The AI in Business Podcast

A persistent gap remains between what modern manufacturing technology makes possible and how day‑to‑day work is still executed, with paper workflows, limited machine visibility, and siloed data preventing real operational control.   In this episode, Sebastian Dykas, Director of Manufacturing, Engineering, and Maintenance at Smith+Nephew, examines with host Marilie Fouché how tighter data capture and connected systems can move leaders toward real‑time process control and reduced variability. The discussion highlights practical shifts in measurement, machine connectivity, and automation that help teams stabilize output and build more reliable, digitally enabled operations. ​ Learn how leading organizations define the right data — and why cross‑functional collaboration is essential to getting it right. Download our free PDF report, "Beginning with AI," at emerj.com/aik1  

Experienced field engineers are retiring or moving on faster than ever, and the troubleshooting knowledge they carry rarely makes it into a manual before they go.   In this episode, Deniz Mullis, Senior Director of Global Technical Operations at Cytiva, examines why closing that knowledge gap takes daily feedback loops, cleaner documentation, and technician trust. Deniz joins Emerj's Yolandi de Weerdt to walk through what breaks first during a rollout, how her team builds confidence in AI-assisted guidance, and what has to be true inside a service organization before an AI investment can pay off.   This episode is sponsored by Aquant.   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

The pace of modern IT operations is collapsing under the volume of alerts and the slow speed at which teams can contextualize data during active incidents.   In this episode, Luke Rotta, Director of Site Reliability Engineering at Charles Schwab, examines how fragmented tooling, slow triage, and human‑driven workflows create persistent drag on response speed in conversation with host Yolandi de Weerdt, and why AI‑driven pattern recognition and automation meaningfully change that equation.   He highlights how leaders can reduce operational noise, build trust in automation through simulation, and sequence high‑volume workflows to move teams from reactive firefighting toward more intelligent, reliable operations.   This episode is sponsored by BigPanda.   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

Financial institutions are pushing agentic AI past pilot mode and into document-heavy, regulated workflows like AML alerts and account closures, but many leaders still lack a clear model for where automation should run freely and where human judgment has to stay in the loop. In this episode, Yoav Naveh, Co-Founder and Co-CEO at Reindeer AI, examines how banks are structuring agent oversight so automation earns trust incrementally instead of replacing compliance teams outright. The conversation covers how to identify workflows ready for agentic AI, what signals show an agent is learning rather than failing, and how public and historical data can strengthen both compliance decisions and customer retention.   This episode is sponsored by Reindeer AI.   Learn how financial institutions are digitizing paper-based records to unlock usable data for AI, and using alternative data to enhance risk assessment, download our free PDF report, "AI in Financial Services Executive Cheat Sheet" at emerj.com/fcs1

CRM is shifting from a static repository to the core system that supplies the real‑time customer context required for dependable AI‑driven work.   In this episode, Sharif Karmally, VP of SMB Product Marketing at Salesforce, explores with host Daniel Faggella how unified CRM data supports automated lead handling, accurate record updates, and predictive sales and service actions. Leaders will hear practical guidance on system integration, governance for human‑agent workflows, and building a data foundation that scales.   This episode is sponsored by Salesforce.   Learn how leading organizations approach AI investment more like a venture portfolio, download our free PDF report, "Beginning with AI," at emerj.com/aik1

Most enterprise AI pilots don't stall because the underlying models fail — they stall once real production stakes, fragmented systems, and unclear ownership enter the picture. In this episode, Larissa Schneider, Co-Founder & COO at Unframe, unpacks why AI initiatives lose momentum on the way to production and how modular, reusable architecture changes that trajectory. The conversation covers where deployment friction actually originates, how modular AI components cut integration time from months to weeks, and what governance and auditability enterprises need before pushing AI-driven workflows live.   This episode is sponsored by Unframe. Emerj reaches 1,000,000 listeners every year. Learn how leading AI brands partner with Emerj to build credibility and generate pipeline.  Learn more about how executive conversations drive pipeline for other AI brands - download our media kit at emerj.com/AD1  

Infrastructure readiness has become the real bottleneck for agentic AI in healthcare, as enterprises confront the shift from systems that generate content to systems that execute tasks across complex, regulated workflows.   In this episode, Alex Tyrrell, SVP and CTO of Health at Wolters Kluwer, examines how agentic AI changes operational demands for healthcare organizations in conversation with host Matthew DeMello, highlighting the need for domain‑adapted reasoning, granular APIs, and stronger observability as agents drive higher‑volume system interaction.   He underscores the practical implications for leaders: preparing backend systems for agent‑driven load, adapting models to real‑world workflows, and avoiding monolithic architectures that limit safe, scalable deployment.   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  

Mobile app attackers already operate at machine speed, but most enterprise cyber functions still rely on manual, human-paced patch cycles to keep up. In this episode, Tom Tovar, Co-Creator at Appdome, examines how agentic AI is replacing manual vulnerability assessment and forcing cyber teams to become producers of protection rather than evaluators of risk. The conversation covers the shift from application-level security policies to release- and user-level personalization, the data and context needed to run agentic protection pipelines, and the accountability questions that come with agentic decision-making.   This episode is sponsored by Appdome.   Learn how financial institutions are digitizing paper-based records to unlock usable data for AI, and using alternative data to enhance risk assessment. Download our free PDF report, "AI in Financial Services Executive Cheat Sheet" at emerj.com/fcs1

A surge in AI adoption is forcing enterprises to expose sensitive data to new systems and access paths, creating security risks that traditional perimeter models can't contain.   In this episode, Todd Vancil, Vice President of Veeam's Securiti AI Sales Engineering Team, examines how securing the data itself — through classification, labeling, and governed access — becomes the prerequisite for safely deploying AI at scale, in conversation with host Daniel Faggella, Emerj CEO and Head of Research.   This episode is sponsored by Securiti AI . Learn how brands work with Emerj and other Emerj Media options at go.emerj.com/partner  

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

Legacy enterprises are facing a decisive shift from stalled pilots and fragmented data toward agentic systems that reshape customer experience, operations, and net‑new revenue.   Matt Renner, President and Chief Revenue Officer at Google Cloud, examines how leaders can move beyond early AI failures to build modern data foundations, orchestrate heterogeneous agents, and accelerate transformation in conversation with Daniel Faggella, Emerj CEO and Head of Research.   The discussion highlights capability‑driven ROI, structured AI governance, data modernization, agentic orchestration, and the emerging security imperatives shaping enterprise adoption.   To listen to the conversations other infrastructure and AI leaders in the Fortune 500 are tuned into, subscribe to the AI infrastructure podcast at emerj.com/inf1

The rapid expansion of AI‑driven knowledge work is exposing a growing tension: teams are moving faster across more surface areas, but their context is fragmented across tools, personal systems, and ad‑hoc workflows.   In this episode, Tsavo Knott, CEO and co‑founder at Pieces, examines how the shift toward AI‑native individual contributors requires a unified substrate for capturing and retrieving operational context, and how this enables more effective coordination between humans, agents, and cross‑functional teams with host Daniel Faggella, Emerj CEO and Head of Research.   He highlights the practical implications for leaders, from standardizing how context is recorded to enabling context‑router roles that keep decisions and execution aligned at the speed modern AI workflows demand.   This episode is sponsored by Pieces. Learn more about how recipes drive pipeline for other AI brands - download our media kit at emerj.com/AD1

Visual AI gets stuck not because the technology fails, but because enterprises cannot move it from controlled environments into the variability of daily operations. In this episode, Brian Ton, Senior Laboratory Manager at Florida Crystals Corporation, examines why building operational trust in visual AI requires more than a successful pilot — and what actually determines whether a deployment becomes standard practice or stalls. The conversation covers validation and verification frameworks, the role of the feedback loop in sustaining trust, and why starting with small, demonstrable wins is more effective than reaching for enterprise-wide solutions from the outset. This episode is sponsored by Roboflow. Do you sell AI products or services? Emerj gives you access through trusted content and real conversations. 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

A growing reliance on data‑driven systems is creating tension for enterprise leaders between operational efficiency and the human judgment required for high‑risk, high‑reward decisions. In this episode, Kun He, Lead Scientific Advisor at Bayer, examines how AI improves agricultural decision‑making while still leaving critical gaps that only human intuition and risk tolerance can fill, in conversation with host Matthew DeMello. He highlights where AI reliably accelerates complex workflows and where leaders must preserve the capacity to recognize outlier opportunities and make unconventional calls that data alone would never surface. 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

A growing share of enterprise work now depends on systems that can support both human and AI agents, exposing bottlenecks in coordination, governance, and cross‑functional process design. In this episode, Debanjan Saha, Chief Executive Officer at DataRobot, examines how enterprises can rebuild their operational architecture to support digital employees at scale in conversation with host Daniel Faggella, Emerj CEO and Head of Research. He highlights the practical shifts required — from identity and access control to auditability, simulation, and cross‑system orchestration — that enable agents to take on back‑office and cross‑functional work reliably and safely. To hear the full conversation, visit: emerj.com/inf1

Enterprise IT teams are drowning in alert volume as cloud, microservices, and CI/CD pipelines outpace what human operators can process. In this episode, Assaf Resnick, CEO and Founder at BigPanda, examines how agentic AI can shift IT operations from reactive troubleshooting to a prevention-first model built on an enterprise IT knowledge graph. The conversation covers how to build and own that knowledge graph, where human judgment still belongs in the incident response loop, and why an evolutionary rollout beats a full system overhaul. This episode is sponsored by BigPanda. Learn how brands work with Emerj and other Emerj Media options at emerj.com/partner

As financial institutions move beyond AI experimentation, the primary bottleneck is no longer capability; it is governance. In this episode, Yoav Naveh, Co-Founder and Co-CEO at Reindeer AI, examines how enterprises can operationalize agentic AI inside regulated financial workflows, including the two-loop oversight model that keeps agents compliant, self-correcting, and continuously improving. The conversation covers what it means for agents to self-heal when they encounter exceptions, how decision logic should be governed in practice, and how to approach the build-versus-buy decision in a way that places accountability ahead of capability. This episode is sponsored by Reindeer AI. In this episode, we cover how enterprises in banking and financial services are operationalizing agentic AI inside regulated workflows — and what governance infrastructure actually makes it sustainable at scale. To go deeper on this topic and learn how banks are using RPA to reduce operational costs in repetitive workflows and applying anomaly detection to identify fraud in real time, download our free PDF report, "AI in Banking Executive Cheat Sheet," at emerj.com/bcs1

Enterprise AI adoption is moving faster than security and governance frameworks can follow,  forcing organizations to make difficult trade-offs between competitive urgency and operational risk. In this episode, Jason Loomis, CISO at Freshworks, examines why most enterprises have not yet resolved the tension between AI deployment speed and security maturity, and outlines a sequenced approach; beginning with regulatory compliance, advancing through data trust, and extending into AI-specific security frameworks. The conversation covers how leadership can build the investment case for AI, how culture shapes adoption outcomes, and why the biggest executive mistake is expecting returns before committing the resources that make them possible. Connect with ideal enterprise AI through the strategies Emerj employs to help leading AI brands and startups: emerj.com/AD1  

As enterprises move from single to multi-agent AI deployments, a coordination infrastructure problem is emerging that connectivity protocols alone cannot solve. In this episode, Guillaume de Saint Marc, VP of Engineering at Outshift, examines why agents that can communicate still fail to collaborate — and outlines the semantic alignment, shared memory, and authorization architecture required to address it. The discussion covers specific failure modes in production multi-agent systems, the organizational security and observability decisions that determine whether agentic deployments scale, and how open standards protect enterprises from vendor lock-in as the space evolves.   This episode is sponsored by Outshift by Cisco. In this episode, we cover why enterprises scaling multi-agent AI need more than connected agents — they need shared semantic grounding, persistent memory, and fine-grained authorization to enable genuine collaboration. 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

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 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

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

The rising use of general‑purpose models in regulated environments is creating a widening gap between what AI can generate and what fiduciary professionals can safely rely on. In this episode, Steve Hasker, CEO at Thomson Reuters, examines how AI must be trained, validated, and governed to deliver the level of accuracy required in legal, tax, and audit workflows in conversation with host Dan Faggella, Emerj CEO and Head of Research. The discussion highlights the operational demands of vertical AI, the role of expert‑trained agents, and why human oversight remains essential in high‑stakes professional work. 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 

Supply chain organizations still struggle to respond to major disruptions because their core planning systems can't evaluate structural options or network‑level changes at the speed required. In this episode, Joris Wijpkema, Executive Vice President for Solutions and Strategy at Optilogic, joins host Marilie Fouché and examines how a dedicated, high‑compute modeling layer enables teams to run thousands of scenarios in minutes and make faster, better‑aligned decisions. The discussion highlights how leaders can strengthen resilience by unifying data foundations, building trust in modeling before a crisis, and integrating design‑grade optimization directly into planning cycles. This episode is sponsored by Optilogic. Learn how brands work with Emerj and other Emerj Media options at go.emerj.com/partner

Enterprise AI investments frequently succeed at the pilot stage and collapse at scale, not because the technology fails, but because the organizational conditions for adoption were never established. In this episode, Darko Todorovic, CTO at HTEC Group, examines why most AI ROI gaps originate in poor problem definition and inadequate change management, and outlines how senior leaders can build the baselines, KPIs, and organizational readiness needed to measure and sustain real returns. The conversation covers practical guidance on assessing technological and organizational maturity, avoiding POC-to-production pitfalls, and selecting the right AI tools for specific business contexts. This episode is sponsored by HTEC. In this episode we cover how enterprise leaders can measure and prove AI ROI after deployment. To go deeper on this topic and learn how to identify real AI trends by tracking where venture funding is flowing, and by listening to how leading CEOs describe risk and competitive strategy, download our free PDF report, "3 Ways to Discover AI Trends in Any Sector" at emerj.com/ait1

Pharma commercial teams are generating more data than ever, but field intelligence is still arriving too late to change rep behavior before the engagement window closes. In this episode, Damion Nero, Global Head of Statistics at Daiichi Sankyo, joins Emerj editor Yolandi de Weerdt to examine why fragmented data pipelines, not a shortage of data, are the structural root of the gap between commercial insight and field execution. The conversation covers what separates teams that successfully adopt AI from those stuck in the pilot phase, and why starting with routine, high-certainty use cases consistently produces more commercial lift than chasing ambitious automation. This episode is sponsored by ODAIA. 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

Enterprise AI agents fail consistently in production, not because of model limitations, but because they lack a live, temporally aware context layer grounded in the actual current state of the business. In this episode, Ravi Marwaha, Chief Operating Officer & Chief Technology Product Officer at Arango, explores how treating context as infrastructure—rather than a data pipeline problem—enables agents to reason accurately, explain their decisions, and deliver measurable outcomes across customer support, semiconductor engineering, and clinical trial site selection. The discussion covers five practical frameworks for CIOs and chief data officers on building real-time, explainable context layers on top of existing enterprise systems, without ripping and replacing current infrastructure. This episode is sponsored by Arango. To learn how to improve landing page conversion and use self-qualification systems to identify high-intent leads, download Emerj's free PDF report, "B2B AI Lead Generation Guide," at emerj.com/aig2

Enterprise leaders face a growing gap between rapid AI advancement and the fragmented data and processes that limit their ability to operationalize it. In this episode, Guillermo Vazquez, Chief Architect in the Business Transformation Services for SAP America, examines with host Nick Gersch how harmonized data, standardized processes, and clear identification of differentiating workflows form the groundwork for effective AI‑enabled ERP. He highlights the practical sequence for building this foundation so future AI‑driven adaptation becomes seamless rather than disruptive. For AI brands trying to reach senior decision-makers, podcasts are one of the few channels that earn 20+ minutes of focused attention from VP+ leaders. Emerj reaches 1,000,000 listeners annually — see how other AI brands are driving pipeline: emerj.com/AD1

Regulatory volatility, scientific‑grade context requirements, and entrenched legacy processes are creating a level of operational complexity in pharma that makes even high‑value AI initiatives difficult to move from concept to production. In this episode, Art Shectman, CEO at Elephant Ventures, examines with host Marilie Fouché how leaders can cut through that complexity by isolating a single, clearly defined workflow slice and rebuilding it for near‑term, dependable deployment rather than long‑range architectural perfection. The discussion highlights how removing outdated process assumptions, selecting an atomic workflow with organizational alignment, and aiming for a contained operational win enable pharma teams to build momentum and scale AI responsibly in highly regulated environments. 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

Individual AI productivity gains are already here, but they are uneven, and they are not the main event. In this episode, Tim Sears, Chief AI Officer at HTEC, argues that the real transformation in software development will arrive when AI becomes a catalyst for teamwork rather than an enhancer of individual performance. The conversation examines why software development is the clearest available model for how AI will eventually reshape every business function, how the developer role is being elevated from syntax and grunt work toward architecture, security, and client judgment, why the traditional build-versus-buy decision is being replaced by a build-versus-build reality, and what it will mean when perfection in enterprise software becomes the expected standard rather than the exception. For senior leaders trying to move from supporting AI in principle to actually delivering change, Sears offers a direct and practitioner-grounded view of what needs to change in teams, in expectations, and in the way business processes are understood and redesigned. AI is moving fast — new tools, new research, new use cases every week. Emerj synthesizes what matters most, so senior leaders and practitioners can stay ahead without getting buried. Join 85,000+ subscribers and get the most useful AI business insights delivered to your inbox. Visit: http://emerj.com/ad1

The skilled labor crisis in industrial equipment service is not a future problem; it is eroding operational performance now, as retiring engineers take decades of institutional knowledge with them and incoming technicians cannot fill the gap at speed. In this episode, Mike Hughes, Group Service Director at Peak International Group, outlines how service organizations can close the expertise gap through smarter knowledge capture, targeted AI deployment, and a frontline-first approach to modernization. The conversation covers remote diagnostics, first-time fix performance, the realities of working with imperfect data, and the two or three use cases leaders should prioritize before attempting a broader transformation. This episode is sponsored by Aquant. Learn how brands work with Emerj and other Emerj Media options at go.emerj.com/partner

A widening gap has emerged between the speed of AI innovation and the ability of large enterprises to deploy it responsibly, leading many organizations to repeat avoidable mistakes in scaling. In this episode, Shaje Ganny, Author, Guest Lecturer, TEDx Speaker, and Digital Transformation Director at Procter & Gamble, joins Matthew DeMello to examine how leaders can ground AI adoption in clear business value and human-centered operational design. The discussion highlights practical considerations for evaluating AI through its impact on the company, the consumer, and the surrounding workforce community, and the executive education and policy foundations required to move from pilots to reliable enterprise deployment. 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 http://emerj.com/AD1

The rapid shift from seat‑based licensing to hybrid and consumption‑based AI pricing has made technology spend significantly harder for enterprises to predict and control. In this episode, Adam Mansfield, Practice Leader at UpperEdge, examines how these new pricing models create financial exposure for buyers and why clear forecasting, transparency, and leverage are increasingly difficult to secure in negotiations with major vendors, in conversation with host Marilie Fouché. He highlights the practical steps leaders must take now — from auditing current usage and identifying under‑leveraged spend to engaging vendors early and using the broader. This episode is sponsored by UpperEdge. To go deeper into vendor negotiations and learn how to assess AI providers by leadership credibility and funding signals, download our free report, "5 Ways to Select the Right AI Vendor," at emerj.com/aiv3

Enterprise AI initiatives treat design as a finishing step. Carsten Wierwille, Chief Product & Design Officer at HTEC, argues that this is a strategic mistake, and one that explains why so many AI investments produce tools that work technically but fail to change how people actually work. In this episode, Wierwille examines why enterprises keep building AI because they can rather than because they understand the problem, how the shift to AI-assisted ideation has moved the bottleneck from creation to review, and why the answer is not faster shipping but sharper design clarity at the start. The conversation covers the financial advisor as a model for AI force-multiplication, why the MVP framework breaks down for genuinely novel AI experiences, how design now extends to defining the evaluation criteria for AI output, and what Wierwille calls cognitive design, the practice of thinking about how users will perceive, decide, and trust before anyone writes a line of code. This episode is sponsored by HTEC. Learn how brands work with Emerj and other Emerj Media options at go.emerj.com/partner

Computer vision implementations in manufacturing never advance beyond the pilot phase — not because the technology fails, but because deployment is treated as a software problem rather than an operational one. In this episode, Jeff Witt, Digital Transformation Leader at a Fortune 500 global leader in building materials and fiberglass composites, examines the architectural, organizational, and change management decisions that determine whether a vision AI initiative reaches production and scales. The conversation covers how to build a reusable data architecture for vision data, why shifting ownership from IT to business units accelerates deployment, and what a platform mindset — versus a point solution approach — looks like in a multi-site manufacturing environment. This episode is sponsored by Roboflow. Learn how brands work with Emerj and other Emerj Media options at go.emerj.com/partner

The growing use of AI‑driven modeling in clinical development is exposing how limited traditional, single‑study dose selection and patient assessment methods have been for complex oncology programs. In this episode, Shefali Kakar, Global Head of PK Sciences and Oncology at Novartis, examines how deeper data integration across phases enables more precise dose decisions, clearer safety interpretation, and a more consistent understanding of patient variability alongside host Matthew DeMello. She outlines how longitudinal analysis, exposure–response modeling, and covariate evaluation are helping teams reduce unnecessary sub‑studies, tailor dosing for diverse patient groups, and strengthen cross‑functional decision‑making throughout development.  According to Nielsen, 91% of podcast listening happens alone, creating a focused, distraction-light environment well suited for complex B2B messaging. Learn how leading brands and AI startups connect with enterprise AI buyer audiences at scale by downloading our media kit at go.emerj.com/partner

The pressure on financial services AI leaders to show board-level results has intensified — yet the pace of vendor pitches, shifting tooling stacks, and stalled pilots has made action feel riskier than waiting. In this episode, Art Shectman, CEO and Founder at Elephant Ventures, breaks down why the instinct to evaluate everything before building anything is the primary obstacle to production, and what a realistic first step actually looks like inside a regulated enterprise. The conversation covers how to identify the right initial workflow, how to structure a time-boxed sprint toward a minimum viable production deployment, and how to present early AI wins to boards that have stopped trusting strategy decks. This episode is sponsored by Elephant Ventures. Learn how brands work with Emerj and other Emerj Media options at go.emerj.com/partner

The reason enterprise AI programmes stall is not the technology — it is the sequence in which decisions are made before and after the pilot succeeds. In this episode, Ronny Fehling, Chief AI Transformation Officer at HTEC, examines why AI initiatives lose momentum at the production threshold and what organisational conditions determine whether they make it through. The discussion covers production slices, decision gates with kill-switch authority, use case discipline, and why top-down AI mandates tend to reproduce the same failure modes regardless of budget. This episode is sponsored by HTEC. Learn how brands work with Emerj and other Emerj Media options at go.emerj.com/partner

Deepfake voice fraud is not bypassing enterprise security technology, it is beating the workflows agents rely on to make trust decisions in real time. In this episode, Jon-Rav Shende, Global CTO for Data and AI at Thales Group, outlines where enterprise voice channels are most exposed, why identity, urgency, and business action converging in a single call represents the highest risk point, and what a practical four-step response framework looks like for regulated organisations. The discussion covers how to map risky voice journeys, define escalation decision points, build the evidence chains auditors and cyber insurers will require, and deploy AI as a risk signal layer without automating high-risk actions beyond appropriate controls. ​ This episode is sponsored by Modulate. Learn how brands work with Emerj and other Emerj Media options at go.emerj.com/partner

A growing share of pharmaceutical innovation is now constrained not by scientific imagination, but by the infrastructure required to support AI at scale. In this episode of the AI in Business podcast, Thomas Fuchs, Chief AI Officer at Eli Lilly & Company, joins Matthew DeMello to explore how Lilly's new AI supercomputing platform is reshaping scientific discovery and enterprise operations. The conversation examines how large-scale computing enables more advanced models, secure and usable data environments, and faster scientific iteration across the organization. Learn how brands work with Emerj and other Emerj Media options at go.emerj.com/partner

Boards are pushing CIOs to commit to AI strategies built on contracts written for an entirely different era of enterprise software. In this episode, John Belden, Chief of Research and Strategy at UpperEdge, breaks down the six dimensions of uncertainty CIOs now face when weighing major AI and ERP commitments, and explains why the next five years are about flexibility, not productivity. The conversation covers the case for tighter SI accountability around adaptability, the practical role of contractually-protected optionality, and the difference between performance theater and the kind of continuous learning that keeps a transformation honest. This episode is sponsored by UpperEdge. Learn how brands work with Emerj and other Emerj Media options at go.emerj.com/partner

Context defines accurate, reliable AI decision‑making, forcing enterprises to confront the fragmentation that prevents systems from accessing the information those decisions depend on. In this episode, Ravi Marwaha, Chief Operating Officer & Chief Technology Product Officer at Arango, examines how AI breaks down when it is asked to reason across disconnected architectures that cannot supply a unified, critical context. The discussion highlights how leaders can isolate the information that drives real decisions, structure access so AI can use it at the moment of action, and establish governance as agent‑generated outcomes move into production. This episode is sponsored by Arango. Learn how brands work with Emerj and other Emerj Media options at go.emerj.com/partner

A significant share of manufacturing knowledge still lives in the heads of retiring workers, and the window to capture it is closing as operations push toward AI-enabled ways of working. In this episode, Anand Gnanamoorthy, Director of Corporate Strategy and AI at Ingersoll Rand, examines how manufacturers can digitize tribal knowledge, structured operational data, and decades of unstructured archives before that context disappears. The discussion covers separating data, insights, and decision-making across AI deployments; tapping messy, unstructured data without over-cleaning it; anchoring use cases to the frontline worker rather than the process; and treating every AI project as permanently in pilot mode. This episode is sponsored by Poka. Learn how brands work with Emerj and other Emerj Media options at go.emerj.com/partner