Advertise on podcast: The AI in Business Podcast

Service organizations hold most of their critical expertise in experienced technicians rather than manuals, and that knowledge is leaving as the workforce turns over. In this episode, Thomas Cottereau, CEO and Founder at SightCall, explores why the real challenge is capturing expertise rather than producing more documentation, and how AI can structure what experts already know into trusted knowledge. He outlines how to capture knowledge inside everyday service work, keep human experts reviewing what AI produces, and treat that knowledge as a continuous loop that feeds both technicians and existing AI systems. ​ This episode is sponsored by SightCall ​ To learn how leading AI brands like NVIDIA and Google Cloud work with Emerj to reach Fortune 500 AI buyers - download our media kit at: emerj.com/AD1

Enterprise investment in AI is rising faster than the ability to prove what it returns, leaving technology and vendor spend largely unexamined until leadership asks whether it is working. In this episode, Kellen Winters, Director of Customer Success and Business Operations for AI at Novartis, examines why demonstrating AI value starts with building a measurement discipline before any tools are chosen. The conversation covers anchoring AI priorities in the processes where work stalls or drains resources, narrowing a long list of use cases to the few that can be measured, tying pilots to existing KPIs within 90 days, and using a structured intake process to track value from the first idea onward.   To learn how leading brands and AI startups connect with enterprise AI buyer audiences at scale, download our media kit at emerj.com/AD1

A job title no longer tells you what a technical role actually requires, now that AI has changed how the work gets done. In this episode, Carrol Chang, CEO of Andela, unpacks how AI is reshaping technical roles, and why hiring and assessment need to shift from job titles to skills.   The conversation covers how leaders can assess for judgment and problem-solving rather than just technical output, and how to map a workforce for reskilling as roles continue to change.   This episode is sponsored by Andela.   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

Shifting regulations and siloed risk and compliance functions are forcing enterprise leaders to rethink audit-driven compliance. In this episode, Craig Unger, CEO and Founder at Hyperproof, explains why compliance should be treated as a continuous state, with every control tied back to the organization's overall risk profile. He also covers standardizing a single set of controls across frameworks and regions, expressing risk in financial terms for boards, and using a five-stage maturity model to plan next steps.   This episode is sponsored by Hyperproof   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

A recommendation that's right 80% of the time sounds fine, until it's steering machinery next to a person. On the factory floor, that margin is the difference between routine and catastrophic. In this episode, Jeff Witt, Global Process Digital Authority at GE Vernova, examines why industrial AI demands a different standard of rigor than its enterprise counterpart, from edge computing architectures to context-rich recommendations frontline workers can trust immediately. He also outlines a framework for earning the right to automate, proving a process is documented, optimized, and consistent before scaling AI across multiple sites.   This episode is sponsored by Poka.   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/aiv1 ​

Financial institutions are moving fast on generative AI without a clear answer for who owns, and who is liable for, the content it produces and consumes. In this episode, Stefan Zima, Head of Group Regulatory Compliance and AI Compliance at Raiffeisen Bank International AG, examines how banks can build real accountability and visibility into their AI content practices before regulators or auditors force the issue. The conversation covers cross-border data governance, third-party AI risk, and where research reports, market commentary, and training materials create the most exposure. ​ Learn how leading AI brands like NVIDIA and Google Cloud work with Emerj to reach Fortune 500 AI buyers - download our media kit at: emerj.com/AD1

Alert volume inside IT operations has outpaced manual triage, leaving real incidents buried in noise. In this episode, Dhrubojyoti Das Deb, SVP of Global IT at JPMorgan Chase & Co., explores how AI-driven correlation and intelligent grouping can turn thousands of alerts into a handful of actionable incidents. The conversation covers where accountability sits when AI assists in triage, how teams build trust in automation incrementally, and where leaders should start when moving from reactive firefighting to intelligent operations.   This episode is sponsored by BigPanda.   Learn how to build high-converting landing pages and use self-qualification systems to identify and prioritize high-intent leads. Download our free B2B AI Lead Generation Guide at emerj.com/aig1

Enterprise CX teams have poured budget into AI, yet many still can't show measurable returns — frontline staff double-check the tools, and each new bot becomes another disconnected silo. ​ In this episode, John McMullan, Head of Product Marketing and Strategy at Observe.AI, joins host Yolandi de Weerdt to examine why AI adopted without operational discipline falls short, and how a shared interaction intelligence layer can carry context across AI agents, core systems, and human handoffs. ​ McMullan outlines the practical shifts: putting frontline experts, analysts, and engineers on the build team, starting with one small workflow and learning from early failures, testing agents against simulations of real conversations, and getting data clean and connected before scaling.   This episode is sponsored by Observe.AI.   To learn how to spot real AI trends in any sector, download our free report, "3 Ways to Discover AI Trends in Any Sector," at emerj.com/ait1

A single misstep in a new customer service channel can undo months of trust-building work.   In this episode, Paul Cuglietta, Customer Services Director at Simplyhealth, explores how disciplined testing, governance, and stakeholder sign-off shape agentic AI rollouts inside a regulated, high-trust environment. The conversation covers matching automation to the way customers speak across channels, building a long-term AI vision tied to company purpose, and getting buy-in through comparable case studies. This episode is sponsored by NiCE Cognigy.   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

An AI agent can function as designed yet produce results an organization cannot verify or safely use. In this episode, Melissa Ramey, Director, AI Transformation – Data Insights and User Experience at Salesforce, joins Emerj's Yolandi de Weerdt to examine who owns agent behavior after deployment and why correcting individual outputs does not fix the underlying workflow. She explains how source evidence, shared definitions, and clear decision boundaries help teams govern agents before expanding their autonomy. To go deeper on AI strategy, including evidence-based AI ROI and building long-term capabilities, download Emerj's free PDF report, "3 Keys to Thriving in the Coming Era of Automation," at emerj.com/cok1

Drug discovery teams are running short of new ideas to test, and AI models that simulate how cells respond to drugs only pay off once leaders know when those predictions can be trusted with experiments, timelines, and budget.   In this episode, Daniel Veres, Co-Founder and Chief Scientific Officer at Turbine, and Giorgio Gaglia, Head of Systems and Disease Biology at Sanofi, examine with host Yolandi de Weerdt how testing models on data they have never seen shows which R&D decisions their predictions can reliably inform.   The discussion offers R&D leaders a practical way to judge when a model's predictions are ready to inform which experiments to run, where in the pipeline to start, and how much risk to accept.   This episode is sponsored by Turbine.   Learn how these conversations drive pipeline for other AI brands — download our media kit at emerj.com/AD1

In biopharma R&D, AI investment has overwhelmingly targeted generating more drug candidates, even though the industry has rarely lacked for compounds worth pursuing. In this episode, Mukhtar Ahmed, Director of Project and Portfolio Strategy at Greenstone Biosciences, explores why the harder and more valuable problem is deciding which candidates deserve continued capital and attention. The conversation covers synthesizing fragmented evidence across teams, keeping human judgment and accountability in the loop, and measuring AI's impact across a portfolio rather than a single program. ​ Emerj works with a select group of AI vendors to reach Fortune 500 decision-makers through research, media, and direct access. If you want to be considered, download our media kit at emerj.com/AD1

Finance teams face immense pressure to operate faster, but rushing to automate over fragmented systems only compounds errors downstream. Ciprian Porutiu, SVP of Strategic Initiatives at Marsh, joins host Yolandi de Weerdt to explain why establishing a common language and federated data domains matters far more than forcing everything into a single centralized database. He shares practical approaches for managing phased rollouts, involving cross-functional users early in system design, and connecting underlying data governance directly to strategic business performance. ​ This episode is sponsored by Fynapse. 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

For large banks, the barrier to AI at scale is rarely the model. It's decades of fragmented systems, inherited through mergers, acquisitions, and new products, where each team trusts its own data and no single view of context exists. Agentic AI doesn't fix that fragmentation; it amplifies it across every user and every prompt. ​ In this episode, Nathaniel Bell, Data Management Director at Wells Fargo, joins host Yolandi de Weerdt,  to examine why the real challenge for regulated institutions is scaling trust while automating, why leaders should judge new technology on the business outcome it can prove rather than the demo, and why no process should be automated before it's standardized. ​ Bell outlines the practical shifts underway: from manual attestations and spreadsheets toward automated logs that show what happened in a workflow, from governance as document review toward governance built into workflows as code, and from buying new tools toward changing how teams work once they have them. ​ According to Nielsen, 91% of podcast listening happens alone, indicating deep, undistracted attention — ideal for complex B2B messaging. ​ To learn how leading brands and AI startups connect with enterprise AI buyer audiences at scale, download our media kit at emerj.com/AD1

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