Ananta Krishnan

Our own EVP, Chief Scientist & Head of Enterprise AI Prem Natarajan recently spoke to TechTarget about Capital One’s partnership with the National Science Foundation (NSF), which will help launch five new National AI Research Institutes to drive breakthroughs in high-impact areas across society like material and computational sciences, economics, and mental health. https://bit.ly/4grWY3m

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Our own EVP, Chief Scientist & Head of Enterprise AI Prem Natarajan recently spoke to TechTarget about Capital One’s partnership with the National Science Foundation (NSF), which will help launch five new National AI Research Institutes to drive breakthroughs in high-impact areas across society like material and computational sciences, economics, and mental health. https://bit.ly/46fLI5I

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𝐃𝐚𝐭𝐚 𝐂𝐨𝐧𝐭𝐫𝐚𝐜𝐭𝐬: Dashboard shows wrong numbers. Pipeline failed again. Why? Someone upstream changed a column name without telling consumers. We're all running on handshake agreements about our data until those agreements break at the worst possible time. 𝐃𝐚𝐭𝐚 𝐜𝐨𝐧𝐭𝐫𝐚𝐜𝐭𝐬 𝐚𝐫𝐞 𝐭𝐡𝐞 𝐟𝐢𝐱. They're like API contracts but for datasets. You explicitly define what you expect: - Schema and data types - Business definitions - Quality expectations - When data arrives and how fresh it should be 𝐖𝐡𝐲 𝐭𝐡𝐢𝐬 𝐦𝐚𝐭𝐭𝐞𝐫𝐬 𝐧𝐨𝐰: Teams own different data products (data mesh is real). AI models break when data drifts. Tools like dbt and Soda finally make this stuff practical. 𝐇𝐨𝐰 𝐭𝐨 𝐬𝐭𝐚𝐫𝐭: Pick one important dataset. Write down the rules. Set up alerts when rules break. Expand from there. It won't stop change from happening, but it makes change predictable instead of surprising. Full guide on my Substack: https://lnkd.in/gPHPxF9m #DataEngineering #DataQuality #DataOps #DataStrategy

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Deepanker Venkatesh and I recently authored a perspective on the ISDA Common Domain Model (CDM), a foundational development shaping the future of capital markets. ISDA CDM is more than a standard. It represents: • Industry-wide consistency in how data and workflows are defined and executed. • Operational efficiency and risk reduction through streamlined post-trade processes. • Strategic readiness for regulatory alignment and cross-market interoperability. For market participants, the opportunity lies in moving from awareness to action. Engaging early with CDM will better position organizations to capture efficiencies, enable innovation, and strengthen competitive advantage. I welcome thoughts on how CDM will influence the operating model of the industry in the years ahead. Nageswar Cherukupalli, Meena Athinathan, Edward R. Merchant, Anke Bivens, Cary Ellexson, Sathish Ranganathan, Sachin Khatri, Vikesh Gupta, Andreas Pilleris

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Never a normal day at Northeastern University. Attended an #AI panel today that fundamentally shifted how I think about the future of work. Connie Yowell from Northeastern University moderated a discussion that went deeper than the usual AI hype. Peter McCrory from Anthropic, Alexandra (Ally) Legend Siegel from Salesforce, and Rob English from The Work weren't just talking about theoretical futures ... they were discussing transformations happening right now in their organizations. What hit different wasn't any single prediction. It was the collective acknowledgment that we're all, whether building #Agentic AI models, implementing #Enterprise solutions, or reimagining talent acquisition, customer returns, navigating uncharted territory together. The room was full of students about to graduate into a job market that looks nothing like it did even two years ago. The questions weren't about if #AI will impact careers, but whether we'll be architects of that change or just adapt to it. Peter McCrory mentioned something about #AI capabilities expanding faster than our frameworks for understanding them. Alexandra (Ally) Legend Siegel discussed enterprise transformation with #AI at scales that reshape entire industries. Rob English challenged assumptions about what talent even means in an #AI-first world. As someone working at the intersection of #AI and #Business, these perspectives from different vantage points revealed blind spots I didn't know I had. Left that room with more questions than answers. Which might be exactly the point. The gap between companies discussing AI transformation and those actually doing it? That's where the opportunity lives. What fundamental assumptions about your industry are you ready to question? #AI #FutureOfWork #ArtificialIntelligence #Innovation #TechPanel #HigherEducation #CareerDevelopment #AITransformation #TechLeadership #WorkforceDevelopment #NortheasternUniversity #EdTech #EnterpriseTech #TalentAcquisition #DigitalTransformation Ekaterina Karniouchina Aleks Gollu D'Amore-McKim School of Business at Northeastern University Northeastern University, Oakland - Career Connections & Experiential Learning Dan Sachs Nexacore Solutions

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🚀 Just wrapped up a hands-on project applying Retrieval-Augmented Generation (RAG) to a real-world use case: interpreting LIC’s New Jeevan Shanti policy document using LLMs. The core idea? Combine vector search over regulatory PDFs with GPT-4 to generate grounded, faithful answers to complex policy questions. Key capabilities we built: ✅ LCEL-based RAG architecture using LangChain ✅ Contextual compression retriever for focused document lookup ✅ Modular LLM & embedding selection (OpenAI, HuggingFace, Mistral) ✅ Evaluation using LLM-powered metrics: faithfulness, relevance, correctness 📄 The document used is publicly available here: LIC New Jeevan Shanti Policy PDF 💻 Full code available on GitHub: https://lnkd.in/gS9mEzWA 💡 This architecture brings the best of both worlds — retrieval accuracy and generative flexibility — especially in regulated domains like insurance and financial services. 🔜 Next step: Incorporate Agents that can reason across multiple steps — not just answer a question, but decide which tools to use, when to search, how to validate, and when to stop. That’s where the future of enterprise GenAI lies. Would love to hear how others are pushing RAG, agents, and LLMs in production! #GenAI #LangChain #LLM #RAG #AIForOps #InsuranceTech #LLMEvaluation #LangChainAgents

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When you have the best of different disciplines sitting together to solve a complex problem - how do you get them to have discourse if they all only talk in their own jargon? Specialist can provide the right answers... if you ask the right questions. This is where the Expert Generalist comes in. This skillset is particularly important in some roles like Enterprise Architects where one need to cross multiple dimensions of specialisation. On one axis, spanning the various technical disciplines such as cloud, cyber, application, storage, network, compute, etc. and on another across the organisation capabilities, i.e. sales, product, operations, finance, risk, etc.

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