The AI with Maribel Lopez (AI with ML)

Hosted by Maribel Lopez

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May 2026

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The AI with Maribel Lopez podcast interviews leading thinkers, experts and innovators on the latest trends in Artificial intelligence areas such as agentic AI, generative AI, AI security, AI ethics and governance. Maribel Lopez is a technology industry analyst, keynote speaker and founder of the Data For Betterment Foundation and Lopez Research. The podcast shares advice, strategies and techniques on how to use AI solutions such as conversational AI, computer vision and automation to make businesses more efficient. New episodes are released every week on Wednesdays.

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May 19, 202628 min

Moving Beyond Building AI Agents With IBM's Suzanne Livingston

Enterprises have agents. Most can't run them at scale. IBM's Suzanne Livingston explains what changes when you have hundreds — not two.Full Show NotesScaling agentic AI is not the same problem as building it. At IBM Think 2026 in Boston, I sat down with Suzanne Livingston, VP of Product for IBM watsonx Orchestrate, to talk about where enterprise organizations actually are on this journey — and what it takes to move from a pilot to a production environment running hundreds of agents across dozens of departments.Suzanne walks through the full watsonx portfolio, then goes deep on the challenge she hears from customers constantly: the agent worked in the demo, but now it needs to run reliably at scale, with proper governance, observable across the estate, and permissioned correctly for every user and every system it touches. That is a fundamentally different problem than building the agent in the first place. The new Orchestrate Agent Control Plane is IBM's answer to it.This episode is for enterprise technology leaders who have moved past "should we do agents" and are now asking "how do we run them well." If your organization is somewhere between first pilot and full production deployment, this conversation is the one to listen to this week.What We CoverWhy the jump from generative to agentic AI changes the operating model, not just the technologyWhat agent orchestration means in practice when you have 40 sub-agents reporting to one master agentWhat the Orchestrate Agent Control Plane does and why cross-estate visibility matters more than per-agent optimizationHow enterprises are treating AI agents like digital employees — with identities, goals, managers, and performance reviewsWhy governance isn't optional in an agentic environment and what "governance light" looks like for organizations just getting started.Guest BioSuzanne Livingston is Vice President of Product Management for IBM watsonx Orchestrate, IBM's enterprise AI orchestration platform. She leads the product team responsible for agent building, orchestration, evaluation, and the recently announced Orchestrate Agent Control Plane. Suzanne presented at IBM Think 2026 in Boston.IBM Think profile: https://www.ibm.com/think/author/suzanne-livingstonResources MentionedIBM watsonx Orchestrate 30-day free trial: https://www.ibm.com/products/watsonx-orchestrateIBM Think 2026 content: https://www.ibm.com/thinkLopez Research blog: https://www.lopezresearch.com/research/📢 STAY CONNECTEDSubscribe to the AI with Maribel Lopez audio podcast: https://www.buzzsprout.com/1947446Subscribe to my LinkedIn newsletter — AI Decoded with Maribel Lopez: https://www.linkedin.com/newsletters/ai-decoded-with-maribel-lopez-7312533413582827520/Lopez Research blog: https://www.lopezresearch.com/research/Follow me on LinkedIn: https://www.linkedin.com/in/maribellopez/Follow me on X: https://x.com/MaribelLopez🔍 ABOUT MARIBEL LOPEZMaribel Lopez is founder and principal analyst at Lopez Research, a technology research and strategy firm focused on enterprise AI. She advises CIOs, CDOs, CMOs, IT leaders and technology vendors on AI adoption, agentic systems, AI governance, and AI-driven customer experience. Her insights have been featured in mainstream TV and print media such as Bloomberg, CGTN, Marketwatch, Reuters, Wall Street Journal, and Yahoo Finance. She's also a contributor to Forbes.com, and her research is used by organizations navigating the gap between AI capability and enterprise deployment reality.SEO Keywords I

May 13, 202624 min

Four Types of AI Agents With Dell's John Roese. Most Enterprises Are Only Building One

Dell's CTO built a 4-category agent framework from real production deployments. Most enterprises are ignoring two of the categories that matter most.Full Show NotesEnterprise leaders are mapping AI agents to org charts — building digital employees, agentic teams, AI workers — and then wondering why the results fall short. Dell's Global CTO John Roese has been running agents in production long enough to know exactly why that framing fails, and what to do instead.In this episode, Roese shares a framework Dell developed from actual production deployments, not pilots. It identifies four categories of AI agents defined by two dimensions: how much autonomy you grant the agent, and how complex the underlying process is. Most enterprises are focused on one category. Two of the four are widely overlooked — and they may represent the fastest path to measurable ROI.This is a practical, grounded conversation about where agents are actually delivering value today, how to think about infrastructure cost in the context of agent economics, and why the sequence in which you deploy agents matters as much as which agents you build. If your organization is trying to move from AI experimentation to production, this episode is required listening.3. Chapter titles:[00:00] — Introduction: Dell's dual role as tech vendor and enterprise AI user[01:38] — Why the org chart model for agents fails[03:12] — Decoupling human capacity from work capacity for the first time[04:23] — The two-by-two framework: autonomy vs. process complexity[06:14] — Productivity agents: what most enterprises already have[07:00] — Hygiene agents: the overlooked category that fixes foundational data problems[08:01] — The CRM data example: why every CRM is inaccurate and how agents fix it[10:05] — Latent infrastructure capacity: running agents in GPU white space to cut costs to cents[13:53] — Facilitation agents: removing entropy from complex cross-functional workflows[17:30] — The sequencing insight: hygiene and facilitation as the path to expert agents[19:24] — Why coordination agents aren't agentic bosses — and where human control actually lives[22:21] — Roese's closing advice: become literate, pick a few, get them into production4. Guest BioJohn Roese is the Global Chief Technology Officer and Chief AI Officer at Dell Technologies, where he is responsible for technology strategy, AI deployment, and research and development across the company. He has held senior technology leadership roles at Nortel, Enterasys Networks, Broadcom, and EMC. At Dell, he operates at a rare intersection: leading AI strategy for a major technology vendor while also deploying AI internally at enterprise scale — which means his frameworks are tested against real production constraints, not just market positioning.LinkedIn: linkedin.com/in/johnroeseDell Technologies: dell.comAbout This PodcastAI with Maribel Lopez is a podcast for enterprise technology leaders navigating AI adoption, agentic systems, AI infrastructure, and AI governance. Host Maribel Lopez covers enterprise technology and advises CIOs, CDOs, CMOs, and technology vendors on how to move from AI experimentation to measurable business outcomes. New episodes published bi-weekly.Subscribe on your platform of choice: buzzsprout.com/1947446

April 7, 2026Episode 7615 min

The New Rules for Scaling AI: What Yum Brands Learned

Picking a use case, proving value, and expanding has been the standard starting point for enterprise AI. For organizations early in their AI journey, that advice still holds. But for large enterprises that are past the pilot stage and trying to scale across business units, geographies, and brands, it isn't enough.At NVIDIA GTC, Cameron Davies, Chief Data Officer of Yum Brands, shared how his team is thinking about AI differently — and why they had to. With 63,000 restaurant locations, 100 million daily transactions, and 1,500 franchisees across 155 countries, Yum operates at a scale where a single bad AI decision can fail loudly, repeatedly, and fast.In this episode, Maribel breaks down Davies' framework and what it means for how enterprise leaders should be thinking about AI in 2026 and beyond.---**What you'll learn**- Why the use case as a unit of AI planning has a structural limitation at enterprise scale- What "scalable AI skills" means and why it's different from building agents for specific use cases- Why governance has to come before deployment, not after — and what happens when it doesn't- How measurement functions as operational discipline, not just a reporting obligation- What Yum's AI flywheel looks like and why it only works if measurement is continuous- What this framework means for organizations that aren't Yum-sizedAbout Cameron DaviesCameron Davies is the Chief Data Officer at Yum Brands, the parent company of KFC, Taco Bell, Pizza Hut, and The Habit Burger Grill. He leads the company's corporate data and analytics strategy and oversees the development and adoption of advanced data capabilities. He previously spent seven years as SVP at NBCUniversal and over 18 years at The Walt Disney Company, where he led the Corporate Center of Excellence for AI and machine learning.---**Resources and references mentioned**-NVIDIA GTC session: "Scaling AI Agents Globally Across Brands, Use Cases, and Restaurants" (S81755) — Cameron Davies, Yum Brands- Responsible AI Institute — chaired by Manoj Saxena- Trustwise — AI trust startup founded by Manoj Saxena- Byte — Yum Brands' proprietary e-commerce, point-of-sale, and menu platform- Lopez Research blog: The Rules for Scaling AI Have Changed. Yum Brands Proved It. — [LINK]---📢 STAY CONNECTEDSubscribe to the AI with Maribel Lopez audio podcast: https://www.buzzsprout.com/1947446Subscribe to my LinkedIn newsletter — AI Decoded with Maribel Lopez: https://www.linkedin.com/newsletters/ai-decoded-with-maribel-lopez-7312533413582827520/Lopez Research blog: https://www.lopezresearch.com/research/Follow me on LinkedIn: https://www.linkedin.com/in/maribellopez/Follow me on X: https://x.com/MaribelLopez---

March 31, 2026Episode 7527 min

Physics AI Explained: Why Hardware Design Requires a Different Kind of AI

Not every AI problem is a language problem. I talk with Vinci CEO Hardik Kabaria about what changes when AI has to reason about the physical world.Full show notesMost of the AI conversation in enterprise circles is about large language models — text, code, maybe images. This episode is about something different: what happens when AI has to reason about physical systems where the laws of physics don't negotiate and a wrong answer can't be patched after the product ships.I talked with Hardik Kabaria, CEO of Vinci, about how physics-based AI models are built differently from generative models, why determinism is a requirement rather than a preference in hardware design, and what it means for organizations manufacturing physical products to think carefully about where AI fits in their workflow. The conversation covers data security, scalability, and the practical question of how to evaluate new AI tools when the cost of a mistake is measured in product recalls rather than content edits.This episode is most relevant for technology leaders at companies that design or manufacture physical products. But the underlying insight — that deterministic and probabilistic AI serve different purposes and require different evaluation criteria — applies to any organization building a portfolio of AI tools.What we cover:Why physics-based AI is a different modality than large language models, and what that means for how you build and evaluate itThe case for determinism in AI: why hardware design requires the same answer every time, regardless of who asksHow AI is making physics analysis accessible to more engineers, reducing dependence on a small pool of highly specialized talentWhy data security requirements are higher for hardware design than for most enterprise AI deployments — and what deployment models address thatHow to think about AI across the full product lifecycle, from early concept to manufacturing sign-offWhat "trust but verify" looks like in practice: building benchmarks before deploying AI in high-stakes design workflowsTimestamps:Chapters:00:00 Introduction to AI and Vinci02:04 Understanding Physics Intelligence Layer04:20 The Role of Physics in AI Models07:04 Digital Twins and AI Scalability09:35 Misconceptions in AI for Physical Systems12:15 Determinism vs. Non-Determinism in AI15:01 Deployment Challenges for Physics-Based AI17:41 Signals of Success in AI Implementation20:20 The Future of AI in Hardware Design23:01 Preparing for the Shift to AI in Physical SystemsGuest bio Hardik Kabaria is CEO and co-founder of Vinci, an AI company building foundation models for the physical world. His background is in physics and geometry software for hardware engineering, with experience across the tools mechanical and electrical engineers use to design, simulate, and manufacture physical components. Vinci was founded two and a half years ago and is focused on making physics-based analysis accessible at the speed and scale of AI inference.Company: VinciResources mentioned:Vinci: https://www.getvinci.aiLopez Research blog: https://www.lopezresearch.com/research/📢 STAY CONNECTEDSubscribe to the AI with Maribel Lopez audio podcast: https://www.buzzsprout.com/1947446Subscribe to my LinkedIn newsletter — AI Decoded with Maribel Lopez: https://www.linkedin.com/newsletters/ai-decoded-with-maribel-lopez-7312533413582827520/Lopez Research blog: https://www.lopezresearch

March 25, 2026Episode 7415 min

NemoClaw, OpenClaw, and the Real Reason Enterprises Haven’t Deployed AI Agents Yet

NVIDIA’s NemoClaw adds enterprise security to OpenClaw. What it does, what it doesn’t, and what CIOs should do before deploying.FULL SHOW NOTESOpenClaw became the fastest-growing open-source project in history. Enterprise buyers watched from the sidelines — not because the technology wasn’t useful, but because an autonomous agent with access to corporate file systems, credentials, and external communication channels is a governance and security problem that no one had solved at the enterprise level.At NVIDIA’s GTC 2026 conference, Jensen Huang announced NemoClaw: a reference stack that adds enterprise security controls to OpenClaw. In this solo episode, Maribel Lopez breaks down what NemoClaw actually does, why the SaaS partner ecosystem matters as much as the technology itself, and where the hype is running ahead of the reality.WHAT WE COVER• Why OpenClaw created a shadow IT problem before NemoClaw existed• What OpenShell, the Privacy Router, and Nemotron models actually do for enterprise buyers• Why Salesforce, ServiceNow, SAP, Cisco, and CrowdStrike being in the ecosystem matters• The hardware dependency NVIDIA’s marketing glosses over• Why “working with NVIDIA” and “ready to deploy” are not the same thing• The three questions every CIO should answer before touching any of this TIMESTAMPS00:00 — Why enterprise IT teams were watching OpenClaw from the sidelines01:45 — What OpenClaw is and why it created an enterprise security problem04:00 — What NemoClaw actually does: OpenShell, Privacy Router, Nemotron06:30 — The SaaS ecosystem: Salesforce, ServiceNow, SAP, Cisco, CrowdStrike08:30 — Where the hype is ahead of the reality10:15 — Three questions CIOs should answer before deployingRESOURCES MENTIONED• NemoClaw announcement and NVIDIA Agent Toolkit: build.nvidia.com• Full written analysis: NemoClaw Brings Enterprise-Grade Security Controls to OpenClaw — lopezresearch.com• NVIDIA GTC 2026 Jensen Huang keynoteABOUT THIS PODCASTAI with Maribel Lopez covers enterprise AI adoption, agentic systems, AI governance, and AI-driven customer experience. Maribel Lopez is founder and principal analyst at Lopez Research, a technology research and strategy firm.Subscribe on Apple Podcasts, Spotify, or your platform of choice.KEYWORDSenterprise AI agents, agentic AI security, NemoClaw NVIDIA, OpenClaw enterprise deployment, AI agent governance, enterprise AI strategy, AI governance enterprise, agentic AI risks

March 10, 2026Episode 7311 min

Why Deploying More AI Tools Won’t Fix Your Workflows: Lessons Learned From Cisco

Most enterprises are layering AI tools on top of broken processes and wondering why ROI never materializes. In this solo episode, Maribel breaks down Cisco’s systematic approach to workflow redesign, why visibility into how work actually gets done is the missing first step, and what enterprise leaders need to change about their leadership culture and talent systems before AI adoption will deliver real results.Key Topics Covered• Why AI tool adoption without workflow redesign fails to deliver ROI• How Cisco’s Atlas AI agent system maps work across the enterprise• The digital workflow canvas that lets leaders redesign processes systematically• Results from Cisco’s pilot: 60% of activities AI-augmentable, 28 transformational use cases• Why framing AI as augmentation rather than headcount reduction drives adoption• The leadership and talent system changes most companies missKey TakeawayThe technology exists. The use cases are proven. What’s missing is the organizational discipline to redesign workflows before deploying more tools. Start with your data and your processes, not your tools.Resources & Links Blog post: Why AI Tool Adoption Without Workflow Redesign Is a Waste of Money [Lopez Research] Related: Five Steps to Follow for Successful AI Deployments [Lopez Research]Related: Three Shifts in AI-Driven Labor That CIOs and CEOs Can’t Ignore [Lopez Research]Subscribe to AI with Maribel Lopez on your channel of choice here.

March 3, 2026Episode 7012 min

SaaS Isn't Dead — But the "Dead" Narrative Is Leading Enterprise Buyers Astray

Episode Summary: The "SaaS is dead" narrative is generating real confusion for enterprise buyers trying to make procurement decisions right now. In this solo episode, Maribel Lopez breaks down the two legitimate arguments driving the disruption narrative — AI coding tools and agentic AI — separates what's real from what's overstated, and gives enterprise technology leaders the two questions that actually matter for evaluating their SaaS stack in an AI-first world.What You'll Learn:Why AI coding tools like Claude Code and Codex are not a SaaS replacement strategy — and what they should be used for insteadWhere agentic AI creates genuine revenue model pressure for SaaS vendors, and which vendors are already respondingThe specific conditions that would have to be true for SaaS to decline significantly — and which are not yet metHow to evaluate your SaaS vendors' agentic AI readiness beyond roadmap promisesWhy the liability and compliance math still heavily favors established SaaS platforms for most enterprise use casesKey Takeaways:Rebuilding mature systems of record with AI coding tools is not a competitive advantage — it's a distraction from building software that reflects your actual differentiationThe per-seat revenue model is under real pressure, but vendors moving on agentic capabilities are finding new revenue: Salesforce is generating $540M ARR from AgentForce; Intercom crossed $200M from its AI-first pivotCommodity SaaS with no data moat or compliance depth faces the hardest disruption; platforms with systems of record have a path forwardThe right test for any SaaS vendor right now: what can they show you working in production — not a roadmap, not a demoCompanies and Examples Referenced:Salesforce / AgentForce: $540M ARR from agentic capabilitiesIntercom: $200M ARR from AI-first product pivotWorkday: Certified connector ecosystem as an example of integration moats that can't be replicated quicklySAP: Proactive procurement optimization as an example of SaaS becoming more valuable, not lessResources:Read the full article: SaaS Isn't Dead. But Its Revenue Model Is Under Pressure — Lopez ResearchReferenced: Cathay Capital on agentic AI and B2B softwareConnect with Maribel on LinkedInSubscribe to AI with Maribel Lopez on your podcast channel of choice — links at lopezresearch.com.SEO Keywords: enterprise AI adoption, SaaS revenue model, agentic AI enterprise, AI agents B2B software, enterprise software evaluation, AI coding tools enterprise, SaaS disruption, enterprise AI strategy

February 24, 2026Episode 6924 min

Agentic AI Beyond the Hype: How Banks Are Actually Deploying It

KeywordsAI, agentic AI, Work Fusion, RPA, intelligent automation, compliance, machine learning, LLMs, automation, enterprise technologyEpisode SummaryAgentic AI dominated industry conversation in 2025. But in 2026, enterprise leaders are asking a harder question: How do we deploy AI agents safely, accurately, and in production environments?In this episode, Maribel Lopez speaks with Peter Cousins, CTO of WorkFusion a UiPath company, about how AI agents evolved from RPA and intelligent automation into production-ready “digital workers.” The discussion focuses on regulated industries, where explainability, auditability, and risk controls matter as much as automation gains.Rather than hype, this conversation explores what it takes to operationalize AI agents: governance frameworks, confidence thresholds, human oversight, and model risk management.Sound Bites"2025 was the big agentic AI year.""You can't just throw it in and it's good to go.""It's been great talking to you."Chapters00:00Introduction to Agentic AI and Work Fusion02:00Transitioning from RPA to AI Agents04:38Operationalizing AI Agents in Business09:21Navigating the Hype of Agentic AI12:04The Role of LLMs in Regulated Environments14:47Multi-Agent Orchestration and Collaboration17:21Improving AI Agents through Learning21:01The Importance of Non-Human Identity in AI24:06Closing Thoughts on Adopting Agentic AI

February 17, 202615 min

Agentic Commerce QuickTake: Should Anyone Care?

The National Retail Federation Show highlighted that Agentic Commerce is the new buzzword for 2026. But before you rewrite your roadmap, let's talk reality.Julie Ask and Maribel Lopez are discussing:What actually has to happen before agents can buy things for consumersWhy 85% of retail is still offline (and what that means for AI commerce)The payments protocol wars: Google/Shopify vs. OpenAI/Stripe/PayPalWhere to actually invest your AI budget in customer experienceSpoiler: The "auto-magic" future isn't here yet. But the opportunities in between? #AgenticAI #RetailInnovation #CommerceAI #NRF2026

January 21, 202636 min

AI, CX, and the Shift from Automation to Action with Jarrod Johnson of TaskUs

Agentic AI is emerging as the next evolution of artificial intelligence in customer experience (CX), moving beyond chatbots to systems that can take real action on behalf of customers. In this episode of AI with Maribel Lopez, Maribel Lopez speaks with Jarrod Johnson, Chief Customer Officer at TaskUs, about how enterprises are actually deploying AI in customer experience today. The conversation covers real-world CX use cases, where AI delivers measurable ROI, why data and process design remain the biggest bottlenecks, and how organizations should manage risk, governance, and human handoffs as agentic AI scales. This episode is designed for enterprise leaders evaluating AI strategies for customer experience transformation.Bio: Jarrod Johnson, Chief Customer Officer, TaskUsJarrod Johnson is the Chief Customer Officer of TaskUs. He is responsible for TaskUs' go-to-market strategy and execution across all client-facing and market-facing functions. Jarrod leads the "Client Organization" at TaskUs, including client success, sales, product and service management, and TaskUs’ consulting function, which includes the Agentic AI Consulting Practice. Jarrod is responsible for all aspects of revenue management and growth for TaskUs. He brings over 20 years of experience in enterprise technology-enabled services and business management.Show notes00:00 – AI in Customer Experience (CX): What This Episode Covers01:31 – What a Chief Customer Officer Does in AI-Driven Customer Experience03:46 – Top Customer Experience (CX) Bottlenecks Blocking AI Adoption05:56 – Chatbots vs. Agentic AI: What’s the Difference in Customer Experience?09:31 – How to Start with Agentic AI in Customer Experience (Real ROI Use Cases)12:46 – When AI Should Hand Off to Humans in Customer Experience15:41 – AI in Customer Experience: Cost Reduction vs. Revenue Growth18:21 – Voice AI in Customer Service: Why It Finally Works22:01 – AI Guardrails, Safety, and Brand Risk in Customer Experience26:31 – Measuring AI-Driven Customer Experience (CX Metrics That Matter)29:46 – AI for Customer Experience: Market Fragmentation and Vendor Landscape33:46 – Agentic AI Pitfalls to Avoid in Customer Experience Transformation