Keynote-Martin Schroeter
19 Feb 2026 14:15h - 14:30h
Keynote-Martin Schroeter
Summary
The session opened with Speaker 1 introducing Martin Schroeter, chairman and CEO of Kindrill, as a leading voice on moving AI from laboratory optimism to real-world production ([1-4]).
Schroeter framed the central challenge as turning AI into reliable, day-to-day operations at scale rather than isolated demos, emphasizing that failures in critical sectors such as hospitals or energy grids can have life-changing consequences ([12-15]).
He argued that the problem is not a lack of innovation-AI technology is “brilliant”-but a readiness gap, noting that while over two-thirds of organisations have heavy AI investment, almost half still fail to achieve meaningful returns ([20-23]).
In India, 75 % of projects stall after proof-of-concept, which he attributes to the fact that AI has not yet been industrialized; the necessary infrastructure, data pipelines, operational processes and skilled people are missing ([24-28]).
Kindrill’s customers therefore seek clarity on four readiness questions: how to deploy AI across fragmented data environments, whether systems can run 24/7 without failure or cyber-attack, how to integrate agentic AI into regulated, mission-critical settings, and how to prepare the workforce for new AI-augmented roles ([29-38]).
Trust emerges as the overarching concern, with leaders needing assurance that AI decisions are accountable, transparent and explainable, especially in regulated domains such as government and banking ([44-45]).
Schroeter highlighted India as a crucial proving ground for industrializing AI at national scale, citing initiatives like Digital India and the India AI Mission that create policy, digital and talent foundations ([50-53]).
He gave concrete examples: the Unified Lending Interface that reduces loan approval time from weeks to minutes, and the deployment of agentic AI at Bangalore International Airport to shift IT operations from reactive to proactive, self-healing modes ([54-58]).
Through community partnerships, Kindrill is also building digital and cybersecurity skills and launching a cyber-defense operations centre in Bangalore to counter AI-enabled threats at the network edge ([59-60]).
The speaker stressed that moving from invention to impact requires industrializing AI governance, embedding auditability, logging, explainability and compliance directly into live systems, a strategy he calls “policy as code” ([65-68]).
He urged policymakers and companies to focus on scalable infrastructure, trustworthy security and a skilled workforce as the fundamentals for responsible AI deployment ([69-71]).
Finally, Schroeter concluded that the future of AI will be decided not by research labs or boardrooms but by the choices and investments made today to bridge experimentation and industrialization, thereby strengthening the institutions societies rely on ([76-83]).
The discussion underscored that responsibly industrialized AI can move beyond optimization to deliver reliable, inclusive outcomes for people, planet and progress ([55-57][82-83]).
Keypoints
Major discussion points
– AI is at a readiness / industrialization crossroads, not an innovation problem. Schroeter stresses that while AI technology is “brilliant,” it is not yet industrialized; the infrastructure, data, operations, and people are unprepared for large-scale, reliable deployment [21-28]. He calls for moving governance from policy documents into live systems, embedding auditability, explainability, and compliance [65-68].
– Four critical readiness questions dominate customers’ concerns. These include how to deploy AI across fragmented, multi-cloud data; whether AI can run 24 × 7 without failure, cyber-attacks, or data drift; the suitability of agentic AI for mission-critical, regulated environments; and how to prepare the workforce for new AI-augmented ways of working [30-41].
– India is presented as a strategic proving ground for responsible, large-scale AI. The speaker highlights national initiatives such as Digital India and the India AI Mission, and cites concrete examples-Unified Lending Interface, agentic AI at Bangalore International Airport, and a new cyber-defense operations centre-to illustrate how AI can be deployed at national scale [50-60].
– Trust and governance are portrayed as prerequisites for AI impact. Trust is built through clear guardrails, accountability, transparency, and explainability, especially in regulated sectors like banking, government, and healthcare [44-46][71-75]. “Policy as code” is offered as a mechanism to embed these safeguards directly into AI systems [66-68].
– A call to action for coordinated investment, reskilling, and partnership. The speaker urges companies and governments to focus on scalable infrastructure, security, and people-skill development, emphasizing that the future of AI depends on closing the gap between experimentation and industrialization [69-83].
Overall purpose / goal
The discussion aims to shift the narrative from AI hype to practical, responsible industrialization. Schroeter seeks to convince policymakers, business leaders, and technologists that achieving real-world impact requires addressing readiness challenges-technical, regulatory, and human-through scalable infrastructure, robust governance, and workforce transformation, using India’s ecosystem as a model.
Tone of the discussion
– Opening (0:00-5:00): Formal, appreciative, and optimistic, thanking leaders and framing AI as a transformative opportunity.
– Middle (5:00-15:00): Cautionary and analytical, highlighting concrete readiness gaps, operational risks, and the need for trust.
– Later (15:00-end): Solution-focused and inspirational, showcasing successful Indian deployments, outlining governance approaches, and issuing a rallying call for collective action. The tone progresses from celebratory acknowledgment to sober problem-identification, and finally to an urgent, hopeful call to industrialize AI responsibly.
Speakers
– Martin Schroeter – Role/Title: Chairman and CEO, Kindrill (Kyndryl) – Area of expertise: IT infrastructure services, AI operationalization and industrialization [S2]
– Speaker 1 – Role/Title: Moderator/host introducing the keynote speaker – Area of expertise: (not specified) [S3][S5]
Additional speakers:
(none)
Speaker 1 opened the session by introducing Martin Schroeter, chairman and CEO of Kindrel, as a leading voice on turning AI hype into production-grade solutions. Schroeter thanked Prime Minister Narendra Modi and the summit’s ministers, policymakers, CEOs and global livestream audience for convening the event and framed the gathering as an “extraordinary opportunity” to discuss responsible AI for people, industry and communities [1-4][5-11].
He identified the core problem: AI must move from demos and pilots to reliable, day-to-day operation at national and enterprise scale. In hospitals, banks, transport networks and energy grids, failure is not a mere inconvenience but a threat to lives, making operational reliability a prerequisite for the summit’s pillars of people, planet and progress [12-18][13-15].
Schroeter argued that the bottleneck is not a lack of innovation; the technology is “brilliant,” but AI has not yet been industrialised. The gap lies in infrastructure, data pipelines, operational processes and skilled personnel. While more than two-thirds of organisations globally invest heavily in AI, almost half still struggle to realise meaningful returns, and in India 75 % of projects stall after the proof-of-concept stage [22-24].
Kindrel’s customers focus on four critical readiness questions:
1) Deploying AI across fragmented, multi-cloud and edge data environments while integrating legacy core systems [30-31];
2) Guaranteeing 24 × 7 reliability, security, resilience to cyber-attacks, data drift and regulatory scrutiny, thereby earning user trust [32-36];
3) Safely integrating agentic AI into regulated, mission-critical settings [37-39];
4) Upskilling the workforce for AI-augmented roles, noting that nine in ten leaders expect profound change while fewer than one in three feel staff are ready [40-43].
Trust is built through clear guardrails that embed auditability, logging, explainability and compliance directly into AI systems-a “policy-as-code” approach that moves governance from static documents into live code [62-66].
India is presented as a strategic proving ground for responsible, large-scale AI. National initiatives such as Digital India and the India AI Mission provide policy, digital and talent foundations. A concrete example is the Unified Lending Interface, which reduces loan-approval times from weeks to minutes while enhancing transparency [50-52].
Kindrel’s footprint in India includes building scalable platforms for banking, citizen services, telecoms and airports that handle millions of transactions each day [54-56]. At Bangalore International Airport, agentic AI enables proactive, self-healing IT operations, shifting from reactive to autonomous management [55-57].
Through community partnerships, Kindrel is upskilling digital and cybersecurity talent, and it will open a new cyber-defence operations centre in Bangalore to detect and contain AI-driven threats at the network edge before they cause disruption [58-60].
Schroeder called on policymakers and enterprises to focus on three fundamentals-scalable infrastructure, trustworthy security and a skilled workforce-and to measure AI’s impact beyond productivity gains, including how institutions help societies adapt to the next phase of industrial automation [69-73].
He concluded that the future of AI will not be decided in research labs or boardrooms but by today’s choices and investments that bridge experimentation and industrialisation. The transition is both technical and human: building trust, reskilling workers at scale and ensuring AI systems are worthy of the institutions society relies on [70-74].
Ladies and gentlemen, I would now like to welcome Mr. Martin Schroeter, who is the chairman and CEO, Kindrill. As the leader of the world’s largest IT infrastructure services company spun out of IBM, Mr. Martin Schroeter manages the technology backbone of thousands of enterprises across the globe. His view of what it takes to actually run AI in production environments offers a necessary corrective to summit stage optimism. Ladies and gentlemen, please join me in welcoming the chairman and CEO of Kindrill, Mr. Martin Schroeter.
Thank you. Thank you. Thank you very much. Good afternoon, everybody. First, I want to thank the Honorable Prime Minister of India, Sri Narendra Modi, for convening this distinguished group of ministers, policymakers, global leaders, fellow CEOs, and of course, everybody watching on the live stream. And I want to thank all of you for your support and for your support for the initiative that we are carrying out in this country. And I want to thank all of you for your support and for your support for the initiative that we are carrying out in this country. It is an extraordinary opportunity for us to be here with you as we all focus on how to usher in this new era of AI responsibly for people, for industry, and for our communities.
Today, I’m proud to represent the collective knowledge and experience of Kindrel’s engineers, technical practitioners, problem -solving consultants, the people who support the mission -critical systems that the world depends on every day. As the largest IT infrastructure services provider, the question that we continuously come back to at Kindrel, and one that I suspect many of the policymakers and the business leaders and the technologists and the citizens here among us have, is how do we actually make AI work in the real world for real -world impact? Not a demo, not a pilot or an experiment. And not in theory, but in day -to -day operations under real constraints with people working alongside AI agents at national and enterprise scale.
Scale means something here in India that’s different than anywhere else, where failure of these systems is just not an option. Because when AI moves, when it moves from labs into the systems that power economies, the hospitals and the banks and the transportation networks and the energy grids and the governments, getting it wrong, and these are the systems we run every day, getting it wrong is not just an inconvenience, it actually impacts lives. And these systems sit at the heart of what this summit represents, the people, the planet, and the progress that we’re all working on. Progress in all three depends on the ability to operationalize AI reliably and, again, at scale. So today I’ll share a bit about what we’re learning, working with our global customer base and our partners to close the gap between investments, intelligence and reality, and where AI either becomes part of how we work and how work actually gets done.
or never makes it out of the experimentation phase. And what we’re seeing is not an innovation problem. The innovation is real, but it’s a readiness problem. We’ve conducted global studies with business and IT leaders countless times, and our research shows that while more than two -thirds of global organizations are already heavily invested in AI, almost half still struggle to see meaningful returns. And in India, in India alone, 75 % said their innovation efforts stall after the proof -of -concept stage. So based on our research and our experience with our customers, both in regulated and unregulated industries, the reason, the leading indicator for why projects stall is not because of the technology isn’t smart. It’s brilliant. It’s brilliant.
It’s because we haven’t industrialized it yet. AI today is not industrialized. The infrastructure, the data, the operations, and the people simply aren’t ready to support AI adoption and deployment at scale. So our customers really want greater clarity and greater support on four critical questions. First, on operational conduct, they want to know how to deploy AI when data is fragmented across clouds, across their core systems of record, and at the edge of the environments in which they operate. When business processes were never designed for AI, and when regulations differ by sector and by geography, and when trust, security, and resilience are imperative to how it works. Second, and more systemically, they’re asking, can this system really run 24 by 7 without failure?
Can it withstand cyber attacks and outages and data drift and regulatory scrutiny? And can the people trust it when it matters most? And can it? Can they trust the decisions it’s going to make? Those are the systems we run every day. Third, they’re asking about agentic AI. Whether they’re truly ready to use it in mission -critical environments, are they able to meet the regulatory requirements that come with those environments, and are they able to integrate with existing systems? And fourth, they’re asking about their workforce. How to prepare people for new ways of working with AI. Nine in ten leaders expect AI to fundamentally reshape work, yet fewer than one in three believe their workforce is ready.
Or that they’re equipped to help their teams get there. All of this ladders up to trust. Can leaders trust these AI systems and the insights they provide? And that trust is built when AI operates within clear guardrails where actions are accountable and transparent and explainable, which is essential for organizations in every industry, and especially in government, in banking, and other regulated environments. These are the core readiness questions. And the core readiness challenges that we see every day. And they’re at the heart of why so many AI initiatives stall. They remind us that innovation must operate reliably, predictably, and securely, day after day, in the real world. So I’m thrilled that this year’s AI Summit is India because India is one of the world’s most important proving grounds for industrializing AI at extraordinary scale.
Under the leadership of Prime Minister Modi, India has recognized AI as a strategic national priority, building policy and digital and talent foundations needed to support innovation, and again, at scale. Through initiatives like Digital India and the India AI Mission, and investments in digital public infrastructure, India has positioned itself not just as an adopter of AI, but as a global contributor to how AI can be deployed responsibly and inclusively. AI -powered platforms like the Unified Lending Interface are expanding access to credit at scale, reducing loan times from weeks to minutes, and while improving transparency and inclusion. India’s digital experience offers an important lesson for the world when technology must operate at a national scale across public services and financial systems, healthcare, transportation, and energy.
Reliability, governance, and human integration are not features, they are prerequisites. Kindle is very proud to be a partner to many of India’s leading companies and government agencies. Our local engineering teams have built scalable platforms for banking, for citizen services, for telecoms, and for airports to handle the millions of users and transactions every day. At Bangalore International Airport, we’ve applied agentic AI to shift IT operations from a reactive response to a proactive resilience, supporting self -healing capabilities that improve operational predictability and strengthen trust in the airport’s digitalization. Through our community partnerships in India, we’re helping build digital and cybersecurity skills because safe, responsible AI adoption depends on people being ready. not just technology. And because sophisticated adversaries are already using AI to move at machine speed tomorrow, tomorrow we’re opening a new cyber defense operations center in Bangalore so we can detect and contain threats that already start at the edge of the network before they become disruptions.
So we are deeply committed to helping India and our partners around the world implement AI at the scale to drive people, planet, and progress outcomes. In every part of the globe, conversation about agentic must now shift from intelligence to industrialization, from what AI can do to how it’s orchestrated and how it’s governed and secured and integrated, and how it’s sustained with agents and humans partnering to drive business impact. This is a transition every major technology invention has gone through. Invention comes first, but impact only comes when society’s learned how to industrialize it safely, reliably, and at scale. A critical part of this industrialization is operationalizing the governance of AI. That means moving governance out of policy documents and into live systems, embedding auditability, logging, explainability, and compliance directly into how AI operates.
We’re seeing how our approaches, like policy as code, can establish clear guardrails for agentic AI to drive trust and compliance, giving regulators, boards, and the citizens alike the confidence in these systems are controlled, accountable, and safe. So what do we do next? Excuse me. We get ready by focusing on the fundamentals, infrastructure that can scale, security that earns trust, and people with the skills to operate. We operate AI responsibly. This readiness perspective is particularly important for policymakers. Excuse me. Because the impact of AI cannot be measured only by productivity gains or economic growth. as important as those are to drive the future, it will also be measured by how institutions help people adapt in the next phase of industrial automation and how work evolves.
Excuse me. AI can absolutely change the world. It can change work, it can change skills, it can change mindsets, and it can change operating models. But it will only change, oh, thank you very much, it will only change the world when it is embedded responsibly and reliably into the systems that society depends on every day. The future of AI will not be decided in the research labs or the boardrooms. It will be decided by the choices and the investments we make now, by how we close the gap between experimentation and industrialization. Excuse me. The work ahead is hard, because this is not just a technology shift, it’s a human shift. We have to build trust in AI, we have to reskill our workforces at scale, and we have to ensure these systems are worthy of the societies that depend on them.
The responsibility belongs to the companies and the governments alike. And it is a responsibility worth embracing, because when AI is industrialized responsibly, it doesn’t just optimize. It strengthens the institutions people rely on every day. And that is how AI truly changes the world. Thank you very much.
“Martin Schroeter, chairman and CEO of Kindrel”
The knowledge base identifies Martin Schroeter as chairman and CEO of Kyndryl, not Kindrel, indicating the company name is misspelled in the report.
“The bottleneck is not a lack of innovation; the technology is “brilliant,” but AI has not yet been industrialised.”
The knowledge base states that technology is not the bottleneck and that success requires changes to processes, organization, incentives, skills, and culture, confirming the speaker’s point.
“In India 75 % of projects stall after the proof‑of‑concept stage”
The knowledge base reports that almost 80 % of AI pilots do not make it to production, but it does not provide an India‑specific 75 % figure, suggesting the reported statistic is inaccurate or unsupported.
“Deploying AI across fragmented, multi‑cloud and edge data environments while integrating legacy core systems”
The knowledge base discusses the risks and complexities of hybrid and multi‑cloud environments, adding nuance to the challenges of fragmented cloud deployments.
“While more than two‑thirds of organisations globally invest heavily in AI, almost half still struggle to realise meaningful returns”
The knowledge base notes that a large share of AI pilots (around 80 %) fail to reach production, providing supporting context for the difficulty organisations face in achieving returns.
The discussion shows a clear convergence on the importance of responsible, production‑grade AI. Both the moderator and the keynote speaker stress that AI must be trustworthy, governed, and operationally ready before it can deliver societal benefits. Schroeter’s detailed arguments about readiness, governance, and workforce skills reinforce this shared stance.
High consensus on the need for responsible AI deployment; this alignment signals strong support for policies and industry actions that prioritize industrialisation, governance, and trust as prerequisites for AI impact.
The transcript shows strong alignment between the moderator’s framing and Schroeter’s detailed discussion. Both stress moving AI from pilot to production, the importance of readiness, and the need for trustworthy, transparent systems. No substantive contradictions appear between the speakers.
Minimal to none – the speakers are largely in agreement, indicating a cohesive narrative that reinforces the summit’s focus on responsible, industrial‑scale AI deployment.
Martin Schroeter’s remarks transformed what could have been a routine product showcase into a nuanced, multi‑layered dialogue about AI’s readiness for real‑world, mission‑critical use. By first debunking the myth that technology alone drives impact, he redirected attention to the industrialization gap. His articulation of four concrete readiness questions and the concept of ‘policy as code’ supplied a practical roadmap, while historical analogies and the emphasis on trust and human factors broadened the scope to include ethical, regulatory, and societal dimensions. These pivotal comments steered the discussion from abstract optimism to grounded, actionable challenges, prompting policymakers, executives, and technologists to reconsider priorities, align on governance frameworks, and invest in workforce transformation. Collectively, they shaped the conversation into a forward‑looking, responsibility‑centered narrative that underscores AI’s potential only when it is reliably, transparently, and human‑centrically industrialized.
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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