The AI with Maribel Lopez (AI with ML)

Maribel Lopez
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  • TECHNOLOGY
  • UPDATED WEEKLY

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. 

  1. 12/05/2025

    From AI Chaos to Production: Why 2026 is the Year Enterprise AI Gets Real

    Maribel Lopez reports live from AWS re:Invent 2025 in Las Vegas, unpacking why the AI experimentation phase is officially over. With statistics that say 95% of AI projects are failing and enterprise budgets tightening, 2026 demands production-quality AI—not more proof-of-concepts. This episode explores the critical shift from building agents to deploying them safely at scale. Key Themes The Reality Check (2025 Recap) MIT study reveals 95% AI project failure rateMcKinsey and BCG document widespread implementation strugglesBoard-level AI initiatives now demand real ROI, not just innovation theaterThe POC gold rush is over—experimentation budgets are drying upAgentic AI Grows Up The conversation has evolved from "can we build agents?" to "can we trust them in production?" Three critical roadblocks: Security & Orchestration: How agents interact without creating vulnerabilitiesPolicy & Governance: Preventing rogue agents and establishing guardrailsObservability: Real-time monitoring to ensure agents perform as intended AWS re:Invent 2025 Highlights Agent Core Improvements Enhanced policy frameworks defining agent boundaries and permissionsHuman-in-the-loop controls for high-stakes decisionsBetter cross-stack orchestration for multi-agent workflowsThe Discoverability Problem AWS Marketplace now features natural language searchUpload requirements documents instead of filling rigid formsAI-suggested prompts help non-technical users navigate complex decisionsSmarter filtering for nuanced needs (performance vs. cost vs. compliance)The Full-Stack Maturity Recognition that AI "takes a village"—no single vendor owns the entire stackGrowing emphasis on open standards (A2A, MCP) for SaaS integrationTools designed for all skill levels, not just data scientists Key Takeaway Enterprise AI in 2026 isn't about doing more—it's about doing it right. The winners will be organizations that prioritize governance, observability, and practical deployment over flashy demos. Host: Maribel Lopez Recorded: AWS re:Invent, Las Vegas, December 2025 Follow-up: Stay tuned for next week's deep-dive episode with demos and vendor interviews

    13 min

  2. 10/13/2025

    AI Meets Cybersecurity: Protecting Critical Infrastructure with Black & Veatch’s Ian Bramson

    In this episode of AI with Maribel Lopez, Maribel sits down with Ian Bramson, Vice President of Global Industrial Cybersecurity at Black & Veatch, to explore the growing intersection between artificial intelligence and operational technology (OT) security. From power grids and oil refineries to manufacturing plants, critical infrastructure systems are becoming increasingly connected—and therefore more vulnerable. Ian shares how Black & Veatch is helping industrial organizations rethink cybersecurity from the ground up, integrating protection early in the design and build process rather than bolting it on later. Together, Maribel and Ian discuss the evolution of OT threats, the rise of AI in both defense and attack scenarios, and why cybersecurity must be seen as a core business function, not an afterthought. 🧩 Key Discussion Topics 1. The Evolution of Industrial Cybersecurity Ian’s unconventional career path—from Coca-Cola to futurist consulting with Alvin Toffler to leading cybersecurity initiatives.Why Black & Veatch launched its dedicated industrial cybersecurity practice and how it’s integrated across engineering, procurement, and construction (EPC).2. IT vs. OT Cybersecurity: What’s the Difference? IT focuses on data protection; OT focuses on physical safety and uptime.The rising threat of cyber-physical attacks on power, water, and manufacturing systems.How the increasing connectivity of devices—from pumps to sensors to AI controllers—creates new risks.3. Foundational Security: Basics Still Matter Start with asset inventory—knowing what you need to protect.Identify vulnerabilities and train your “human layer.”Build security in from day one instead of bolting it on later.4. The Expanding Threat Landscape Why ransomware is still relevant but no longer the only concern.The growing risks of supply chain attacks, remote operations, and super dependencies (as seen in the CrowdStrike outage).How attackers are weaponizing AI to accelerate attacks—and how defenders can use AI for faster detection and response.5. AI and OT: A Double-Edged Sword How AI is reshaping the attack surface for industrial systems.Why every company is already “in the AI game,” whether they realize it or not.The three layers of AI to consider: AI used in cybersecurity, AI inside your operations, and AI in the wild used by partners and adversaries.6. The Biggest Misconceptions About OT Security The “myth of the air gap”—why physical isolation no longer guarantees safety.Common organizational blind spots: board confusion between IT and OT, fragmented responsibility, and lack of lifecycle thinking.The need for Cyber Asset Lifecycle Management (CALM) to ensure long-term resilience.7. Building a Resilient Future Why early planning and a holistic approach are key to managing future risks.The importance of embedding security, governance, and ethics into every new AI or industrial project.

    26 min

  3. 10/13/2025

    Why Your Gut Instinct is Costing You Millions a Chat with Verint's AI Analytics Expert Daniel Ziv

    About This Episode Daniel Ziv, Global VP of AI and Analytics at Verint, reveals why experienced executives are making their worst decisions in decades—and how AI analytics is rewriting the rules of business intelligence. Learn the two critical frameworks that separate AI winners from losers, and why the biggest risk isn't picking the wrong technology—it's doing nothing at all. Guest Bio Daniel Ziv leads AI and analytics product management and go-to-market strategy at Verint, where he helps global enterprises transform customer experience through data-driven decision-making. With two decades in the analytics space, Daniel has witnessed firsthand how AI is fundamentally changing what's possible in customer insights. Key Timestamps [00:00] - Why change is happening faster than ever before [03:04] - The Macro vs. Micro Analytics Framework explained [06:19] - Two flawed decision-making patterns destroying value [09:20] - Real ROI: $80M saved, $10M found in 48 hours [15:32] - Generative AI vs. Agentic AI: What's the difference? [21:03] - The hybrid cloud advantage (why on-prem isn't dead) [26:35] - Common misconceptions about Verint [28:49] - Daniel's advice for making AI decisions today [32:17] - Final thoughts: "Ride the dragon" Key Takeaways The Two Fatal Mistakes: Gut-based decisions without data - Your experience is becoming less reliable as change accelerates Analysis paralysis - Waiting weeks for insights while competitors move in hours The Macro-Micro Framework: Macro Analytics: Understand patterns across ALL interactions (the 30,000-foot view) Micro Analytics: Apply insights to individual interactions in real-time Companies that excel at both create significant competitive advantage Real Results: Large telecom: $80M saved + 11% sales increase Typical deployment: $5-10M in insights found within 1-2 days UK financial services: $5M additional revenue from loan process improvements Energy supplier: $2M saved through increased agent capacity Generative → Agentic Evolution: Generative AI responds to prompts (you ask, it answers) Agentic AI breaks down goals and executes multi-step workflows autonomously Example: Genie Bot evolved from answering questions to analyzing, quantifying, and exporting results automatically Action Items for Listeners Audit your decision-making speed - Are you making gut calls or waiting too long for data? Identify one quick-win AI deployment - What could you turn on this week without changing infrastructure? Evaluate your analytics gaps - Do you have macro insights, micro operationalization, or both? Test before scaling - Start with 300 users, validate, then scale to 30,000 Connect with Daniel - Reach out on LinkedIn to discuss your specific use case Connect With Daniel Ziv LinkedIn: https://www.linkedin.com/in/dziv1/ About the Host Maribel Lopez brings decades of technology industry analysis experience, helping business leaders cut through hype to understand what actually works in AI, cloud, and digital transformation. https://www.linkedin.com/in/maribellopez/ Subscribe & Follow If you found this conversation valuable, subscribe for more deep dives with AI leaders who are actually deploying this technology and seeing real business results. Tags: #AI #Analytics #CustomerExperience #GenAI #AgenticAI #BusinessIntelligence #CXAutomation #DataDriven #DigitalTransformation #Verint

    32 min

  4. 09/23/2025

    What's Next for Cognitive ERP and Manufacturing Intelligence with Epicor's Kerrie Jordan

    Episode Overview Host Maribel Lopez sits down with Kerrie Jordan, the newly appointed Chief Marketing Officer at Epicor, to discuss the evolution of ERP systems and the transformative power of cognitive ERP in manufacturing, distribution, and supply chain industries. Guest Bio and social links Kerrie Jordan - Chief Marketing Officer, Epicor Kerrie Jordan, Chief Marketing Officer at Epicor, leads the global go-to-market efforts, bringing together her deep product innovation and strategic marketing experience to drive brand growth and customer engagement across the make, move, and sell industry communities. https://www.linkedin.com/in/kerriejordan/ Key Topics Discussed Cognitive ERP: From System of Record to System of Action Definition: Transforming ERP from passive data storage to intelligent, proactive decision-making systemsKey capabilities:Sensing signals in data noiseServing up actionable insights when neededConnecting organizations across supply chainsCreating intelligent business communitiesEpicor Prism: Agentic AI Technology What it is: Conversational ERP experience launched last yearKey features:Natural language interaction (type or speak)Information querying without knowing system screens/reportsAutomated actions with human approval (semi-autonomous approach)Multiple specialized agents (Knowledge Agent, RFP Agent, Business Communications Agent)Real-World Success Stories Measuring AI ROI Focus on specific business outcomes, not just AI implementationApply fundamental business case principles"Nail it before you scale it" approachBaseline analysis and clear success metricsFuture Vision (Next 1-2 Years) Data Platform Evolution Explosion of structured and unstructured dataCritical need for data normalization and healthOpen, secure connections as "good cloud citizens" AI Development Trajectory Current: Pre-trained models and agentic AIFuture: Self-service pipelines for custom AI model creationModel-agnostic strategy with patented inference pipelineCommunity-based insights and collaboration Quotable Moments "We are an organization that is really focused on our core industries... making, moving, selling the things that we use every day""It's all about accelerated value... How can we get as close to zero as possible?""This era that we're in [is] like the modem dial-up era of AI""Nail it before you scale it

    39 min

  5. 09/16/2025

    Racing Against AI-Powered Fraudsters: How Experian Stays Ahead

    Overview Maribel Lopez interviews Kathleen Peters, Experian's Chief Innovation Officer, about AI's evolution in fraud detection, the shift to generative and agentic AI, and balancing innovation with security in financial services. Key Topics AI Evolution at Experian 15-year AI journey: Using machine learning for fraud detection long before generative AIDemocratization shift: Public LLMs like ChatGPT and Claude made AI accessible beyond data scientistsInnovation labs: 15-year-old team of PhDs and researchers finding insights in vast datasetsResponsible AI Implementation Risk Council: Cross-functional team ensuring responsible AI adoptionSecurity-first approach: Enterprise tools with guardrails protecting sensitive credit dataCustom AI stack: Proprietary systems maintaining data privacy while leveraging AIAgentic AI Applications EVA Experian Virtual Assistant (Consumer Assistant): Evolved from chatbot to personalized agent that can take actions like unlocking credit scoresBusiness Assistant: Democratizes data science, enabling rapid model development through natural languageReal-time capabilities: Shifted from batch to real-time fraud detectionAI-Powered Fraud Threats Fraudster empowerment: Bad actors adopting AI faster than security measuresDeep fake risks: Sophisticated impersonation for identity theft and account takeoverAgent authentication: Challenge distinguishing legitimate vs. fraudulent AI agentsIndustry urgency: Can't wait for regulation; must develop solutions proactively Key Achievements Fast, safe adoption: Chose innovation over waiting, with proper security guardrailsProduct success: Launched consumer EVA and business AI assistantsIndustry leadership: Staying ahead of evolving fraud landscape Advice for Organizations Establish Risk Council: Cross-functional leadership team for AI governanceDefine values first: Determine organizational risk tolerance before technical implementationSupport curiosity safely: Enable experimentation within secure boundariesDon't wait: Move quickly but responsibly - the technology won't slow downKey Quote "If you set up the infrastructure right, then you can let them hack away. You can let people be very curious." Participants: Maribel Lopez (Host), Kathleen (CIO, Experian) Focus: #AI #FraudDetection #GenerativeAI #AgenticAI #FinancialServices #Security Kathleen Peters Chief Innovation Officer NA Fraud, Innovation & Commercialization  Kathleen Peters leads innovation and strategy for Experian’s Fraud and Identity business in North America, continuously exploring new ways to solve market challenges in identity, risk, and fraud detection. She and her team define business strategies and investment priorities while incubating new products, analyzing industry trends and leveraging the latest technologies to bring ideas to life. Kathleen joined Experian in 2013 to lead business development and global product management for Experian’s newest fraud products. She later served as the Head of the North America Fraud & Identity business, until being named Chief Innovation Officer for Decision Analytics in 2020. Kathleen has twice been named a “Top 100 Influencer in Identity” by One World Identity (now Liminal), an exclusive list that annually recognizes influencers and leaders from across the globe, showcasing a who’s who of people to know in the identity space.For nearly two decades, she has lived in

    27 min

  6. 08/27/2025

    Ford Pro's Kevin Dunbar Shares How AI Transforms Fleet Management

    Episode Summary Kevin Dunbar joins Maribel Lopez to discuss how AI is revolutionizing commercial fleet management through Ford Pro Intelligence. With nearly two decades of experience at companies like Cisco and Palo Alto Networks, Kevin shares insights on how Ford's commercial division is processing over a billion data points daily to help fleet operators optimize operations, reduce costs, and improve safety.AI with Maribel Lopez: Transforming Fleet Management with Kevin Dunbar Guest: Kevin Dunbar, General Manager of Ford Pro Intelligence Host: Maribel Lopez, Founder of the Data for Betterment Foundation and Lopez Research Key Topics Covered Ford Pro Intelligence Platform Commercial division serving business and government customersComprehensive ecosystem from vehicle upfitting to fleet managementData services, telematics software, and fleet controlsUpdated from last earnings to 757,000 and 24% yoy growth. (vs. 675,000+ subscribers with 20% growth rate.)Data at Scale Processing over 1 billion connected vehicle data points dailySensor data ranging from tire pressure and GPS to seatbelt activity and driver behaviorClean, structured data transformation into actionable insightsAI Applications in Action Digital vehicle walkarounds replacing 20-minute manual processesPredictive maintenance moving customers from reactive to proactive serviceE-switch assist tool using machine learning for electrification decisionsConnected uptime system achieving 98% vehicle availabilityTangible Business Impact 10% reduction in insurance costs through safer driving coaching20% improvement in driver safety metrics25% reduction in speeding incidents80% reduction in cost downtime10-20% total cost of ownership reduction Notable Quotes "We want to make sure that their Ford vehicle works as hard for their business digitally as it does mechanically." - Kevin Dunbar "It's not just about having data. It's about having clean, structured data." - Kevin Dunbar For more episodes of "AI with Maribel Lopez," visit Lopez Research and follow our latest insights on AI transformation across industries. About Ford  Pro and Ford Pro Intelligence Ford Pro is helping commercial customers transform and expand their businesses with vehicles and services tailored to their needs. Ford Pro Intelligence is Ford’s comprehensive solution for fleet digitalization and operational efficiency, combining connected vehicle data, telematics tools, and smart management software under one platform Follow Kevin at https://www.linkedin.com/in/kevin-dunbar-78343558/ Follow Maribel at https://www.linkedin.com/in/maribellopez/ #FordProIntelligence #FordPro #FleetManagement #Fleets  #DataSecurity

    27 min

  7. 08/20/2025

    Verint Executive Reveals: The 3 Best Starting Points for Enterprise Agentic AI Adoption

    Episode Overview In this episode, Maribel Lopez sits down with David Singer, Global Vice President and Go-To-Market Strategy at Verint, to explore the rapid evolution from generative AI to agentic AI and how organizations can successfully implement AI solutions that deliver real business outcomes. Key Topics Discussed The Evolution from Generative to Agentic AI Generative AI: Excellent at answering questions and synthesizing information from knowledge sourcesAgentic AI: Takes the next step by actually executing actions autonomously, not just providing recommendationsThe critical difference: autonomous decision-making versus rules-based automation Building Trust in Autonomous AI Systems Start with human-in-the-loop monitoring for training and validationGradually reduce oversight from constant monitoring to spot checksApply quality monitoring practices to AI agents similar to human agentsConsider AI agents as "silicon-based employees" requiring training, access controls, and performance management Successful AI Implementation Strategies Start with Clear Outcomes: Define specific business goals before selecting technology Focus on solutions that deliver outcomes, not just impressive technologyBegin with well-understood processes that can be enhanced rather than completely reimaginedThree Proven Starting Points: Call Wrap-up Automation: AI-powered summarization reduces agent workloadIVR Modernization: Convert top call flows to agentic conversational AIQuality Management: Scale from monitoring 1-3% of calls to near 100% coverage Vendor Selection Criteria Proven outcomes at scale: Look for vendors with demonstrated success stories and customer referencesTechnology adaptability: Choose providers who can evolve with the rapidly changing AI landscapeProduction readiness: "POCs are easy, production is hard" - prioritize vendors with production deployment experience Change Management for AI Adoption  Deploy solutions that genuinely help employees firstBuild internal champions through positive early experiencesScale gradually to maintain trust and adoption Key Insights Employee Experience Drives Customer Experience: AI solutions that improve employee satisfaction often lead to better customer outcomesObservability is Critical: Comprehensive monitoring and quality management become essential as AI systems gain autonomyOutcomes Over Technology: Success comes from focusing on business results rather than being enamored with the latest AI capabilities About the Guest David Singer is the Global Vice President and Go-To-Market Strategy at Verint, where he focuses on delivering AI-powered outcomes for customer experience automation. Verint has been incorporating AI into their platform for over a decade, evolving from call recording and workforce management to comprehensive CX automation solutions.  You can follow David here: https://www.linkedin.com/in/dwsinger/ You can follow Maribel here:  Closing Thoughts Singer emphasizes two crucial points for organizations embarking on AI initiatives: Avoid spending significant resources on new technology only to use it exactly as you did beforeAlways start with outcomes first - let business goals drive vendor selection, implementation strategy, and change management approaches

    33 min

  8. 08/11/2025

    Cisco Live 2025: Jokel and Pandey on Enterprise AI Infrastructure and the Internet of Agents

    In this episode from Cisco Live, Maribel Lopez sits down with two Cisco executives, Vijoy Pandey, SVP of Outshift at Cisco and Nathan Jokel, SVP of Corporate Strategy and Alliances at Cisco, to discuss how AI is fundamentally changing enterprise infrastructure over the next year. The conversation explores the evolution from deterministic to probabilistic computing, the emergence of agentic workflows, and practical advice for business leaders navigating the AI transformation. Host: Maribel Lopez Guests: Vijoy Pandey, SVP of Outshift at CiscoNathan Jokel, SVP of Corporate Strategy and Alliances at CiscoRecorded at: Cisco Live Episode Overview In this episode from Cisco Live, Maribel Lopez sits down with two Cisco executives to discuss how AI is fundamentally changing enterprise infrastructure over the next year. The conversation explores the evolution from deterministic to probabilistic computing, the emergence of agentic workflows, and practical advice for business leaders navigating the AI transformation. Key Topics Discussed The Three Waves of AI Infrastructure Evolution Wave 1: AI training in public cloud (mostly behind us)Wave 2: AI inference moving to enterprise data centers for control, security, and economic reasonsWave 3: AI moving to the edge with physical and embodied AI requiring new infrastructure for robots and devicesFrom Deterministic to Probabilistic Computing Vijoy explains the fundamental shift happening in computing: Traditional computing: deterministic, machine-speed but limitedHuman intelligence: agentic but slowNew paradigm: AI agents with human-like behavior operating at machine speed and scaleThe Internet of Agents A collaboration platform where AI agents from different vendors can: Get discovered and authenticatedCompose workflows togetherExecute tasks collaborativelyBe evaluated for performanceReal-world example: Building a sales funnel portal using agentic interfaces from Salesforce, ServiceNow, Microsoft, and Cisco security - all working together without manual UI clicking. AI and Energy Challenges The Problem: By 2028, projected 63 gigawatt shortfall for new data center capacitySolutions:Invest in diverse energy sources (nuclear, renewables, battery storage)Build data centers near power sources (e.g., Cisco's Middle East partnerships)Develop more energy-efficient infrastructureFocus on smaller, specialized models instead of racing for maximum parametersCisco's Specialized AI Models Foundation SAC 8B: 8 billion parameter model specialized for security policyDeep Network Model: Expert model trained on network configurations Outshift: Cisco's Innovation Engine Cisco's internal incubator tackling problems adjacent to core business in: Space: Areas adjacent to networking, security, observability, collaborationTime/Risk: Higher-risk ventures that can't enter at Cisco scale initiallyCurrent Big Hairy Audacious Goals (BHAGs):Internet of AgentsQuantum Internet - building quantum networks for distributed quantum computing

    21 min
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About

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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