Agentic Lakehouse for Financial Services

Financial services institutions face a paradox: they have more data than ever, and less ability to use it than their AI ambitions require. Fragmented systems, regulatory constraints, costly pipelines, and proprietary platforms have created a data architecture that is expensive to run and slow to change, right as the pressure to deploy AI at scale is accelerating.

In this session, financial services SME Joe Rodriguez outlines how leading banks and insurers are cutting through this complexity with an agentic lakehouse approach. By unifying data across core banking, risk, compliance, and customer systems without moving it, embedding governance at the data layer rather than bolting it on, and deploying AI agents that can find, query, and act on trusted data in real time, institutions are finally making meaningful progress on AI at scale.

You will walk away with:

A map of the five highest-priority data and AI use cases in financial services today, from real-time fraud detection to customer 360 to portfolio analytics, and what separates institutions that are executing from those still stuck in pilot mode
A clear framework for evaluating your current architecture against the demands of agentic AI: unified data, governed access, business context, and hybrid deployment
An honest view of the competitive landscape, including Snowflake, Databricks, and internal builds, and where each approach breaks down for regulated FinServ workloads
Practical next steps for moving from fragmented data infrastructure to a governed lakehouse foundation, starting with one use case

Who should attend: IT and data leaders across financial services, including Operations IT, Risk IT, Compliance IT, Data Engineering, and CDO organizations at banks, capital markets firms, wealth managers, and insurers who are responsible for analytics, AI, or data platform programs.

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