Tag: Podcast

  • The New Rules for Scaling AI: What Yum Brands Learned

    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-sized

    About Cameron Davies

    Cameron 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 CONNECTED

    Subscribe to the AI with Maribel Lopez audio podcast: https://www.buzzsprout.com/1947446
    Subscribe 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

    —

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

    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 notes

    Most 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 it
    • The case for determinism in AI: why hardware design requires the same answer every time, regardless of who asks
    • How AI is making physics analysis accessible to more engineers, reducing dependence on a small pool of highly specialized talent
    • Why data security requirements are higher for hardware design than for most enterprise AI deployments — and what deployment models address that
    • How to think about AI across the full product lifecycle, from early concept to manufacturing sign-off
    • What “trust but verify” looks like in practice: building benchmarks before deploying AI in high-stakes design workflows

    Timestamps:

    Chapters:
    00:00 Introduction to AI and Vinci
    02:04 Understanding Physics Intelligence Layer
    04:20 The Role of Physics in AI Models
    07:04 Digital Twins and AI Scalability
    09:35 Misconceptions in AI for Physical Systems
    12:15 Determinism vs. Non-Determinism in AI
    15:01 Deployment Challenges for Physics-Based AI
    17:41 Signals of Success in AI Implementation
    20:20 The Future of AI in Hardware Design
    23:01 Preparing for the Shift to AI in Physical Systems

    Guest 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: Vinci

    Resources mentioned:

    📢 STAY CONNECTED

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

    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 NOTES

    OpenClaw 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

    TIMESTAMPS

    00:00  —  Why enterprise IT teams were watching OpenClaw from the sidelines

    01:45  —  What OpenClaw is and why it created an enterprise security problem

    04:00  —  What NemoClaw actually does: OpenShell, Privacy Router, Nemotron

    06:30  —  The SaaS ecosystem: Salesforce, ServiceNow, SAP, Cisco, CrowdStrike

    08:30  —  Where the hype is ahead of the reality

    10:15  —  Three questions CIOs should answer before deploying

    RESOURCES 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 keynote

    ABOUT THIS PODCAST

    AI 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 here: https://www.buzzsprout.com/1947446

    KEYWORDS

    enterprise AI agents, agentic AI security, NemoClaw NVIDIA, OpenClaw enterprise deployment, AI agent governance, enterprise AI strategy, AI governance enterprise, agentic AI risks

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

    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 miss

    Key Takeaway
    The 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.

  • SaaS Isn’t Dead — But the “Dead” Narrative Is Leading Enterprise Buyers Astray

    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 instead
    • Where agentic AI creates genuine revenue model pressure for SaaS vendors, and which vendors are already responding
    • The specific conditions that would have to be true for SaaS to decline significantly — and which are not yet met
    • How to evaluate your SaaS vendors' agentic AI readiness beyond roadmap promises
    • Why the liability and compliance math still heavily favors established SaaS platforms for most enterprise use cases

    Key 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 differentiation
    • The 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 pivot
    • Commodity SaaS with no data moat or compliance depth faces the hardest disruption; platforms with systems of record have a path forward
    • The right test for any SaaS vendor right now: what can they show you working in production — not a roadmap, not a demo

    Companies and Examples Referenced:

    • Salesforce / AgentForce: $540M ARR from agentic capabilities
    • Intercom: $200M ARR from AI-first product pivot
    • Workday: Certified connector ecosystem as an example of integration moats that can't be replicated quickly
    • SAP: Proactive procurement optimization as an example of SaaS becoming more valuable, not less

    Resources:

    Subscribe 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

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

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

    Keywords
    AI, agentic AI, Work Fusion, RPA, intelligent automation, compliance, machine learning, LLMs, automation, enterprise technology

    Episode Summary
    Agentic 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.”

    Chapters

    00:00
    Introduction to Agentic AI and Work Fusion

    02:00
    Transitioning from RPA to AI Agents

    04:38
    Operationalizing AI Agents in Business

    09:21
    Navigating the Hype of Agentic AI

    12:04
    The Role of LLMs in Regulated Environments

    14:47
    Multi-Agent Orchestration and Collaboration

    17:21
    Improving AI Agents through Learning

    21:01
    The Importance of Non-Human Identity in AI

    24:06
    Closing Thoughts on Adopting Agentic AI

  • 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 consumers
    Why 85% of retail is still offline (and what that means for AI commerce)
    The payments protocol wars: Google/Shopify vs. OpenAI/Stripe/PayPal
    Where to actually invest your AI budget in customer experience

    Spoiler: The “auto-magic” future isn't here yet. But the opportunities in between?  
    #AgenticAI #RetailInnovation #CommerceAI #NRF2026

  • 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, TaskUs
    Jarrod 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 notes
    00:00 – AI in Customer Experience (CX): What This Episode Covers

    01:31 – What a Chief Customer Officer Does in AI-Driven Customer Experience

    03:46 – Top Customer Experience (CX) Bottlenecks Blocking AI Adoption

    05:56 – 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 Experience

    15:41 – AI in Customer Experience: Cost Reduction vs. Revenue Growth

    18:21 – Voice AI in Customer Service: Why It Finally Works

    22:01 – AI Guardrails, Safety, and Brand Risk in Customer Experience

    26:31 – Measuring AI-Driven Customer Experience (CX Metrics That Matter)

    29:46 – AI for Customer Experience: Market Fragmentation and Vendor Landscape

    33:46 – Agentic AI Pitfalls to Avoid in Customer Experience Transformation

  • CES Quick Take Part 1: Julie Ask of Ask Advisory

    CES 2026 Quick Take: Physical AI, Ambient AI, and the Reality of Adoption

    In this episode, Maribel Lopez, founder and principal analyst at Lopez Research, is joined by Julie Ask, founder of Ask Advisory, for a candid, unscripted conversation on what CES 2026 actually revealed about the state of AI.

    Rather than focusing on flashy demos or speculative promises, Maribel and Julie examine where AI is delivering real value today—and where expectations are running ahead of reality. 

    Julie's bio

    Julie is a prominent customer experience analyst, technology futurist, and digital product strategist who has advised hundreds of global brands on the impact emerging technologies (e.g., mobile, sensors, extended reality, networks, AI) can and will have on customer experiences. She actively works with enterprises and vendors to understand how technology and consumer trends will impact their business with a deep focus on customer engagement strategies. 

    For more than 25 years, her work has defined the evolution of consumer digital experiences and inspired brands to take action. Her combined background in engineering and business gives her a unique ability to help business leaders understand what is possible and leverage technology to drive business outcomes. She has appeared frequently on Bloomberg while her research has been cited by the Wall Street Journal, New York Times, Financial Times, and a breadth of marketing publications. She co-authored The Mobile Mind Shift book in 2014. She founded Julie Ask Advisory in 2024 to pursue her passion for helping business leaders understand the impact of AI on experiences. 

  • 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 rate
    • McKinsey and BCG document widespread implementation struggles
    • Board-level AI initiatives now demand real ROI, not just innovation theater
    • The POC gold rush is over—experimentation budgets are drying up

    Agentic 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 vulnerabilities
    • Policy & Governance: Preventing rogue agents and establishing guardrails
    • Observability: Real-time monitoring to ensure agents perform as intended

    AWS re:Invent 2025 Highlights

    Agent Core Improvements

    • Enhanced policy frameworks defining agent boundaries and permissions
    • Human-in-the-loop controls for high-stakes decisions
    • Better cross-stack orchestration for multi-agent workflows

    The Discoverability Problem

    • AWS Marketplace now features natural language search
    • Upload requirements documents instead of filling rigid forms
    • AI-suggested prompts help non-technical users navigate complex decisions
    • Smarter 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 stack
    • Growing emphasis on open standards (A2A, MCP) for SaaS integration
    • Tools 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

  • 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.

  • 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

  • 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 systems
    • Key capabilities:
      • Sensing signals in data noise
      • Serving up actionable insights when needed
      • Connecting organizations across supply chains
      • Creating intelligent business communities

    Epicor Prism: Agentic AI Technology

    • What it is: Conversational ERP experience launched last year
    • Key features:
      • Natural language interaction (type or speak)
      • Information querying without knowing system screens/reports
      • Automated 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 implementation
    • Apply fundamental business case principles
    • “Nail it before you scale it” approach
    • Baseline analysis and clear success metrics

    Future Vision (Next 1-2 Years)


    Data Platform Evolution

    • Explosion of structured and unstructured data
    • Critical need for data normalization and health
    • Open, secure connections as “good cloud citizens”


    AI Development Trajectory

    • Current: Pre-trained models and agentic AI
    • Future: Self-service pipelines for custom AI model creation
    • Model-agnostic strategy with patented inference pipeline
    • Community-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

  • 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 AI
    • Democratization shift: Public LLMs like ChatGPT and Claude made AI accessible beyond data scientists
    • Innovation labs: 15-year-old team of PhDs and researchers finding insights in vast datasets

    Responsible AI Implementation

    • Risk Council: Cross-functional team ensuring responsible AI adoption
    • Security-first approach: Enterprise tools with guardrails protecting sensitive credit data
    • Custom AI stack: Proprietary systems maintaining data privacy while leveraging AI

    Agentic AI Applications

    • EVA Experian Virtual Assistant (Consumer Assistant): Evolved from chatbot to personalized agent that can take actions like unlocking credit scores
    • Business Assistant: Democratizes data science, enabling rapid model development through natural language
    • Real-time capabilities: Shifted from batch to real-time fraud detection

    AI-Powered Fraud Threats

    • Fraudster empowerment: Bad actors adopting AI faster than security measures
    • Deep fake risks: Sophisticated impersonation for identity theft and account takeover
    • Agent authentication: Challenge distinguishing legitimate vs. fraudulent AI agents
    • Industry urgency: Can't wait for regulation; must develop solutions proactively


    Key Achievements

    • Fast, safe adoption: Chose innovation over waiting, with proper security guardrails
    • Product success: Launched consumer EVA and business AI assistants
    • Industry leadership: Staying ahead of evolving fraud landscape


    Advice for Organizations

    1. Establish Risk Council: Cross-functional leadership team for AI governance
    2. Define values first: Determine organizational risk tolerance before technical implementation
    3. Support curiosity safely: Enable experimentation within secure boundaries
    4. Don't wait: Move quickly but responsibly – the technology won't slow down

    Key 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

  • 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 customers
    • Comprehensive ecosystem from vehicle upfitting to fleet management
    • Data services, telematics software, and fleet controls
    • Updated 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 daily
    • Sensor data ranging from tire pressure and GPS to seatbelt activity and driver behavior
    • Clean, structured data transformation into actionable insights

    AI Applications in Action

    • Digital vehicle walkarounds replacing 20-minute manual processes
    • Predictive maintenance moving customers from reactive to proactive service
    • E-switch assist tool using machine learning for electrification decisions
    • Connected uptime system achieving 98% vehicle availability

    Tangible Business Impact

    • 10% reduction in insurance costs through safer driving coaching
    • 20% improvement in driver safety metrics
    • 25% reduction in speeding incidents
    • 80% reduction in cost downtime
    • 10-20% 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 

  • 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 sources
    • Agentic AI: Takes the next step by actually executing actions autonomously, not just providing recommendations
    • The 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 validation
    • Gradually reduce oversight from constant monitoring to spot checks
    • Apply quality monitoring practices to AI agents similar to human agents
    • Consider 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 technology
    • Begin with well-understood processes that can be enhanced rather than completely reimagined

    Three Proven Starting Points:

    1. Call Wrap-up Automation: AI-powered summarization reduces agent workload
    2. IVR Modernization: Convert top call flows to agentic conversational AI
    3. Quality 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 references
    • Technology adaptability: Choose providers who can evolve with the rapidly changing AI landscape
    • Production readiness: “POCs are easy, production is hard” – prioritize vendors with production deployment experience


    Change Management for AI Adoption 

    • Deploy solutions that genuinely help employees first
    • Build internal champions through positive early experiences
    • Scale gradually to maintain trust and adoption


    Key Insights

    • Employee Experience Drives Customer Experience: AI solutions that improve employee satisfaction often lead to better customer outcomes
    • Observability is Critical: Comprehensive monitoring and quality management become essential as AI systems gain autonomy
    • Outcomes 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:

    1. Avoid spending significant resources on new technology only to use it exactly as you did before
    2. Always start with outcomes first – let business goals drive vendor selection, implementation strategy, and change management approaches