Pramod Rawat on Generative AI, Agentic AI & BFSI Transformation скачать в хорошем качестве

Pramod Rawat on Generative AI, Agentic AI & BFSI Transformation

Most organizations today are experimenting with AI. But very few are able to build scalable, enterprise-grade AI products — especially in highly regulated environments like banking. In this episode of the Dawdle.live Leaders Podcast, we host Pramod Rawat, an AI and Product Strategy leader with deep expertise in generative AI, agentic AI, machine learning, and enterprise transformation. With leadership experience at global organizations, he shares what it really takes to move from AI experimentation to production-ready platforms. 🔍 What You’ll Learn in This Episode: • The difference between AI experimentation and enterprise AI products • Why guardrails, governance, and compliance are non-negotiable • How to design scalable AI platforms in regulated industries • The role of RAG, vector databases, and AutoML in enterprise AI • Real-world AI use cases in banking (fraud detection, contact centers, risk) • How agentic AI breaks down complex workflows into autonomous systems • Where human-in-the-loop is still critical • The importance of PII protection and data governance • Centralized vs decentralized AI operating models • How AI changes product management vs traditional software • Why time-to-market and ROI alignment matter more than ever • The future of AI in enterprise systems over the next 5–10 years 💡 Key Insight from the Episode “AI becomes valuable only when it is governed, scalable, and aligned to business outcomes.” Building AI is not just about models — it’s about: • Architecture • Compliance • Cost vs ROI • Accuracy & bias control • Organizational alignment 🧠 Real Enterprise Use Case Pramod shares a powerful example of agentic AI in banking: Instead of a single system handling customer queries: • One AI agent validates identity (fraud prevention) • Another retrieves financial data • Another handles product queries • Another schedules actions This creates a modular, scalable AI system that improves efficiency while maintaining control. ⚠️ Critical Reality Check AI in enterprise is NOT plug-and-play. Organizations must solve for: • Hallucination risks • Bias and model drift • Data privacy (PII protection) • Regulatory compliance • Monitoring and governance Without these, AI cannot go into production. ⏱ Chapters 00:00 – Introduction to Pramod Rawat 01:19 – AI experimentation vs enterprise AI products 03:00 – Why guardrails are critical in AI systems 05:00 – Scaling AI platforms in regulated environments 07:24 – AI, search, and platform monopolies 10:26 – Innovation trends in AI (OpenAI, Anthropic, global ecosystem) 11:14 – Real AI use cases in banking 14:00 – Agentic AI explained with contact center example 16:29 – Where human-in-the-loop is essential 18:30 – AI disruption and skill evolution 24:00 – AI tools, productivity, and real-world usage 26:14 – Governance in LLMs, RAG, and AutoML 28:36 – Centralized vs distributed AI models 32:49 – Enterprise AI operating models explained 35:45 – Product management in AI vs traditional systems 41:13 – Future of AI in enterprise (5–10 years outlook) 44:39 – Final thoughts 🎯 Who Should Watch This • CXOs and enterprise leaders • AI/ML and data leaders • Product managers and tech strategists • Banking, fintech, and regulated industry professionals • Anyone building AI products at scale #ArtificialIntelligence #GenerativeAI #AgenticAI #EnterpriseAI #DigitalTransformation #AIinBanking #ProductManagement #FutureOfWork #Leadership #DawdleLive #Dawdle