#ArtificialIntelligence #FutureOfAI #DeepTech #SpaceTech #satellites

What if AI didn’t just power chatbots — but ran satellites, triaged disaster data in orbit, and one day hosted whole data-centres in space? Vishesh Vatsal (CTO, SkyServe) breaks it down.In this episode of Insider Opinion, Vishesh Vatsal — CTO at SkyServe, co-founder of Dfy Graviti, and an IIT Kanpur aerospace engineer — walks us through the near-term and moonshot futures of AI in space: onboard compute, satellite constellations, real-time disaster response, and the wild but plausible idea of data-centres in orbit. We also dig into hiring for deep tech, trust and governance, and practical use cases India should prioritize.Key takeaways- Onboard AI is already feasible — edge GPUs and compressed models make meaningful inference in orbit possible today.- Bandwidth + cost = need for local intelligence — send insights (kilobytes), not raw imagery (gigabytes).- Constellations change the game — frequency, spectrum and resolution improve, but orchestration & collision risk require smart autonomy.- High-value use case: disaster response — faster detection and targeted intervention can save lives.- Moonshot: moving some compute to space (solar-powered data centres) is logically possible and likely within our lifetimes.- Hiring for the AI future: mix of fundamentals + curiosity + at least one AI-skeptic/devil’s advocate on the team.- Governance warning: accelerate safeguards and alignment investment alongside capability development.00:00 — Will AI run satellites in 10 years?02:57 — Coders vs dreamers: hiring for deep tech06:12 — AI in space: from rovers to autonomy09:37 — Disaster response from orbit13:01 — Why space still fears autonomy15:56 — Constellations: frequency, spectrum & resolution20:55 — Moonshot: data centres in space 🚀24:59 — Avoiding collisions: AI & Kessler Syndrome30:06 — India’s role in the global AI race33:39 — Beyond space: AI for Bangalore traffic36:27 — Do you trust AI more than humans?38:40 — Future headline 2040: AI cures all diseases#EdgeComputing #DataCentres #DisasterResponse #UrbanTech #Constellations #RemoteSensing

#cloud #strategy #data In today’s episode, we dive deep into Data Strategy in the Age of AI with Dr. Shweta Darbha — a seasoned tech leader who has driven digital transformation, cloud adoption, and API-first solutions at some of the world’s top financial institutions.From cloud migration challenges to breaking data silos with APIs, Shweta explains how enterprises can move beyond buzzwords and build smarter, secure, and AI-ready organizations. We unpack common mistakes in cloud adoption, how compliance can coexist with innovation, and why APIs are now the backbone of modern fintech.If you’ve ever wondered how to get real business value out of AI, data, and cloud strategies, this episode will give you clarity and practical insights.What you’ll learn in this podcast:- Why a real data strategy is the backbone of AI- The most common mistakes companies make in cloud adoption- How to innovate while staying compliant in financial services- API-first explained in simple, non-technical terms- Breaking down data silos with cloud + API-first design- What separates winners from laggards in data strategy over the next 5 years00:30 — Episode intro & topic setup 01:23 — Q1: What does a real data strategy look like in the AI era? 03:01 — Data is the backbone of AI, not just a buzzword 04:49 — Defining data strategy: what is AI trying to achieve? 06:05 — Q2: Common mistakes when moving data to the cloud 07:23 — Challenge 1: Lack of clear cloud policy 08:32 — Challenge 2: Teams not cloud-ready, learning on the job 10:41 — Deep-dive: Deciding what transactional data to push to cloud (UPI example) 12:31 — Q3: How can organizations innovate while staying compliant in the cloud? 14:36 — Topic: Data abstraction & built-in security 16:11 — Q4: Why APIs are the backbone of modern digital services 19:03 — Q5: Breaking data silos with cloud + API-first 23:20 — Final Q: Looking ahead 5 years — winners vs laggards 28:36 — Closing & thanks#DataStrategy #AI #CloudAdoption #APIFirst #Fintech #BankingTechnology #DigitalTransformation #APIs #ArtificialIntelligence #CloudComputing #FintechInnovation #DataDriven #TechPodcast #FutureOfBanking #AIinFintechdata strategy in the age of AI, cloud adoption in banking, api first solutions explained, fintech data strategy, ai in financial services, digital transformation in banking, how to break data silos, ai and cloud computing, api integration in fintech, data governance and AI, future of fintech 2025, banking technology trends, enterprise AI strategy, cloud migration mistakes, ai powered digital banking

#agenticai #manager #podcast #salesforceEvery boardroom is asking the same question today: “Are we really ready for AI?”In this episode of Data & AI Exchange, we sit down with Hitesh Seth — Chief Architect at Salesforce and a veteran of large-scale Data & AI platforms — to explore what true AI readiness means beyond hype and pilot projects.From financial services to healthcare, Hitesh has helped global enterprises design AI roadmaps, integrate data platforms, and build production-grade AI systems. Here, he shares how leaders can assess data maturity, governance, culture, and talent before scaling AI.What You’ll Learn: - Early signals of an AI-ready organization - How to separate AI theater from real business value - Why strong data foundations & governance are critical - Balancing quick wins vs. long-term AI bets - The non-negotiable roles & teams needed for success - How to measure ROI, fail fast, and pivot effectively - Why a growth mindset is key for leaders in the AI era00:00 – Opening & Introductions01:30 – Early Signals of AI Readiness03:00 – Digital Transformation as a Readiness Indicator04:30 – Data Journeys & Maturity Levels05:40 – AI Theater vs. Real Business Value07:30 – Predictive vs. Generative AI in Enterprises08:30 – What a Solid Data Foundation Looks Like10:30 – Governance & Data Ownership11:40 – Red Flags in Data Quality & Governance13:20 – Quick Wins vs. Strategic Bets15:00 – Talent, Leadership & Change Management16:00 – Before Approving AI Pilots: Tech, APIs & Security18:00 – Efficiency vs. Growth: The Business Value Test19:40 – Building Teams: Upskilling vs. Hiring Externally21:00 – Why Architects Are Critical in AI Readiness22:30 – Non-Negotiable Roles: Data Engineers, MLOps & Governance24:00 – Measuring ROI & Knowing When to Pivot25:30 – Fail Fast, Learn Faster: Lessons from Pilots26:30 – Growth Mindset & Continuous Reinvention27:10 – Closing Thoughts & Takeaways