AI in Financial Services Podcast

Enterprises rushing agentic AI into production are running it through approval gates, batch windows, and audit systems built for human speed — and the gap is where most operational risk lives. In this episode, Chris Cadwell, President and CEO at Concentrix Corporation, examines how machine-scale transactions break processes designed for human pace and why bounded digital delegates outperform unrestricted digital twins in the enterprise. The discussion covers compliance bots that check other bots, the cost reality of poorly tuned agentic agents, and what leaders need to stop doing if they want a defensible AI roadmap. Learn how to evaluate AI vendors by assessing leadership expertise, and why funding benchmarks can signal product maturity and stability, download our free PDF report, "5 Ways to Select the Right AI Vendor," at emerj.com/aiv2.

A fundamental shift is emerging as AI moves core financial operations away from traditional web interfaces and into AI‑native cloud‑desktop environments, redefining how CFO teams execute and manage workflows. In this episode, Henry Ward, founder and CEO at Carta, examines how this transition changes the CFO operating model alongside host Daniel Faggella, highlighting why AI‑literate finance leaders gain leverage by orchestrating reporting, close processes, and analysis directly through agent‑driven systems. The discussion outlines how reusable AI skills, shared operational source files, and automated coordination reshape month‑end close, reporting agility, and cross‑team execution for modern finance organizations. To go deeper on this topic and learn how financial institutions are digitizing paper-based records to unlock usable data for AI, and using alternative data like public web and social signals to enhance risk assessment, download our free PDF report, "AI in Financial Services Executive Cheat Sheet" at emerj.com/fcs2

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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