Nitin Singhal

I just read “Enterprise AI’s Reality Check” by Kapil Venkatachalam at FTV Capital, and it's a breath of fresh air amidst the AI hype. Kapil cuts right to it: amid billions in infrastructure bets and LLM growth, the real opportunities lie deeper—at the application layer, where AI directly powers business value. From personalized assistants adding real revenue at companies like Toast, Taco Bell, and Bank of America, to startups like Orbus Software, Windward, Arden Insurance, and Kore.ai, AI is starting to deliver beyond pilot illusions.. If you're focused on bridging the gap between AI experimentation and business outcomes, this is a must-read! #aiforreal #AIAdoption #AIAdoption #AIApplications #DataGovernance #AIInfrastructure #AIAtScale https://lnkd.in/ggzyBexW

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Led by Chidambaram Chidambaram, Senior Customer Architect at Elastic, the session Implementing Agentic RAG using Elasticsearch and LangChain is now available to watch on demand. It covers building agentic retrieval augmented generation (RAG) systems that retrieve, reason, and support contextual decision-making in real-world scenarios. Watch on-demand: https://gag.gl/rqR9Om

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We’re learning a lot at Snowflake, very quickly, about what it actually takes to deliver high quality, high-performance agentic analytics in the real world. Rajhans Samdani and I collaborated to write this latest post, which captures some of those learnings, especially around the emerging “context layer” and why it is becoming foundational for trustworthy data agents. https://lnkd.in/g-8TMcmD What’s been most interesting is how consistently the same themes show up across customer engagements. Whether it’s Semantic Views, Cortex Analyst, our Agent Framework, or Snowflake Intelligence, the same pattern keeps reinforcing itself. You can’t get to reliable, scalable outcomes without a strong, well-defined layer of context that connects data, business meaning, and user intent. This is one of those moments where the pace of change is hard to overstate. The space is evolving quickly, and we’re seeing ideas move from early experimentation into real customer impact in a matter of weeks. Being able to take what we’re learning directly from customers, feed that back into the platform, and then see it improve results is what makes this work so exciting right now. If you’re thinking about how to build or scale agentic analytics, I hope you’ll find this perspective useful.

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People hear “AI governance” and immediately imagine committees, PDFs, and 4-week review cycles. You don’t need any of that. You just need traceability. Every agent action in your system should log: - what it did - why it did it - the inputs - the outputs - and any validation checks It gives you accountability without slowing the workflow to a crawl. You shouldn’t need a meeting to explain how an agent made a decision. It should be obvious in the logs. Governance shouldn’t feel like friction. It should feel like clarity.

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Are Your Data Leaders Thinking… or Just Connecting? Here’s an uncomfortable truth. Many data teams spend most of their time not analyzing… not predicting… not innovating… But connecting systems. Fixing flows. Maintaining pipelines. That’s not a talent problem. That’s an architecture problem. When ingestion, governance, and analytics live in different tools, Your smartest engineers end up stitching instead of strategizing. That ratio is broken. Modern platforms like Microsoft Fabric shift the balance: • Built-in ingestion • Built-in governance • Built-in analytics Less system stitching. More business thinking. Because scaling insight shouldn’t require scaling headcount. 📩 datafabric@quadranttechnologies.com Write to us for a quick walkthrough or POC discussion. And if you’re heading to FabCon Atlanta (March 18–20), let’s connect in person; see you there. Microsoft | Quadrant Technologies | Ram Paluri, MBA | Vamshi Reddy | Bhaskar Gangipamula | James Kass | Prakash Nagarajan | Lavina DSilva | Mithun P N | Dr. Madhavi Gundavajyala | Siva Kanuru | Sivani Pamidi | Varsha Panguluri | Ajay kumar Erukulla | #Microsoft #DataStrategy #MicrosoftFabric #CIO #CDO #ModernData #FabCon

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Lately I’ve been jumping between a few AI and data-intelligence projects at Snowflake, and one thing keeps hitting me over and over again: the hardest part of AI has very little to do with the model itself. Models are cool. They look great in slides and polished demos. But the second you plug them into real workflows, real products, and real customers… things get interesting really fast. Here are a few things I keep running into: Data looks clean until the moment you actually rely on it Every team has its own idea of how something “should work” A perfect solution on paper turns into something completely different in production Integrations sound easy, right up until you start doing them for real And my personal favorite: “We’ll fix that later,” which usually translates to “we’ll rebuild this twice” What’s funny is that these challenges barely change from company to company. Whether it was Snowflake, ServiceNow, Cisco, or even earlier in my research days, the pattern is pretty much identical: AI only becomes useful when teams communicate well and work through the messy parts together. It’s not glamorous. It’s not magic. It’s just alignment, transparency, and adjusting when reality doesn’t match the plan. And honestly, that’s what I like about this field. The tech is fun, but the real progress comes from people actually building things together. Just wanted to jot this down before I forget. Curious what others are seeing in your teams lately. For you, what’s been harder: the model or the people part? By the way, here’s a video that showed up in my feed and fits this topic pretty well 👇 https://lnkd.in/g3wTuzb6

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🚀 Strategies for Safer Production Releases Release day doesn’t have to be stressful. In my latest Substack article, I outline proven strategies for deploying software safely and confidently in production. These include: ✅ Progressive rollouts with feature flags ✅ Canary releases and controlled exposure ✅ Blue/green deployments for zero-downtime rollback ✅ Runtime configuration and kill‑switch capabilities These risk mitigation tactics let engineering teams innovate faster while keeping outages and errors minimal — key for quality, speed, and customer trust. 🎯 Read the full article: https://lnkd.in/g5YiHBz6 #DevOps #CI_CD #SoftwareDelivery #FeatureFlags #CanaryRelease #BlueGreenDeployments #EngineeringBestPractices #LearnDataWithSeckin #Data

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