AI in Financial Services Podcast

A growing share of AI‑for‑CX guidance assumes a generic enterprise environment, but this episode surfaces the deeper operational friction that emerges when automation meets high‑stakes, highly regulated customer interactions.
 
In this episode, Shri Nandan, VP of AI Products and Experiences at Comcast, examines how regulatory constraints, fragmented data ownership, and emotionally or legally sensitive workflows reshape what AI can realistically deliver in healthcare and financial services alongside host Yolandi de Weerdt.
 
She highlights the practical shifts required for leaders to scale AI safely and effectively — from establishing tight governance and unified data foundations to designing agent systems that know when to escalate and when to automate.
 
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AI is breaking down the traditional separation between technology leadership and people leadership, forcing enterprises to rethink how work is structured and how employees adopt new systems.
 
In this episode, Guillaume Lardeux, Head of Transformation and Office of the CEO at Genesys, and Eva Majercsik, Chief People Officer at Genesys, examine how AI-driven workflow redesign requires shared ownership across functions, as discussed with host Marilie Fouché.
 
They highlight the practical shifts involved — mapping current processes, identifying friction points, rebuilding workflows where humans and AI operate together, and supporting adoption through cultural alignment and iterative experimentation.
 
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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 Caldwell, 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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