical standards
Thought provoking comments
In Fraunhofer, we call it augmented intelligence, which means that human intelligence is still at the core of what we’re talking in terms of AI.
Speaker
Anandi Iyer
Reason
This reframing of AI as ‘augmented intelligence’ is philosophically significant as it shifts the narrative from AI replacing humans to AI enhancing human capabilities. This distinction is crucial for addressing societal anxieties about AI displacement and sets a more collaborative tone for human-AI interaction.
Impact
This comment established a foundational perspective that influenced the entire discussion, moving away from fears of AI dominance toward a more collaborative framework. It set the stage for subsequent speakers to discuss AI applications in terms of human enhancement rather than replacement.
AI should not widen inequalities. It should strengthen inclusion. Productivity and resilience. Let us ensure AI becomes a pillar of sustainable economic growth and social good.
Speaker
Dr. Rajkumar Upadhyay
Reason
This comment directly addresses one of the most critical challenges of AI deployment – the risk of exacerbating existing inequalities. It’s thought-provoking because it frames AI development as having moral imperatives beyond just technological advancement, emphasizing social responsibility.
Impact
This shifted the conversation from purely technical and economic benefits to include social justice considerations. It influenced subsequent speakers to address how their AI implementations consider inclusivity and social impact, adding depth to the business-focused discussion.
technology must serve people, not the other way around. Our AI-made Innocent Agenda combines innovation with responsibility.
Speaker
Prof. Dr. Kristina Sinemus
Reason
This principle challenges the often technology-first approach to AI development by asserting human-centric values. It’s particularly insightful as it comes from a government official, showing policy-level commitment to responsible AI development rather than just market-driven innovation.
Impact
This comment reinforced the human-centric theme and provided a policy framework that other speakers could reference. It elevated the discussion from technical capabilities to governance and ethical considerations, influencing how subsequent panelists framed their AI initiatives.
AI will not automatically lead to better outcomes. It depends on the choices we make, what we fund, how we regulate, which ecosystems we build, and whom we include.
Speaker
Prof. Dr. Kristina Sinemus
Reason
This comment is profoundly insightful because it challenges the deterministic view of technology progress. It emphasizes human agency and intentionality in shaping AI’s impact, moving beyond technological optimism to acknowledge the importance of deliberate choices in AI development and deployment.
Impact
This comment served as a crucial turning point, shifting the discussion from what AI can do to how we should deploy it. It influenced the CEO panel to discuss not just their AI implementations but also their decision-making frameworks and responsibility considerations.
I would be dishonest if I say we don’t have anxiety how to get over the paradigm shift of engineering and software development using AI
Speaker
Dattatri Salagame
Reason
This admission of vulnerability and uncertainty from a CEO is remarkably honest and thought-provoking. It acknowledges that even industry leaders are grappling with fundamental changes to established processes, making the discussion more authentic and relatable.
Impact
This honest admission changed the tone of the CEO panel from confident presentations to more nuanced discussions about challenges and uncertainties. It encouraged other CEOs to be more candid about their own struggles with AI integration, creating a more substantive dialogue.
The controversial question that I’d like to put to you is they say that the cost of the charge is four is to one. That is, for one German, you can get four Indians in it.
Speaker
Anandi Iyer
Reason
This provocative question directly challenges cost-arbitrage assumptions about Indo-German collaboration, forcing a deeper examination of value propositions beyond simple cost comparisons. It’s thought-provoking because it addresses underlying economic assumptions that often drive international partnerships.
Impact
This question forced Sindhu Gangadharan to reframe the India value proposition from cost savings to innovation capacity and speed, elevating the discussion about India’s role from a service provider to an innovation partner. It challenged stereotypical views of the relationship.
Overall assessment
These key comments fundamentally shaped the discussion by establishing three critical frameworks: human-centricity over technology-centricity, responsibility and ethics as core considerations rather than afterthoughts, and honest acknowledgment of challenges rather than purely optimistic presentations. The progression from Anandi’s ‘augmented intelligence’ concept through the policy makers’ emphasis on human-serving technology to the CEOs’ candid admissions of challenges created a sophisticated dialogue that balanced innovation enthusiasm with realistic implementation concerns. The discussion evolved from technical capabilities to governance frameworks to practical challenges, ultimately presenting a mature view of AI development that considers technological, social, economic, and ethical dimensions simultaneously. The provocative questions about cost arbitrage and the honest admissions about anxiety added authenticity that prevented the discussion from becoming merely promotional, instead fostering genuine strategic thinking about Indo-German AI collaboration.
Follow-up questions
How can we ensure that AI growth is inclusive and minimize negative effects for people and the planet?
Speaker
Georg Enzweiler
Explanation
This addresses fundamental concerns about AI’s societal impact and the need for responsible development that benefits all segments of society.
What kind of effect would AI have on labor markets?
Speaker
Georg Enzweiler
Explanation
This is a critical question for policy makers and industry leaders to understand workforce transformation and prepare for employment changes.
How can we achieve trustworthy responses from AI systems?
Speaker
Dr. Thomas Kuhn
Explanation
Trustworthiness is essential for AI adoption in critical applications like healthcare, manufacturing, and safety-relevant environments.
How can we preserve knowledge in companies when people retire, especially in small and medium-sized enterprises?
Speaker
Dr. Thomas Kuhn
Explanation
This addresses a practical business challenge where institutional knowledge is lost when experienced employees leave.
How do we use AI in smart manufacturing and develop smart manufacturing standards?
Speaker
Dr. Rajkumar Upadhyay
Explanation
This is crucial for Industry 4.0 implementation and maintaining competitiveness in global manufacturing.
How can cross-border industrial data flows and safeguards be enabled?
Speaker
Dr. Rajkumar Upadhyay
Explanation
This is important for international collaboration while maintaining data security and sovereignty.
How can AI improve productivity, yield, and income for farmers in agriculture?
Speaker
Dr. Rajkumar Upadhyay
Explanation
Agriculture is a key economic sector where AI can address food security and farmer welfare challenges.
How can AI be used more effectively in cybersecurity to handle millions of attacks and process data in real-time?
Speaker
Dr. Rajkumar Upadhyay
Explanation
With increasing cyber threats, AI-powered security solutions are critical for national and economic security.
How can India and Germany work together on 6G development?
Speaker
Dr. Rajkumar Upadhyay
Explanation
This represents an opportunity for collaborative technology development in next-generation telecommunications.
How can quantum communication technologies be developed collaboratively?
Speaker
Dr. Rajkumar Upadhyay
Explanation
Quantum technologies are critical for future secure communications and computing capabilities.
How can AI fraud management systems be enhanced through international collaboration?
Speaker
Dr. Rajkumar Upadhyay
Explanation
Financial fraud is a global problem that could benefit from shared AI solutions and expertise.
How can we balance economic development and social growth in AI implementation?
Speaker
Prof. Dr. Kristina Sinemus
Explanation
This addresses the need to ensure AI benefits society broadly rather than creating or widening inequalities.
How can we translate the idea of trustworthy AI into testable criteria and practical procedures for business deployment?
Speaker
Prof. Dr. Kristina Sinemus
Explanation
This is essential for moving from theoretical concepts to practical implementation of ethical AI systems.
How can startup ecosystems in India and Germany work together for global benefit?
Speaker
Dr. Rajkumar Upadhyay
Explanation
This could accelerate innovation and create win-win opportunities for both countries’ entrepreneurial ecosystems.
How can we get over the paradigm shift of engineering and software development using AI?
Speaker
Dattatri Salagame
Explanation
This addresses the practical challenges organizations face in transforming their development processes with AI tools.
How can we ensure AI implementation in enterprise applications maintains explainability, transparency, fairness, and auditability?
Speaker
Sindhu Gangadharan
Explanation
These are critical requirements for enterprise AI adoption, especially in regulated industries and autonomous workflows.
Disclaimer: This is not an official session record. DiploAI generates these resources from audiovisual recordings, and they are presented as-is, including potential errors. Due to logistical challenges, such as discrepancies in audio/video or transcripts, names may be misspelled. We strive for accuracy to the best of our ability.
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