AI in Financial Services Podcast

AI programs in financial services don't fail at the model; they fail at the seam between pilot and production, where data, integration discipline, and ownership decide whether anything reaches scale. In this episode, Jeremy Caine, Technology Strategy and Solution Leader at IBM, unpacks why banks and insurers get stuck in pilot purgatory and what an AI-native future state actually looks like in a regulated environment. The conversation covers a data product strategy that surfaces the data that matters for the use case, the industrialized software delivery lifecycle required to move AI into production, a platform-led architecture built on open foundations and automation, and the operating model shifts senior leaders need to make to convert AI investment into durable business capability.
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Stabilizing the operational environment around underwriting judgment is the shift that enables decisions to move into the market with greater speed, consistency, and control. In this episode, Barbara Stacer, Vice President, Head of Small Commercial Underwriting and Underwriting Operations at Utica National Insurance Group, examines how governed versioning, traceable approvals, and embedded documentation close the execution gap that slows pricing changes after they leave actuarial. She outlines the practical steps leaders can take to reduce queue time, strengthen auditability, and ensure pricing updates reach production when they matter most. Learn how brands work with Emerj and other Emerj Media options at https://go.emerj.com/partner1

AI infrastructure projects are failing not because the technology underdelivers, but because organizations commit capital before establishing the business case. In this episode, Juan Orlandini, CTO at Insight, examines why enterprise AI programs stall and outlines how leaders can apply decades of established IT investment discipline to evaluate, validate, and scale AI initiatives with measurable outcomes in mind. The conversation covers organizational change management across employee adoption segments, the application of FinOps principles to AI infrastructure spending, and a minimum viable proof of concept approach for validating AI investments before scaling.
 
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Architectural decision‑making in large enterprises can break down when system knowledge is fragmented, slowing delivery and creating inconsistent outcomes across teams. In this episode, Amar Akshat, Senior Vice President of Architecture at PaySafe, examines how codified organizational memory and deterministic guardrails enable intelligent systems to accelerate development without sacrificing control. He highlights the shift toward machine‑readable decision records, intent‑driven interfaces, and standardized design patterns that help enterprises reduce drift, strengthen compliance, and move faster with greater confidence.
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Regulated industries are facing mounting pressure to adopt AI while maintaining absolute accuracy, data protection, and clear human accountability. In this episode, Steve Hasker, CEO at Thomson Reuters, joins Emerj's Matthew DeMello to examine how these demands are reshaping expectations for model reliability, data isolation, and the future of high‑stakes regulatory work. The discussion highlights the need for transparent guardrails, expert‑trained systems, and new approaches to handling large‑scale filings without compromising oversight.
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