The AI in Business Podcast

A widening gap between mature digital compute and the newly awakening physical world is forcing enterprises to rethink how they embed AI into logistics, manufacturing, and other high‑stakes environments where errors carry real operational risk.


In this episode, Drew Henry, Executive Vice President for Physical AI at Arm, joins host Daniel Faggella and examines how leaders are navigating the shift from fixed automation to model‑driven intelligent control, and what it takes to make confident, high‑impact infrastructure decisions amid rapid algorithmic and hardware change.


The discussion highlights how advanced teams ground adoption in concrete operational problems, build competency around new model‑based interfaces, and use simulation and digital twins to de‑risk retooling in power‑ and compute‑constrained environments.


If you want to listen to the same things that other infrastructure and AI leaders in the Fortune 500 are tuned into, then check out the AI infrastructure podcast, it's emerj.com/inf1

A surge in AI adoption is creating a rights gap inside financial institutions, where everyday workflows now generate copyrighted reproductions at a scale existing governance models were never built to manage. In this episode, Roanie Levy, Licensing and Legal Advisor at CCC, joins host Yolandi de Weerdt and examines how AI‑driven content use is outpacing traditional licensing frameworks and why leaders must verify rights before embedding copyrighted material into AI systems. The discussion highlights the operational decisions executives need to make around content governance, rights validation, and cross‑functional controls to prevent downstream legal and workflow disruption. This episode is sponsored by CCC. If you offer AI products or services into the enterprise, you need to find enterprise leaders with relevance and readiness.
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The rapid expansion of AI in financial services is creating a widening gap between enterprise ambition and the operational readiness required to deploy systems that are secure, compliant, and trusted. In this episode, Dr. Oscar A. Rodriguez, Vice President of Data Analytics at Citi, joins Daniel Faggella, Emerj CEO and Head of Research, to describe how leaders build the operating model for safe AI at scale, from aligning stakeholders to embedding governance, accountability, and data quality from the start. The discussion highlights practical decisions around cross‑functional alignment, foundation‑first governance, risk ownership, and preparing for evolving regulatory and security demands. This episode is sponsored by Securiti AI. Download the free "AI in Financial Services Executive Cheat Sheet" at emerj.com/fcs1 to go deeper on how early governance prevents AI failures.

Supply chains are moving from predictable planning cycles to a reality where volatility demands continuous redesign and faster decision‑making. In this episode, Dr. Gopalendu Pal, Director of Operations at Target, and Prasad Mahajan, Senior Director of Customer Engagement at Optilogic, examine how leaders can adapt by tightening the gap between sensing disruption and adjusting operations, as Emerj's Daniel Faggella guides the discussion toward the implications for enterprise decision speed. They outline the practical shifts required — reassessing outdated constraints, strengthening data foundations, and using scenario analysis and human‑guided AI to evaluate operational options with greater accuracy and responsiveness. This episode is sponsored by Optilogic. Learn how brands work with Emerj and other Emerj Media options at go.emerj.com/partner

Enterprise AI initiatives consistently break down in document-heavy environments, not because the underlying models are inadequate, but because fragmented data silos, page-break context loss, and uncoordinated extraction tools erode the semantic layer AI needs to reason accurately. In this episode, Sumedh Chaudhary, CTO US Industry Market at IBM, breaks down why a multi-agent architecture is the operational prerequisite for AI to function reliably in regulated, document-intensive workflows. The conversation covers how governance frameworks with measurable error-rate targets distinguish pilot success from production failure, and how enterprises can structure a phased AI approach that blends automation, fit-for-purpose models, and human oversight.
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This episode is sponsored by Arango. In this episode, we cover how enterprises can build multi-agent AI architectures to handle document-heavy workflows — and the governance frameworks that determine whether those deployments scale. To go deeper on this topic and learn how to structure landing pages for higher conversion, and how to use self-qualification systems to prioritize high-intent leads, download our free PDF report, "B2B AI Lead Generation Guide," at emerj.com/aig1