"GenAI is fundamentally different from traditional AI, and that distinction has massive implications for your data pipeline. Does Generative AI require a completely new data strategy? In this essential discussion, Dan Kasun, ISV Partner Sales Leader for Global Financial Services at Amazon Web Services (AWS), answers the crucial question about data infrastructure. He breaks down the difference between traditional AI, which relies on curated, structured data, and GenAI, which thrives on all data, including unstructured documents. Dan explains why Retrieval-Augmented Generation (RAG) architecture is the non-negotiable data layer for GenAI deployments in finance. Learn how to leverage your existing data practices—like governance and access control—to set your firm up for success in both predictive and generative AI without starting from scratch. CHAPTERS: 0:00 - Intro: Get Your Data Pipeline Ready for AI 0:26 - Does GenAI Require a Different Data Pipeline? (Short Answer: Yes) 0:38 - Traditional AI: Highly Structured Data and ETL 1:06 - GenAI: Dealing with Diversity and All Data 1:29 - Why RAG (Retrieval-Augmented Generation) is Essential for GenAI 1:52 - The Jump: Why Speeding into GenAI Too Fast is a Risk 2:18 - The Foundational Requirement: A Solid Data Architecture 2:46 - The Key to Success: Universal Access and Governance 3:45 - Wrap-Up Dan Kasun - ISV Partner Sales Leader, Global Financial Services, AWS Adam Green - Technology Industry Expert, Moderator Presented by Cloudera in partnership with AWS. Watch the next video in the series: GenAI vs. Traditional AI: The Data Financial Services Firms Need to Supply [Link to Video 4] Learn how to build, run, and scale secure enterprise AI—across on premises and cloud—with full control over your data, models, and infrastructure: https://shorturl.at/5Z01O #GenAI #DataPipeline #FinancialServices #RAGArchitecture #DataArchitecture #Cloudera #AWS #TraditionalAI #DataGovernance #FinTech"

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