AI for Economic Development and Social Good

Abstract

The session convened senior policymakers, research leaders, and industry CEOs from India and Germany to explore how artificial intelligence can be harnessed for inclusive economic growth and societal benefit. After an opening address by the Indian minister, a special address highlighted Indo‑German strategic collaboration. Dr Thomas Kuhn presented Fraunhofer’s “augmented intelligence” approach, emphasizing trustworthy AI, knowledge‑preserving industrial AI, and data‑space infrastructure. Ms Anandi Iyer outlined Germany’s AI‑for‑social‑good agenda, showcasing health‑care, agriculture and trust‑by‑design programmes. A CEO panel (Bosch, SAP, Aumovio, Mercedes‑Benz) then discussed concrete use‑cases, challenges and opportunities for AI‑driven manufacturing, agriculture, cyber‑security, quantum communication and fraud‑prevention, underscoring the need for responsible, collaborative innovation.

Detailed Summary

Ashwini Vaishnaw (Minister, GoI)

• Welcomed the Indo‑German community and highlighted the existing Memorandum of Understanding (MoU) between India and Fraunhofer.
• Stressed that many MoU‑driven activities have already been launched, signalling a “fantastic” start to the collaboration.
• Introduced the day’s line‑up: a special address, a Fraunhofer presentation, and a “power‑packed” industry CEO session that would follow the panel.

2. Special Address – Indo‑German AI Partnership

Georg Ensweiler (guest speaker)

• Praised the Fraunhofer presence in India and the breadth of the panel.
• Framed AI as a catalyst for social and economic good, estimating a contribution of USD 5–15 trillion to global GDP by 2030‑33.
• Raised critical questions: inclusivity of growth, environmental impact, labour‑market effects, and the summit’s mantra “welfare for all”.
• Outlined Germany’s AI “lighthouses” (over 60 projects since 2020) targeting climate, biodiversity, renewable energy and circular economy.
• Announced the newly signed India‑Germany‑AI (IA) Pact – a framework covering industry, talent mobility, joint research, infrastructure and AI for social good.

3. Fraunhofer’s Augmented Intelligence Vision

Dr Thomas Kuhn (Fraunhofer IESE)

• Defined Fraunhofer’s terminology: “augmented intelligence” – AI that enhances, not replaces, human expertise.
• Key research pillars
  1. Trustworthiness – developing uncertainty wrappers that attach a confidence score to every AI output, crucial for safety‑critical domains (medical diagnostics, traffic, manufacturing).
  2. Industrial AI & Knowledge Preservation – creating “virtual colleagues” that capture tacit expertise of retiring workers, enabling SMEs to retain critical know‑how.
  3. Data Spaces – secure, rule‑based cloud environments that allow cross‑company data sharing for AI training while protecting privacy and business secrets.
• Described Fed­erated AI training as a method for collaborative model building without exposing raw data, enabling distributed robotics and industry‑wide standards.
• Highlighted Fraunhofer’s AI‑Big Data Alliance of >30 institutes covering life‑sciences, logistics, production, energy, finance, security and defence.
• Presented selected use‑cases: embedded AI for predictive maintenance, image‑analysis for cancer diagnostics, swarm‑intelligence for logistics, and AI‑driven smart‑grid optimisation.

4. German Government Perspective on AI for Social Good

Anandi Iyer (Fraunhofer Office India)

• Emphasised that AI must serve people first – the “technology‑serves‑people” principle.
• Described Germany’s €60 billion AI funding programme supporting 170 start‑ups, with a focus on health, agriculture and trustworthy AI.
• Health‑care examples
  • DEMU – a load‑bearing robotic wheelchair prototype (funded €1.8 M) that autonomously traverses stairs and uneven terrain, improving independence for persons with mobility impairments.
  • RISCA – AI‑driven ECG risk‑stratification platform for early cardiovascular disease detection.
• Agriculture example – AI‑based satellite‑image analysis for early plant‑disease detection, reducing pesticide use and boosting yields.
• Introduced the AI Innovation Lab at “Haiti AI” (high‑performance computing hub) and the AI Quality & Testing Hub in Hessen that turns “trustworthy AI” into testable standards.
• Stressed the synergy: India’s scale (U‑PI, India‑Stack) + Germany’s regulatory + data‑protection expertise → a bridge between innovation and rights.

5. CEO Panel – Industry Viewpoints

CEO / Representative	Organisation	Main Themes & Concrete Initiatives
Dattatri Salagame	Bosch Global Software Technologies	• Deploying AI in autonomous driving, medical devices, and industrial automation. • Dual focus: market‑ready AI products and internal transformation of engineering processes through AI‑augmented design.
Sindhu Gangadharan	SAP Labs India (also NASSCOM, Indo‑German Chamber)	• 97 % of global enterprises already run SAP; responsibility to embed AI responsibly across core processes (lead‑to‑cash, procurement, sourcing). • Emphasis on explainability, transparency, fairness for autonomous workflow AI.
Anshuman Awasthi	Mercedes‑Benz R&D India	• AI already in cars since 2019 (e.g., AI‑driven driver assistance). • Future focus: AI‑enabled operational efficiency in manufacturing and customer‑facing AI in vehicles.
Prashanth Doreswamy	Aumovio India (formerly Continental)	• AI for R&D efficiency (20 % gain), quality‑enhancement, and agentic AI for finance/controlling analytics. • Showcased enhanced night‑vision and e‑travel autonomous vehicle demos at the German pavilion.
Murali Nair	Bertelsmann Stiftung	• Highlighted think‑tank research positioning India as a positive partner for Germany long before official diplomatic overtures.
(Additional CEOs invited for rapid‑fire)	Various (Bosch, SAP, Aumovio, Mercedes‑Benz)	• Discussed cost arbitrage, demographic dividend, skill up‑skilling, and AI‑enabled fraud detection (e.g., real‑time spoof‑call filtration at 5 ms). • Stressed the need for cross‑border data flows, standardisation, and AI‑driven cyber‑security (quantum‑communication, digital‑intelligence platform).

Key discussion points

• Trust vs. Speed – CEOs agreed that rapid AI adoption must be balanced with robust governance to avoid widening inequalities.
• Talent & Reskilling – The German “AI‑for‑societal‑good” emphasis on up‑skilling aligns with India’s massive AI talent pool (≈ 15 % of global AI talent).
• Data Sovereignty & Data Spaces – Repeated call for secure, rule‑based data sharing mechanisms to enable joint R&D while protecting proprietary information.
• AI for Climate & Agriculture – Both sides highlighted AI‑driven precision farming, early disease detection, and AI‑optimized energy consumption in manufacturing as high‑impact use‑cases.

6. Rapid‑Fire Round & Closing Remarks

• Moderator (Ashwini Vaishnaw) invited the four CEOs to the stage for a concise Q&A.
• Bosch spoke of the paradox of legacy (100‑year heritage) vs. AI‑driven paradigm shift in engineering.
• SAP reiterated the ethical responsibility of its massive customer base and the need for explainable AI in autonomous workflows.
• Aumovio highlighted AI‑enhanced quality control and agentic AI for finance & analytics, showcasing live demos (night‑vision, e‑travel).
• Mercedes‑Benz emphasized that AI is already embedded in its vehicles (since 2019) and now being used to tighten operational efficiency across its global supply chain.

The session concluded with a collective call to co‑create AI solutions that are trustworthy, inclusive and scalable, leveraging the unique strengths of both nations.

Key Takeaways

• Strategic Indo‑German AI Pact signed just days earlier will drive joint work across industry, talent mobility, research, and AI for social good.
• Fraunhofer’s “augmented intelligence” model puts human expertise at the core, focusing on trustworthiness, knowledge‑preservation, and secure data‑spaces.
• Germany’s €60 billion AI fund is channelled into health‑care, agriculture and trustworthy‑AI infrastructure; Indian scale and digital public‑infrastructure (U‑PI, India‑Stack) complement this strength.
• Trustworthy AI (uncertainty wrappers, explainability, fairness) is a cross‑cutting requirement for all sectors—manufacturing, finance, healthcare, and autonomous systems.
• Data Spaces are identified as the enabling technology for cross‑company AI training while safeguarding proprietary and personal data.
• Industry CEOs stress a dual agenda: rapid market‑ready AI product deployment and internal transformation of engineering and R&D processes via AI.
• AI for climate‑friendly manufacturing and precision agriculture emerged as high‑impact, mutually beneficial use‑cases.
• Cyber‑security & quantum communication are priority areas where India seeks German expertise, especially for large‑scale real‑time threat detection.
• Skill development and reskilling are pivotal; the partnership aims to leverage India’s large AI talent pool while adopting Germany’s standards for ethical AI.
• Inclusive growth—the overarching goal—is to ensure AI benefits are distributed widely, avoiding new inequities while accelerating sustainable economic development.

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See Also:

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