
Hosted by Daniel Faggella · EN

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. Emerj attracts VP+ enterprise audiences who are already convinced that they need to move *beyond* traditional IT. To learn the exact strategies we use to help leading AI brands and startups connect with their ideal enterprise AI buyers, visit: emerj.com/AD1

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. 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

Significant enterprise investment in AI-driven customer service is producing inconsistent outcomes — and the gap between deployment ambition and measurable business value remains striking. In this episode, Shri Nandan, VP of AI Products and Experiences at Comcast, examines why organizational culture and readiness are the primary determinant of whether AI in CX delivers results that move the needle. The conversation covers how to define resolution in an agentic AI environment, how context transforms the role of human agents, and why a conservative, staged rollout reduces the risk of large-scale failure. This episode is sponsored by Dialpad. In this episode, we cover how to move from AI proof-of-concepts in customer service to deployments that consistently improve business outcomes. To go deeper on this topic and learn how consultants are winning business with evidence-based AI ROI and building long-term capabilities instead of chasing short-term gains, download our free PDF report, "3 Keys to Thriving in the Coming Era of Automation" at emerj.com/cok1

Retailers managing pricing, marketing, and inventory through separate teams with separate data are losing margin not to market volatility, but to decisions that were never designed to work together. In this episode, Felix Hoffmann, CEO at 7Learnings, examines how predictive, unified commercial decision-making replaces reactive, rules-based approaches — and why most retailers underestimate how much revenue they leave on the table by optimizing each function in isolation. The conversation covers how AI-driven demand simulation enables coordinated pricing, marketing, and reordering decisions, and which commercial use cases enterprise leaders should prioritize first to prove ROI before scaling. This episode is sponsored by 7Learnings. If you offer AI products or services into the enterprise, you need to find enterprise leaders with relevance, and readiness. Emerj attracts VP+ enterprise audiences who are already convinced that they need to move beyond traditional IT. To learn the exact strategies we use to help leading AI brands and startups connect with their ideal enterprise AI buyers, visit: https://go.emerj.com/partner

Enterprise software costs are rising while vendor performance often isn't, and AI has fundamentally changed what enterprises can credibly threaten to build in-house. In this episode, David Cost, Chief Digital Officer at Rainbow Apparel, explores how enterprise leaders can restructure vendor contracts to maintain exit leverage, eliminate auto-renewal traps, and use AI-enabled build alternatives as a legitimate negotiating tool. The conversation examines the cost-benefit calculus of build versus buy in the AI era, red flags in service-level agreements, and how to negotiate exits from underperforming contracts. This episode is sponsored by UpperEdge. Learn how brands work with Emerj and other Emerj Media options at go.emerj.com/partner

Finance teams are being asked to influence outcomes in real time while operating on architectures built for delayed, aggregated, and heavily reconciled data. In this episode, Alex Curran, CEO at Aptitude Software, examines how finance functions can move toward real‑time, event‑level visibility and discusses this shift with host Dan Faggella. She highlights the practical changes required for CFOs — from capturing every financial event at the transaction level to enabling continuous reconciliation and full lineage — so finance can surface exceptions immediately and support decisions as they unfold. This episode is sponsored by Aptitude Software. 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/fcs1

As enterprises move agentic AI from controlled pilots into production customer-facing workflows, the gaps in data continuity, governance, and human-agent coordination become the deciding factors in whether AI scales or stalls. In this episode, Shri Nandan, VP of AI Experiences at Comcast, examines why customer experience has become the real stress-test for enterprise AI — and what it takes to scale with customer trust intact. The conversation covers the three data foundations required for context continuity in production, practical principles for human-AI orchestration, and why cross-team governance — a single North Star across CX, IT, and operations — is what separates the organizations that scale from those that fragment. This episode is sponsored by NiCE. 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