Explore the podcast → https://ibm.biz/BdbuSB Can agentic AI solve the scalability challenge in wealth management while expanding access to underserved communities? Ramona Ortega believes it can, but only with the right foundation. She explains how WealthBuild has moved from basic chatbots to RAG and now to agentic workflows that can deliver accurate and personalized guidance at scale. In her opinion, the real blockers aren’t algorithms but messy data, governance gaps and enterprise-grade security. Since traditional financial services can’t reach many everyday consumers, she partners with community banks to close the gap. For her, the future lies where AI, APIs and blockchain meet, opening the door to accessible and democratized financial advice. 0:00 - Intro 5:34 - A vision for serving the underserved 7:54 - The pivot from generative to RAG to agentic 11:54 - What agentic actually means 13:12 - Choosing technology partners 16:23 - Enterprise data and multi-tenant AI are major challenges 20:09 - Keeping individual agents simple within workflows 24:17 - From customer service to customer engagement 28:17 - Serving underserved community institutions 32:45 - Direct-to-consumer financial advice and agent creation 38:39 - Building startups that solve real problems AI news is moving fast. Keep your business ahead with updates about AI advancements, strategies and expert perspectives → https://ibm.biz/BdbuMt The opinions expressed in this podcast are solely those of the participants and do not necessarily reflect the views of IBM or any other organization or entity. #enterpriseai #aistrategy #digitaltransformation #agenticai #businessinnovation #financialservices #aiworkflows #fintech

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