{"id":7799,"date":"2026-06-11T13:47:59","date_gmt":"2026-06-11T13:47:59","guid":{"rendered":"https:\/\/fondationfranceasie.org\/?p=7799"},"modified":"2026-06-11T13:49:15","modified_gmt":"2026-06-11T13:49:15","slug":"sovereign-intelligence-architecting-the-france-india-ai-stack-for-the-automotive-future","status":"publish","type":"post","link":"https:\/\/fondationfranceasie.org\/en\/sovereign-intelligence-architecting-the-france-india-ai-stack-for-the-automotive-future\/","title":{"rendered":"Sovereign Intelligence: architecting the France-India AI Stack for the automotive Future."},"content":{"rendered":"
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Article by Neha Arolkar and Sarita Kaloya<\/p>\n

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Introduction<\/strong><\/p>\n

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At the recent India AI Impact Summit, as panellists, we both presented the Whitepaper \u2013 Indo-French Perspectives on Artificial Intelligence, focusing on the AI & Automotive segment which we co-authored. We highlighted that AI is already delivering measurable impact in parts of the automotive value chain while the industry works on scaling successful proofs of concepts and identifies new use-cases.<\/p>\n

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At the same time, this technological shift is unfolding within a broader context of geopolitical uncertainty, energy transition and the growing need for industrial resilience. AI is becoming a strategic asset, central to how nations and enterprises secure competitiveness and maintain control over critical industrial capabilities. This strategic imperative is particularly relevant for France and India, whose complementary strengths in engineering excellence, AI research and scalable digital capabilities position them uniquely in this landscape.<\/p>\n

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Building further on the Whitepaper, this article explores emerging applications of AI in automotive engineering alongside examining the implications pertaining to the current energy crisis, global supply chain shocks and industrial sovereignty. We also propose a sovereign industry-driven AI fund underlining Indo-French collaboration for the development of resilient, autonomous and future-ready automotive ecosystems.<\/p>\n

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AI Sovereignty, Industrial Resilience and the Indo-French Strategic Mandate<\/strong><\/p>\n

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From Globalized Efficiency to Regional Resilience<\/strong><\/p>\n

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The traditional global manufacturing model, which was once grounded in borderless efficiency and lean integration, has been rendered near-obsolete in the VUCA world. As we navigate the Horizon 2047 Roadmap, we recognize that geopolitical uncertainty is no longer a peripheral risk but the primary determinant of industrial strategy. The Engineering and R&D Pulse 2026 identifies geopolitical uncertainty as a macro-disruption for which only a third of organizations are prepared [1].<\/p>\n

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This instability is deeply linked to the energy-sovereignty nexus. Ongoing tensions in West Asia have catalyzed energy crises that threaten the foundations of industrial capacity. For France and India, it\u2019s essential to secure these foundations by shifting to industrial sovereignty and regional resilience. This shift is particularly critical in the automotive sector, where Electric Vehicles (EVs) are trending upward in light of the fuel crisis while self-driving is redefining the competitive landscape. To maintain strategic autonomy, our nations must transition from being mere consumers of globalized supply chains and AI stacks to becoming architects of sovereign industrial ecosystems [3, 5].<\/p>\n

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AI in Software-defined Mobility and Vehicle Engineering: Speed and Efficiency without compromising Quality to retain Market Share<\/strong><\/p>\n

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AI for Supply Chain Resilience<\/strong><\/p>\n

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The mobility industry remains highly vulnerable to disruptions in semiconductors, raw materials and fuel. AI-based forecasting models already enable supplier risk intelligence by integrating external signals (geopolitical, financial, operational) to predict disruptions, thereby helping organizations achieve 15-25% reduction in inventory holding costs and improved service levels. However, there is still limited visibility across tier-2 and tier-3 suppliers.<\/p>\n

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Generative Design and AI-powered Product Engineering<\/strong><\/p>\n

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AI is fundamentally transforming vehicle engineering by shifting from manual iteration to algorithm-driven design exploration. Generative design and AI-assisted simulation allow engineers to explore thousands of design permutations in hours versus weeks, significantly reducing design cycle times. AI-led CAD\/CAE automation enables up to 60\u201380% reduction in engineering effort on repetitive modelling and validation tasks.<\/p>\n

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Quality Automation<\/strong><\/p>\n

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Constrained by strict regulations, consumer expectations and brand value, most OEMs have already been using computer vision models to improve their defect detection accuracy and reduce maintenance downtime. However, to expedite further using AI and automation, OEMs run into a data challenge \u2013 most data is a mix of current and outdated information, scattered across ERP, PLM, MES, QMS systems. Low AI adoption and legacy systems are additional blockers.<\/p>\n

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Software-defined Mobility<\/strong><\/p>\n

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AI is becoming critical to enhancing vehicle performance and lifecycle value \u2013 for example, AI-driven battery management systems can improve EV range and energy efficiency through real-time optimization. OTA (over-the-air) updates via AI models can allow continuous performance improvement post vehicle launch. However, it still takes a significantly long time to bring a new vehicle from concept to road, while AI models are rapidly evolving. New Chinese OEMs that use state-of-the-art Product Lifecycle Management systems have been able to launch new models much faster.<\/p>\n

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Research from the Capgemini Research Institute mandates that automotive executives must reduce costs by at least 10% and development time by at least 9% over the next two to three years to compete with low-cost innovators, especially from Chinese OEMs [1]. Failure to meet these metrics risks the loss of significant market share, making AI-driven engineering the essential solution to bridge the gap between technical complexity and industrial reality.<\/p>\n

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Therefore, we observe that the industry is shifting from hardware-centric engineering to an AI-accelerated, process-focused model. To be future-ready in an agentic world, engineers are no longer expected to just design a component, rather they seek to reimagine the workflow that generates the component.<\/p>\n

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Low-code platforms have facilitated the democratization of AI since quite some time now. By wiring together visual nodes to create workflows, even engineers who are not professional programmers can leverage complex AI scripts for tasks such as topology optimization and generative design. Up to 80% of code or workflow logic can be reused in subsequent projects, effectively turning engineering expertise into a scalable digital asset [2].<\/p>\n

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Furthermore, AI agents do not merely assist but autonomously execute complex engineering workflows [2]. These agents can orchestrate the industrial value chain by connecting CAx tools and data into a unified system. Unlike traditional manual iterations, platforms like Synera and Dessia allow engineers to program design “rules,” enabling AI to handle repetitive CAD\/CAE execution [2, 4].<\/p>\n

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For example, ROI of AI-driven engineering as evidenced by Dessia Technologies is transformative [4]:<\/p>\n

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Solution Exploration: Exploring 1,000 design alternatives in just 6 hours.<\/p>\n

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Development Speed: Reducing total development time by 80%.<\/p>\n

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Process Reliability: Automating verification and validation to eliminate rework and reduce technical debt [4].<\/p>\n

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The Evolution of Partnerships: From Services to Sovereign Platforms<\/strong><\/p>\n

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The transformation of the traditional IT service provider and OEM relationship is central to our bilateral vision. We are moving beyond vendor-client transactions toward synergetic ecosystems built on performance-based outcomes.<\/p>\n

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Utilizing the Core-Context framework, leading OEMs are rethinking their partnership strategies. Until recently they were offloading \u2018Context\u2019 (typically legacy maintenance and repetitive tasks) to technology partners through predictable billing models while focusing internal talent on \u2018Core\u2019 engineering tasks with a competitive advantage. However, this situation is changing \u2013 OEMs are now increasingly open to outcome-based\/performance-based partnerships with leading technology providers, system integrators and frontier AI companies to expedite their \u2018Core\u2019 work. This could take the form of an end-to-end AI-powered orchestrated engineering platform housing various AI agents and tools. The goal is increasing OEMs\u2019 speed-to-market and cost efficiency to retain business in an increasingly competitive market. [1, 5]<\/p>\n

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As an example of the agentic orchestration approach to engineering platforms, the Synera platform acts as an automation meta-layer over existing CAx tools like Siemens, Catia, Abaqus, and Anza. Engineers can access different specialized tools or agents from different ecosystem players on a single platform on-demand, ensuring they can pick the best-fit solutions for a given problem [2]. For France and India, owning the orchestration layer of these ecosystems is a significant way to control their industrial intellectual property. Within the ecosystem, French deep engineering and simulation expertise combined with India\u2019s automation and platform engineering can enable scaling these capabilities across global programs.<\/p>\n

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The France-India Strategic Mandate: Owning the Sovereign AI Stack and Proposal for an Industry-driven Sovereign AI Fund<\/strong><\/p>\n

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Under the Special Global Strategic Partnership established in the India-France Joint Statement (2026), both nations have designated 2026 as the Year of Innovation [3]. Our mandate is clear: we must develop and own specialized AI models rather than remaining dependent on foreign tech stacks. Just as we prioritize sovereign access to space, we must prioritize sovereign access to AI to bridge the global AI divide and ensure decision-making autonomy [3].<\/p>\n

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Therefore, we propose the establishment of an IT & Automotive\/Mobility Sector Collaborative Fund, explicitly linked to the Joint Advanced Technology Development Group established in the Joint Statement (Section I, Para 12) [3]. This fund could focus on co-developing specialized models for automotive engineering, ensuring the data and knowledge bases of our respective industries are secured on-premise and protected from external volatility.<\/p>\n

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Existing collaborations serve as blueprints for bilateral success:<\/p>\n<\/div>\n

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Dessia x Capgemini:<\/strong> AI-driven design verification and cost estimation that reduces time-to-market and enhances precision [4].<\/p>\n

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Synera <\/strong>(backed by Capgemini via ISAI Cap Venture): Agentic AI platforms already deployed at scale by global leaders, including BMW, Airbus, Volvo Trucks, Hyundai and the NASA Goddard Space Flight Center [2].
\nThese partnerships exemplify shared commitment to the Horizon 2047 vision\u2014co-developing solutions for a smarter, more sustainable and sovereign future [3].<\/p>\n

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Conclusion: A Blueprint for Strategic Autonomy<\/strong><\/p>\n

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In a world reshaped by geopolitical disruption and energy transitions, the Indo-French axis has a strategic opportunity to move from collaboration to co-creation by building sovereign, AI-driven engineering capabilities that power the next wave of mobility. By combining France\u2019s deep engineering pedigree and frontier AI research with India\u2019s growing AI talent pool and IT scale, while anchoring this in outcome-based, platform-led partnerships, both nations can shift from being technology consumers to owners of critical AI stacks. A jointly backed sovereign AI fund nurturing specialized industrial models and ecosystems (with proven early examples) can catalyze this vision. Thus we position Indo-French collaboration not just as a response to global shifts, but as a blueprint for resilient, innovation-led industrial leadership [6].<\/p>\n<\/div>\n

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[1] Engineering and R&D Pulse 2026<\/i> \u2013 Capgemini Research Institute, 2025.<\/p>\n

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[2] Synera: Agentic AI Engineering Platform<\/i> \u2013 Press Materials, Case Studies, & Transcript: “Connected Engineering – Moritz Maier | Podcast #1”.<\/p>\n

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[3] India-France Joint Statement (February 17, 2026)<\/i> \u2013 Ministry of External Affairs, Government of India.<\/p>\n

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[4] Dessia Technologies: Generate, Verify and Validate Designs with AI<\/i> \u2013 Case Studies and Platform Overview, 2025.<\/p>\n

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[5] Intelligent Manufacturing for Automotive: How to Accelerate<\/i> \u2013 Capgemini Engineering, 2025.<\/p>\n

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[6] White Paper Indo-French Perspectives on Artificial Intelligence<\/a>\u00a0by the France India AI Initiative, led by the Fondation France-Asie and the France India Foundation.<\/p>\n

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*****<\/div>\n

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Neha Arolkar is Client Partner, India Client Leader and Global Innovation Leader for a strategic automotive account at Capgemini. Recognized as an \u2018Innovation Driver\u2019 among multiple Client Partners by the Chief Sales Officer and India CEO, she is featured as an expert in several thought leadership publications and integrated annual report of Capgemini since 2020. An IIM-Bangalore alumna, she is a speaker and contributor at IIT Bombay Management Consulting Continuum, IAA Mobility Munich, Digital Product Forum Stuttgart, Indo-French Mobility Conclave and Women Automotive Network. Her work spans AI-powered growth for clientele across Europe, Americas and Asia. As Capgemini\u2019s First Young Leader at the France-India Foundation\u2019s Young Leader Program, she conceptualized and launched the AI for Automotive Accelerator under the France-India AI Initiative. She has also co-authored and co-presented the first Whitepaper of this initiative at the India AI Impact Summit 2026.<\/p>\n

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Sarita Kaloya is a Senior Director at Capgemini, leading AI and Data initiatives for global automotive and industrial clients. With extensive experience across North America, Europe and Asia, she specializes in scaling enterprise AI from strategy to industrialized deployment, with a focus on cloud, data platforms, and applied AI use cases across engineering, manufacturing, and connected ecosystems. She has led multiple large-scale AI transformation programs, helping organizations transition from pilots to production by building robust data foundations and AI-led operating models. Her work emphasizes responsible AI, platform-led delivery, and measurable business outcomes. Sarita actively contributes to Indo-French collaboration initiatives, bringing together research, engineering excellence, and execution capabilities to accelerate innovation and value realization. She is a strong advocate for scalable, trustworthy AI that enables industrial resilience and competitiveness in the evolving mobility landscape.<\/p>\n<\/div>\n

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This publication reflects the views and opinions of the individual authors. As a platform dedicated to the sharing of information and ideas, our objective is to highlight a diversity of perspectives. Accordingly, the opinions expressed herein should not be interpreted as those of the Fondation France-Asie or its affiliates.<\/p>\n","protected":false},"excerpt":{"rendered":"

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