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Daniel Faggella

The AI in Business Podcast is for non-technical business leaders who need to find AI opportunities, align AI capabilities with strategy, and deliver ROI.

Each week, Emerj research staff and journalists interview top AI executives from Fortune 2000 firms and unicorn startups - uncovering trends, use-cases, and best practices for practical AI adoption.

Visit our advertising page to learn more about reaching our executive audience of Fortune 2000 AI adopters: https://emerj.com/advertise

The AI in Business Podcast

For consumer goods companies, the gap between the commercial plan agreed at head office and what gets executed on the shelf is one of the costliest disconnects in the business — investment goes out against activity that never happens, and revenue never arrives against activity that does. ​ In this episode, Stephanie Lilley, VP of Transformation for Europe at Reckitt, examines why rigid rep call cycles and fragmented store-level data keep that gap open, and where AI is starting to close it — from image recognition that verifies in-store compliance to dynamic routing that sends field teams to the stores with the highest return — with host Daniel Faggella, Emerj CEO and Head of Research. ​ She outlines the practical shifts underway: from fixed call files toward a mix of core relationship stores and flexible, ROI-driven visits, from full-time field coverage toward crowdsourced compliance checks, and from geography as the deciding factor toward the quality and recency of store-level data. ​ Learn how these conversations drive pipeline for AI brands — download our media kit at emerj.com/AD1

Closing the Gap Between Commercial Plans and What Happens on the Shelf - with Stephanie Lilley of Reckitt

Traditional, event-based training can't keep pace with technical skills that now shift every six to twelve months. ​ In this episode, Nicola Lyons, Senior Director of Talent Operations at Andela, breaks down why assessment-based, personalized learning is replacing one-time training programs. ​ The conversation covers how leaders can build continuous learning loops that keep technical teams current without turning into another mandatory course. ​ This episode is sponsored by Andela. ​ Reach VP+ leaders at America's largest enterprises through Emerj's podcasts. Emerj reaches 1 million listeners annually. Learn how AI brands use Emerj to generate leads: emerj.com/AD1

Why Traditional Training Can't Keep Technical Teams Current - with Nicola Lyons of Andela

Generative AI is entering clinical workflows just as healthcare institutions grapple with black-box outputs, unclear regulation, and the need for provable accuracy. In this episode, Rhett Alden, CTO at Elsevier, joins Emerj's Yolandi de Weerdt to articulate how claim-level validation and transparent sourcing are becoming prerequisites for clinical trust in AI. The conversation covers emerging AI governance boards modeled on clinical trial review structures, the limits of current US regulation, and why final clinical responsibility remains with the physician.
 
 Learn the strategies Emerj uses to help leading AI brands and startups connect with their ideal enterprise AI buyers: emerj.com/AD1

Responsible Generative AI in Healthcare and What Leaders Need to Know for 2026 - with Rhett Alden of Elsevier Health

For AI systems built into vehicles, robots, and industrial equipment, being smart isn't enough — the difference between success and failure often comes down to whether real-time performance was designed in from the start or added on afterward. In this episode, Drew Barbier, Vice President, IP Business Unit at MIPS, breaks down why starting with the software workload, rather than the hardware, produces measurably better outcomes in real-time compute design. The conversation covers a real-world example of a three-to-five-times performance gain from workload-first design, what changes on a team's side when real-time becomes a starting requirement instead of an afterthought, and how leaders can tell within six months whether their approach is working.
 
 This episode is sponsored by MIPS.
 
 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

Building AI Into Systems That Can't Afford to Be Late - with Drew Barbier of MIPS

Enterprise sales teams routinely let the majority of inbound leads go untouched simply because there isn't enough team bandwidth to work them all. In this episode, Vanessa Tabbert, VP of Agentic Transformation and Sales Development at Salesforce, breaks down how her own team deployed an AI SDR agent to recover leads that would otherwise go cold, without replacing the humans who convert the best ones. The conversation covers how to identify a low-risk first use case, why an AI agent should be coached and measured like a team member rather than launched and left alone, and how the same approach scales down to a small SMB sales team.
 
 This episode is sponsored by Salesforce. ​ 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

AI-Powered Revenue Operations: The Future of Sales for SMBs - with Vanessa Tabbert of Salesforce

Large B2B operations keep running order entry, invoicing, and other repetitive back-office workflows by hand, years after most companies started investing heavily in AI. In this episode, Chris Bradley, Chief Marketing Officer and Head of AI Transformation at Veritiv, breaks down why legacy systems have kept agentic AI out of these workflows, and what changed once Veritiv began automating a process handling 1.7 million orders a year. The conversation covers the audit-trail controls needed to trust an agent with an end-to-end task, how a supervising AI agent checks another agent's work, and how to pick a first automation target based on headcount concentration rather than project size. ​ 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

Where Agentic AI Pays Off in the B2B Back Office - with Chris Bradley of Veritiv

Industrial environments run on a different set of constraints than the typical enterprise office — physical risk, standalone factory sites, and workflows where a fragmented handoff can cost time, quality, or worse. ​ In this episode, Scot Burdette, Global CIO of Measurement & Analytics at ABB, examines why industrial AI has to be built around knowledge capture, human-in-the-loop oversight, and frontline usability, and shares how ABB's own tools are helping a retiring generation's expertise transfer to the workers replacing them, with Emerj host Marilie Fouché. ​ He outlines the practical shift underway, from siloed, tribal knowledge toward a shared operational data layer, and from pilots that prove a concept to the design and implementation work that changes how a factory runs. ​ This episode is sponsored by Poka. 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 Real‑World Conditions That Shape Industrial AI - with Scot Burdette of ABB

Job titles routinely undercount what employees can actually do, especially in fast-moving technical roles. In this episode, Cory Hymel, Head of Research at Andela, explores why mapping skills instead of roles gives leaders a far more accurate picture of workforce capability and AI readiness. The conversation covers how continuous skills assessment, rather than one-time training, keeps pace with a technical skill half-life that's now down to two or three years. This episode is sponsored by Andela. Learn how select AI vendors reach Fortune 500 decision-makers through Emerj's research and media platforms. Download our media kit at emerj.com/AD1

Closing the Skills Intelligence Gap in the AI Era - with Cory Hymel of Andela

A quarter of enterprise teams have gotten a single AI channel into full production — the rest are stuck somewhere behind it. In this episode, Arun Chandra, Chief Operating Officer at NiCE, explores why that gap persists and what closes it. The conversation covers getting data, knowledge, and organizational context ready for scale, building the financial case for AI investment with the CFO, and how open protocols like MCP are reshaping enterprise architecture decisions. This episode is sponsored by NiCE Cognigy. Learn how leading organizations approach AI investment more like a venture portfolio, and why interdisciplinary collaboration is critical to defining the right data for AI success. Download our free PDF report, "Beginning with AI," at emerj.com/aik1

Scaling Agentic Automation With Open Architecture - with Arun Chandra of NICE

The service environment is straining under rising equipment complexity and a rapidly shrinking technician workforce, making patient‑critical uptime harder to guarantee. In this episode, Michael Goldman, Senior Director of Business Operations and Transformation at Patterson Dental, examines how AI can preserve service continuity by capturing institutional knowledge, supporting technician workflows, and enabling earlier, more accurate equipment diagnostics — in conversation with host Yolandi de Weerdt. Learn how leading brands and AI startups connect with enterprise AI buyer audiences at scale, download our media kit at emerj.com/AD1

AI for Patient Critical Uptime in a Shrinking Technician Workforce - with Michael Goldman

This week, we interview Jay Budzik, CTO at ZestFinance, about where AI applies to the world of auto-lending. We speak with Budzik about how underwriting and credit scoring is evolving as a result of advances in machine learning.
 
In addition, we talk about how companies might solve the "black box" of machine learning in finance, particularly how ZestFinance is focusing on transparent models. The financial sector has to contend with complex regulations that prevent certain information from being leveraged in credit models. It can be near impossible to determine how machine learning comes to the conclusions it does, but ZestFinance claims their software in part solves this problem.

How Lenders Can Win More Business with Machine Learning

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