#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
In this episode of Data & AI Exchange (DAX) by Unolabs, we ask the big question: Can AI be trusted in healthcare, pharma, and beyond?Our guest, Dr. Thibault Géoui — a leading AI voice in biotech, an AI consultant, and host of the Tech & Drugs podcast — breaks down the challenges of building trust in AI: from drug discovery to diagnostics and even robotic surgery.What You’ll Learn: - Why “trust in AI” goes beyond accuracy and fairness - The hidden risks of bias in pharma and life sciences data - How AI is already cutting drug R&D timelines from 6 years to 8 months - Real examples where AI outperforms humans in diagnostics - Why benchmarks don’t always capture AI performance - Will we ever trust AI enough for autonomous surgeries or initial diagnostics?Whether you’re an AI enthusiast, healthcare professional, or just curious about the future of technology, this conversation will give you real-world insights into where AI delivers, where it fails, and why trust is the ultimate barrier.Don’t forget to subscribe for more discussions on AI, trust, and the future of technology.00:00 – Can AI replace doctors in the next 10 years?00:29 – Guest intro: Dr. Thibault Géoui, biotech & AI leader01:12 – Why pharma is slow to adopt new technologies02:26 – The gap: 24,000 diseases vs. only 3,000–4,000 drugs03:33 – Drug development costs ($2–6B, 10–12 years) & 95% failure rate04:47 – Why pharma is excited about AI as a new tool05:50 – Trust in AI: accuracy, fairness, reliability, or full lifecycle?07:40 – Pharma’s AI adoption “purgatory” explained09:01 – Why current AI benchmarks don’t tell the full story11:44 – Can bias in AI models ever be fully eliminated?12:53 – Pharma’s own bias: drugs developed for a “typical” patient14:40 – Biological diversity in drug effectiveness (genetics, enzymes, etc.)16:26 – Data sourcing challenges: pharma sitting on tons of unusable data18:07 – Historical vs. new data: accessibility and usability19:17 – Vinod Khosla’s prediction: AI watch for early diagnostics20:15 – AI in cancer imaging: from manual to automated precision22:11 – Specific AI use cases outperforming humans (chess, Go, diagnostics)23:01 – Cutting drug R&D timelines: from 6 years to 8 months24:40 – Preparing oversight mechanisms for AGI-level AI25:16 – Lessons from self-driving cars & autopilot in planes26:20 – Robotic arms in surgery: augmentation vs. full automation27:55 – Sci-fi to reality: Clarke’s novel vs. today’s surgical robots29:07 – AI-assisted vs. autonomous surgery — what’s realistic?29:48 – Closing thoughts: AI’s future in healthcare & pharma#CanAIBeTrusted #ArtificialIntelligence #AIinHealthcare #AItrust #PharmaInnovation #DrugDiscovery #AIMedicine
#agenticai #manager #podcastIs AI Replacing Middle Managers – or Making Them 10x More Powerful?In this power-packed episode of Data & AI Exchange, we dive deep into the evolving role of middle managers in an AI-first corporate world. Are they being replaced? Or are they becoming the most valuable link between humans and machines?Featuring Sandeep Sudarshan CTO at Capgemini UK, with 30+ years of global experience in digital transformation across six continents, this conversation explores:
Recommended Book: "Generative AI for Managers – by Capgemini & Harvard Business Review"Whether you’re a manager, tech lead, or aspiring leader, this episode gives you a clear roadmap to thrive with AI, not fear it.00:00 – Is AI replacing middle managers or making them 10x better?00:38 – Guest intro: Sepran, CTO at Capgemini UK01:03 – Role of middle managers in an AI-first world01:56 – Co-Pilot vs. Co-Thinker AI usage03:00 – What leadership expects from managers now04:44 – Tools like Copilot & Gemini transforming workflows06:58 – Does AI reduce brainstorming time?08:05 – Shift from FTE to outcome-based models09:55 – Leading AI change with empathy & communication11:08 – Is AI competency affecting promotions?12:24 – Overuse of ChatGPT in evaluations13:50 – Importance of filtering information15:10 – Prompt engineering & agent-building for managers17:34 – AI in sales forecasting & telecom profitability20:04 – Changing performance metrics with AI21:55 – Risks of not upskilling in AI23:05 – Managers as a bridge between AI tools & strategy24:20 – Key skills for future-ready managers26:05 – Layoffs, effort estimation & AI’s role27:38 – Book rec: Generative AI for Managers28:00 – Final wrap-up & thanks#AI #MiddleManagement #DigitalTransformation #AILeadership #Capgemini #GenerativeAI #Productivity #DataDriven #PromptEngineering #Copilot #WorkplaceAI #AIForManagers #LeadershipDevelopment #FutureOfWork #AIUpskillingAI, Middle Management, Digital Transformation, AI Leadership, Capgemini, Generative AI, Productivity, Data Driven, Prompt Engineering, Copilot, Workplace AI, AI for Managers, Leadership Development, Future of Work, AI Upskilling, AI Tools for Managers, AI Strategy, AI in Business, ChatGPT for Work, Enterprise AI
#AI with Tony Moroney, Principal at The Digital Explorer & Global AI StrategistAs AI systems evolve into agentic, decision-making entities, the stakes for responsible governance have never been higher. In this episode of Data & AI Exchange, we speak with Tony Moroney, one of EMEA’s top AI and digital disruption thought leaders, about the urgent need for enterprise-grade AI governance frameworks — before regulation races to catch up.What You’ll Learn:- Why most organizations still lack internal AI governance, despite enterprise deployment- What makes agentic AI different — and dangerous — if left unchecked- How to integrate explainability, traceability, and oversight into AI system design- Why global rulebooks may fail without local culture and sovereignty alignment- What a board-level AI policy should include before it's too late#AICompliance #AILeadership #AgenticAI #ResponsibleAI #TonyMoroney #AIGovernance #EnterpriseAI #AIOrdinate #DataAndAIExchange #AIInTheWorkplace #SwarmAI #AIFrameworks
#ai #agenticai #employeeIn today’s fast-changing workplace, one thing is clear: AI isn’t going to replace you—but someone using AI will.In this episode of the Data & AI Exchange podcast, we dive deep into this idea with Saptarshi Nath (fondly called “Saps”), the CEO of Airboxr — a platform that’s redefining how teams work with data and automation.Saps has helped over 40 countries integrate no-code AI workflows, and in this episode, he shares what it truly means to be an AI-enabled employee in 2025, how companies are adapting, and how you can stay relevant as AI reshapes the job landscape.What You'll Learn in This Episode:- What is an AI-enabled professional — and why the definition matters now more than ever.- Why prompt writing is the #1 skill for anyone using AI tools today.- How AI adoption is more about mindset and behavior than just tools.- Real-world examples of how AI is boosting productivity in areas like research, marketing, and content creation.- Why hiring trends are shifting — and how AI proficiency is becoming more important than traditional experience.- Whether you're a marketer, designer, analyst, or business leader, this episode will give you a clear, honest, and practical view of the AI-powered future of work.Tools & Platforms Mentioned:- ChatGPT – General research and ideation- Perplexity – Fact-based AI research assistant- Claude – Preferred by developers for coding tasks- Midjourney & Google Gemini – For visual content generation- N8N – A no-code automation platform with AI integration- Airboxr – AI agent for e-commerce data insights- Replicate.com – Try cutting-edge AI models for sound, video, and more- Notebook LM – Research notebook powered by LLMs00:00 – AI Won’t Replace You, But a Person Using AI Will00:20 – Guest: Saptarshi Nath (Saps), CEO of Airboxr00:39 – Who is an AI-Enabled Employee?01:51 – Misconceptions About AI and LLMs03:25 – Knowing When to Use and Not Use AI04:36 – The “ChatGPT Must Be Right” Problem05:51 – AI as a Work Partner, Not a Competitor06:45 – Why Prompt Quality Matters07:45 – Too Many AI Tools: Where to Begin?08:19 – Getting Started with AI: GPT, Perplexity, Claude09:28 – 3 Tasks to Stop Doing Manually in 202511:01 – Creatives & AI: A Collaborative Future13:05 – Real-World Examples of Productivity Boost with AI14:45 – AI Tools to Try: Replicate, ChatGPT, Perplexity15:56 – AI Agents: Overhyped or Useful?19:23 – From Task Execution to Prompt Thinking20:53 – The Divide Between AI Adopters and Non-Adopters22:52 – How AI is Changing the Hiring Process26:07 – 3 Must-Know AI Tools for Everyone28:02 – Tool Preferences Vary by Role & Use Case29:47 – How to Improve Your Prompt Engineering Skills32:13 – The Ideal AI-Enabled Employee of the Future34:17 – Final Summary and Closing Thoughts#AIEnabledEmployee #FutureOfWork #GenerativeAI #ChatGPT #PromptEngineering #Airboxr #AIProductivity #AIInHiring #NoCodeTools #AIResearch #DataAndAIExchange
#AI #VibeCoding #CTOInsights #banking #agenticai
Can AI write your production code — just from a vague prompt?
And more importantly... who’s accountable when it fails? In this episode of Data & AI Exchange by Uno Labs, we sit down with Gajanan Namjoshi, Ex-Global CTO of Core Banking at #HSBC, to explore one of the hottest — and most controversial — trends in enterprise tech: #Vibe-Coding — where developers (and even non-engineers) generate working code through AI prompts instead of traditional coding.
We ask the hard questions: -
Is vibe-coding helping or hurting compliance?
- What governance is needed for AI-generated code in banking?
- Should non-engineers be allowed to deploy AI-generated code?
- How do you ensure accountability, security, and skill integrity in this new world?
Episode Breakdown -
00:00 - People have stopped Coding
01:24 - Prompts can be very Vague
03:28 - Regulate Vibe-coding in Banking
04:35 - Has the AI used Coding Standards?
06:15 - How to ensure code is compliant?
07:48 - Leading Vibe-coding Teams
08:38 - Non-engineers deploying code?
10:03 - Domain Experience is important
12:07 - AI in Credit Risk Analysis
13:38 - AI in Fraud Detection
14:34 - Governance model for vibe-coding
16:20 - AI Vibe-coding in Agile Project
17:35 - AI development on two tracks
19:06 - Vibe-coding should be controlled
20:20 - Responsibility & Accountability
22:21 - Is POC cycle becoming longer with AI?
23:52 - Garbage in Garbage Out with AI
24:24 - AI Vibe-coding a Nightmare for a CTO?
25:58 - Skillset for Career in Core Banking
27:31 - Should know the Functionality
28:46 - Best phase of IT journey?
30:09 - Manual Testing has its own advantages
32:11 - Summary of Podcast session.
Whether you’re a CTO, engineer, product head, or AI enthusiast, this episode will help you rethink how code, compliance, and leadership work in the AI era.
Watch now – and decide if Vibe-Coding is the future of enterprise tech or a disaster waiting to happen.
#AgenticAI #AICoding #BankingTech #UnoLabs #DataAndAIExchange #PromptEngineering #TechPodcast #EnterpriseAI #CTOLeadership #AICompliance #GajananNamjoshi #HSBC
#AIAgents #AgenticAI #aiagents
Can one person now do the work of five — thanks to AI agents? Welcome to a new era of productivity where developers, engineers, and analysts are becoming AI-powered solo operators inside global tech companies.Understanding AI Agents doesn't require a technical background. The AI evolution has been from basic LLMs like #ChatGPT to AI Workflows and finally to true #AI Agents. Today we discuss practical, real-world examples & use-cases.Learning the key differences between AI technologies and discover how concepts like RAG and ReAct actually work is a required for regular AI users who want to understand how these emerging technologies will impact their daily lives.In this episode of Data & AI Exchange by Unolabs, we sit down with Anirban Nandi, Head of AI at Rakuten India, to explore the growing trend of One-Person Teams with AI Agents — and why 2025 might be the breakout year for agentic AI in enterprise environments.Key Themes you'll learn:- Agentic AI- One-person teams- AI in software development- Future of work in tech- CTO decision-making in AI adoption00:00 - All the AI Hype in last Year00:53 - Are AI Agents here to Stay?02:15 - Different types of AI Agents03:34 - Budgeting for AI Agent Infrastructure04:38 - What actually is an AI Agent?05:30 - AI agents different from RPA?06:43 - AI Agents Use cases in IT Industry07:55 - Accuracy level of AI Agents?09:05 - Business Impact of AI Agentic Products10:18 - One-Employee Teams with AI Agents11:19 - Junior Developer uses AI Agents12:38 - Capability of one-person has Multiplied13:40 - How Code Quality is Maintained?15:15 - New Roles emerging from Agentic AI16:51 - Agent Curation will be a new Role?18:30 - Is this a Year of AI Agents?20:01 - Practical Use Cases of AI Agents21:16 - Practical Efficiency of AI Agents22:11 - Would you Hire an AI Agent?23:26 - Example of one-person team with AI Agents24:40 - How do you set the Accountability?26:03 - Skillset of Agentic AI Employee?27:29 - Thanks to Anirban Nandi for insightsSubscribe for more episodes from Data & AI Exchange, where we decode the practical impact of AI in modern business and tech leadership.AI agents, agentic AI, one person team AI, solo operator AI, AI productivity tools, enterprise AI, AI in tech companies, 2025 tech trends, AI developer tools, AI in software engineering, future of work AI, data and AI exchange, Uno Labs, Anirban Nandi, AI podcast 2025, AI powered employees, one person startup, AI agents in IT, RPA vs AI agents, AI infrastructure, tech podcast India, global tech trends 2025, AI CTO insights, automation in tech teams, AI skillsets for developers#AI #AIAgents #AgenticAI #OnePersonTeam #FutureOfWork #TechLeadership #SoloOperator #EnterpriseAI #DataAndAIExchange #UnoLabs #podcast
The Agentic AI Future: Transforming Work and Organizations
Agentic AI represents a rapid evolution beyond current generative AI, creating autonomous agents that execute tasks, apply judgment, and learn independently. This transformation is reshaping both human roles and organizational structures.
Human Role Evolution:Work will shift as AI becomes a "parallel workforce" alongside humans. AI agents will automate end-to-end processes while augmenting human capabilities, making work "better, faster, more efficient." Rather than just replacing jobs, companies may use AI as "digital labor" to boost productivity while maintaining headcount.
Essential human skills will focus on empathy, emotional intelligence, and fostering connections—areas where the "human touch" remains crucial. New roles like prompt engineers and content specialists will emerge to teach, train, and tune AI agents. Organizations will need large-scale reskilling programs to repurpose workers whose tasks become automated.
A key challenge is earning employee trust. While newer workers embrace AI's ease of use, tenured employees may resist, fearing replacement or distrusting the "AI black box." Clear leadership mandates and role modeling are essential for adoption.
Organizational Structure Changes:Traditional org charts will evolve to include both human employees and AI agents, creating truly hybrid workforces. Some companies already measure capacity in both full-time employees (FTEs) and agent deployments. "Zero-FTE departments" may emerge where agents handle entire functions with humans in oversight roles.
The focus shifts from "org charts" to "work charts" emphasizing tasks over hierarchy. Business functions will define AI capacity needs, IT will develop agentic capabilities (potentially becoming "HR for AI agents"), and HR will lead change management, reskilling, and ensuring AI aligns with company values.
Leaders must evaluate joint human-AI performance and design flexible operating models to adapt to rapidly changing AI technology.
Despite security and usability challenges, widespread adoption is expected within 18-24 months, promising unprecedented personalization at scale and freeing humans for more fulfilling, creative work emphasizing human connection and empathy.
