Dr. Santanu Sinha

My bet is on INDIA 🇮🇳 not on Silicon Valley or Shenzhen India isn’t just participating in the global AI race — we are shaping it. With the 𝗜𝗻𝗱𝗶𝗮𝗔𝗜 𝗠𝗶𝘀𝘀𝗶𝗼𝗻 (₹𝟭𝟬,𝟯𝟬𝟬 𝗖𝗿) and the 𝗦𝗲𝗺𝗶𝗰𝗼𝗻𝗱𝘂𝗰𝘁𝗼𝗿 𝗠𝗶𝘀𝘀𝗶𝗼𝗻 (₹𝟳𝟲,𝟬𝟬𝟬 𝗖𝗿), we’re building the compute and chip backbone needed for long-term innovation. Our 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗣𝘂𝗯𝗹𝗶𝗰 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 — Aadhaar, UPI, India Stack — offers something few nations have: trusted rails to deploy AI at population scale. We’re solving for 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗮𝗻𝗱 𝗶𝗻𝗰𝗹𝘂𝘀𝗶𝘃𝗶𝘁𝘆 through initiatives like 𝗕𝗵𝗮𝘀𝗵𝗶𝗻𝗶 and homegrown Indic models, ensuring AI speaks to every Indian. Add to this the 𝘄𝗼𝗿𝗹𝗱’𝘀 𝗹𝗮𝗿𝗴𝗲𝘀𝘁 𝗦𝗧𝗘𝗠 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲 and national skilling programs like 𝗙𝘂𝘁𝘂𝗿𝗲𝗦𝗸𝗶𝗹𝗹𝘀 𝗣𝗥𝗜𝗠𝗘, and we have a talent advantage that compounds over time. Startups, academia, and global R&D hubs are already translating this foundation into products, while investment in generative AI ventures is accelerating. The equation is simple: 𝘀𝗰𝗮𝗹𝗲 + 𝘀𝗸𝗶𝗹𝗹𝘀 + 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆 = 𝗹𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽. India’s AI future looks promising not just for us, but for the world. Now is the time to execute, collaborate, and innovate at scale. #𝗜𝗻𝗱𝗶𝗮𝗔𝗜 #𝗗𝗶𝗴𝗶𝘁𝗮𝗹𝗣𝘂𝗯𝗹𝗶𝗰𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 #𝗔𝗜𝗟𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 #𝗙𝘂𝘁𝘂𝗿𝗲𝗦𝗸𝗶𝗹𝗹𝘀

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As AI conversations accelerate in India, my Forbes India article focuses on an important but often underplayed point: without unified data foundations, AI remains episodic. https://lnkd.in/gWe68mzc A unified data stack isn’t just architecture — it’s governance, interoperability, metadata, and operating models working together. This is very much in line with how I frame Data 4.0: moving from managing data to designing it for intelligent, agent-driven systems. More context on Data 4.0 here: https://lnkd.in/gnqB-fWn

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The ₹100 crore award for the 1st Nobel laureate in Quantum Science announced by Andhra Prades, as part of Quantum Valley, initiative is a bold & direct policy move to position the state as a global hub for quantum computing. Andhra government turning Santa is yet another move by the state to attract top talent and drive innovation. Have there been similar global initiatives? Quite a few ie - US ARPA-E Quantum Development: Focuses on advancing quantum computing hardware and applications. - EU Quantum Flagship: A €1 billion initiative to establish Europe as a leader in quantum technologies. - China Quantum Industry Pilot: Aims to develop a quantum computing ecosystem and apply quantum technologies to industries like finance and healthcare. - UK Quantum Technology Programme: Invests in quantum research and development, focusing on computing, communications, and sensing. What about success rates? Success rates vary, but notable achievements include: - Google Quantum AI: Achieved quantum supremacy in 2019 and is working towards fault-tolerant quantum computing. - IBM Quantum: Developed a 127-qubit processor and is exploring practical applications. - China's Quantum Satellite: Successfully demonstrated quantum key distribution over 1,000 km. Key questions: Will other states follow suit and will such initiatives position India in the global quantum computing landscape? If successful, it could help position India as a key player in the global quantum ecosystem. Obviously, there are many more things wrt creating an enabling eco-system that are needed but well begun is half done as they say. #India #Innovation #Quantum #Technology #Policymaking #Leadership

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With all the feverish noise surrounding Indian Global Capability Centers, how can they evolve their value propositions from efficiency to real value? https://lnkd.in/gyJPZqiD Saurabh Gupta HFS Research Biswadeep Ghosh Hazra Srini Vaddepalli Ashwin Venkatesan Reetika Fleming #GCC #India

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I am happy to share that the research paper that I co-authored with Deepak Pandey and Vishal Shankar, “𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝘁𝗵𝗲 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗼𝗳 𝗮 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝗲 𝗔𝗜”, has been published in SAGE Journals. → https://lnkd.in/gRMFKbBy I want to highlight some of the key contributions and insights that my co-authors and I were able to develop: 𝟭. 𝗖𝗼𝗺𝗽𝗿𝗲𝗵𝗲𝗻𝘀𝗶𝘃𝗲 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸: We identified and deeply analyzed 𝟭𝟱 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹 𝗽𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲𝘀 𝘁𝗵𝗮𝘁 𝗳𝗼𝗿𝗺 𝘁𝗵𝗲 𝗯𝗲𝗱𝗿𝗼𝗰𝗸 𝗼𝗳 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝗲 𝗔𝗜 — from fairness, transparency, and accountability to robustness and privacy. This framework can serve as a reference point for both researchers and practitioners building AI systems that matter. 𝟮. 𝗚𝗮𝗽 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗮𝗰𝗿𝗼𝘀𝘀 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 & 𝗣𝗼𝗹𝗶𝗰𝘆: We conducted a detailed gap analysis, pointing out where current AI development practices fall short in operationalizing ethical ideals. By bridging that gap between theory and practice, 𝘄𝗲 𝗼𝗳𝗳𝗲𝗿 𝗮𝗰𝘁𝗶𝗼𝗻𝗮𝗯𝗹𝗲 𝗴𝘂𝗶𝗱𝗮𝗻𝗰𝗲 𝗳𝗼𝗿 𝗳𝘂𝘁𝘂𝗿𝗲 𝗔𝗜 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲. 𝟯. 𝗠𝘂𝗹𝘁𝗶-𝗦𝘁𝗮𝗸𝗲𝗵𝗼𝗹𝗱𝗲𝗿 𝗣𝗲𝗿𝘀𝗽𝗲𝗰𝘁𝗶𝘃𝗲: Our research embraced a multi-stakeholder perspective — considering not only developers but also regulators, users, and affected communities—𝗧𝗵𝗶𝘀 𝗵𝗲𝗹𝗽𝘀 𝗲𝗻𝘀𝘂𝗿𝗲 𝘁𝗵𝗮𝘁 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝗲 𝗔𝗜 𝗶𝘀 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗮𝗰𝗮𝗱𝗲𝗺𝗶𝗰 𝗯𝘂𝘁 𝘀𝗼𝗰𝗶𝗮𝗹𝗹𝘆 𝗴𝗿𝗼𝘂𝗻𝗱𝗲𝗱. 𝟰. 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗣𝗮𝘁𝗵𝘄𝗮𝘆𝘀: We didn’t stop at principles: we proposed operational pathways — practical processes and governance models — to 𝗲𝗺𝗯𝗲𝗱 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝗲 𝗔𝗜 𝗶𝗻𝘁𝗼 𝗲𝘃𝗲𝗿𝘆 𝘀𝘁𝗮𝗴𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗔𝗜 𝗹𝗶𝗳𝗲𝗰𝘆𝗰𝗹𝗲. This includes recommendations for auditing, design reviews, and continuous monitoring. 𝟱. 𝗙𝘂𝘁𝘂𝗿𝗲-𝗙𝗮𝗰𝗶𝗻𝗴 𝗥𝗼𝗮𝗱𝗺𝗮𝗽: Finally, 𝘄𝗲 𝗰𝗿𝗮𝗳𝘁𝗲𝗱 𝗮 𝗳𝗼𝗿𝘄𝗮𝗿𝗱-𝗹𝗼𝗼𝗸𝗶𝗻𝗴 𝗿𝗼𝗮𝗱𝗺𝗮𝗽 𝗳𝗼𝗿 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗮𝗻𝗱 𝗶𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻. We highlight priority areas for the AI community: building tools, establishing governance bodies, and promoting cross-disciplinary collaboration. I hope this paper contributes meaningfully to the conversation around ethical, trustworthy, and socially responsible AI. If you work in AI development, policy, or research, we would like to connect and explore how we can put these ideas into action. Please feel free to share your comments with me. Last but not least, thank you to my co-authors, reviewers, and everyone who supported this journey.

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Dr. Subarna Roy, Data Science Leader @ IBM | Chief Data Scientist | Quantum Ambassador, delivered a thought-provoking talk on “Transformer-Based Time Series Forecasting Approach” as part of the “Modelling & Forecasting of Energy & Financial Markets” course taught by Prof Sajal Ghosh in the PGDM–BA program. In this session, Dr. Roy discussed how advanced time-series forecasting enhances operational efficiency and decision-making across industries. She also outlined the evolution of forecasting methodologies, from classical statistical models to deep learning and state-of-the-art Transformer architectures, which are highly effective at capturing complex patterns in time-series data. The session concluded with a demonstration of how Transformer-based forecasting models can be developed and deployed to generate precise and actionable predictions for real-world applications. Management Development Institute, Gurgaon

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Shweta Shah, Ph.D. has suggested a concept that she calls "Content Quality Management". Shweta and I have been writing together for a while, and I am really impressed by this idea, that I think addresses exactly what the data community needs to consider, going forward in an era so indisputably dominated by AI. Shweta is adding to an ecosystem of thinkers like Bill Inmon (with his absolutely wonderful concept of the "textual warehouse") and Jessica Talisman (coiner of the "ontology pipeline"), and a wealth of people in the records- and information management space like tech entrepreneur Rachael Greaves, and skilled practitioners like Michael Upton, Andy Potter (deeply involved in ISO standards on AI records), and Stephen Clarke MRIM that are trained in thinking in the intersection of language and data. If you want ideas in how to succeed with AI, I suggest you follow Shweta Shah, Ph.D. #contentquality #contentqualitymanagement #ai #data #language #dataquality #dataqualitymanagement

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As enterprises move beyond system-level analytics, the focus is shifting to what really happens at the user level. That’s where Digital Interaction Intelligence (#DII) comes in. DII tools track granular digital interactions keystrokes, clicks, screen activity to help enterprises identify automation opportunities, boost operational efficiency, and improve the employee experience. Unlike process mining, DII offers a user-centric lens that leads to faster, more actionable insights. In our latest Digital Interaction Intelligence Products PEAK Matrix® Assessment 2025, Everest Group evaluates 18 leading DII software products on our PEAK Matrix®, assessing: ✅ Product vision and innovation strategy ✅ Security and privacy safeguards ✅ Deployment and support capabilities ✅ Commercial model flexibility and ease of integration Automation Anywhere ABBYY Celonis Cyclone Robotics EdgeVerve epiplex.ai IBM KYP.ai Microsoft Mimica NiCE Nintex Optimus Hive | Optimus Hive is a Revolutionary Business Productivity Application! Pegasystems Skan AI Soroco StereoLOGIC Ltd. UiPath Whether you're leading a digital transformation or exploring tools to boost productivity and insight, this assessment offers a clear view of where the DII market is headed. Read on: https://okt.to/tZndAe Get in touch: Amardeep Modi Santhosh Kumar Vadrevu Vershita Srivastava Shreepriya Sinha Niyati Vohra Sudeshna Chandra #DigitalInteractionIntelligence #ProcessAutomation #PEAKMatrix #EmployeeExperience #OperationalEfficiency #EverestGroup

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