The AI with Maribel Lopez (AI with ML)

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 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. 

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

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

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.

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/; X: https://x.com/kerjordan?lang=en 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

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 Establish Risk Council: Cross-functional leadership team for AI governance Define values first: Determine organizational risk tolerance before technical implementation Support curiosity safely: Enable experimentation within secure boundaries 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

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 

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: Call Wrap-up Automation: AI-powered summarization reduces agent workload IVR Modernization: Convert top call flows to agentic conversational AI 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: Avoid spending significant resources on new technology only to use it exactly as you did before Always start with outcomes first - let business goals drive vendor selection, implementation strategy, and change management approaches

Description: In this episode from Google Cloud Next 2025, we dive deep into Best Buy's AI transformation with Ashley Daniels, VP of Product Management. Discover how one of America's largest retailers approached AI implementation strategically, moving from 93 contact center applications to a unified solution. Ashley shares the real story behind Best Buy's AI journey - the quick wins, unexpected challenges, and why your foundation matters more than the technology itself. From gift finder tools to revolutionizing customer care, learn practical strategies for implementing AI that actually drives business outcomes. Key insights covered: Why treating AI as a "tool in the toolbox" leads to better results The importance of starting with customer experience, not technology How to build strategic partnerships for AI implementation Why domain expertise becomes more critical in an AI world Real timeline: Getting AI summarization live in 6-8 weeks Whether you're in retail, customer service, or leading digital transformation initiatives, this conversation offers actionable insights for your AI strategy. Hosted by Maribel Lopez, founder and principal analyst at Lopez Research who interviewed Ashley Daniels, the VP of Product Management at Best Buy. You can follow Ashley here  https://www.linkedin.com/in/ashley-daniels1219/ and Maribel here https://www.linkedin.com/in/maribellopez/

Guest Profile Liz Centoni brings over 25 years of experience at Cisco where she currently leads a team of 20,000+ people dedicated to helping customers maximize the value of their technology investments. She also serves on the boards of Mercedes-Benz and Workday. Episode Highlights Cisco's Unique Position in the AI Landscape Liz outlines Cisco's three-pillar approach to AI:Investment in back-end AI networks with hyperscalers Enterprise deployment of secure AI use cases Meeting increased capacity requirements for both private and public front-end cloud networks Recent partnership with NVIDIA to accelerate AI adoption and simplify building AI-ready data centers Transforming Customer Experience Vision for customer experience: personalized, proactive, and predictive Goal: Make every customer "feel like they are our only customer" Leveraging data across tech stacks to break down silos and deliver proactive experiences Using AI to reduce cognitive load and workplace friction for employees AI Renewals Agent: A Case Study in Predictive AI Jointly developed with Mistral AI and announced in February 2025 Consolidates data from 50+ signals and sources (both structured and unstructured) Provides real-time sentiment analysis by incorporating customer support data Expected to reduce time spent on renewal proposals from 40% to less than 5% The Future of Agentic AI Moving from AI as a tool to AI as a teammate Current focus on assisting and augmenting tasks, not replacing roles Human oversight remains critical for complex customer networks Evolution from reactive to proactive customer care Impact on Jobs and Work Expectation that everyone needs baseline AI skills Historical pattern of rebalancing versus complete replacement Focus on using AI to eliminate busy work and reduce cognitive load Importance of emotional intelligence and empathy in areas where AI still falls short Closing Thoughts Liz's definition of success: "Customers walk up and say, 'You really know me better than I know myself'... and they feel they can't live without three things: Cisco's security, Cisco's networking portfolio, and Cisco services."

Summary In this conversation, Maribel Lopez and Jeetu Patel discuss the transformative potential of AI in business, the challenges organizations face in adopting AI, and the importance of security in AI applications. They explore the need for visibility, validation, and guardrails in securing AI, the rise of specialized AI models, and the future of AI agents in automating workflows. Patel emphasizes Cisco's commitment to innovation and the urgency for companies to embrace AI to remain relevant in a rapidly evolving landscape. Takeaways AI is transforming business strategies across industries. CEOs are optimistic about AI but feel unprepared. Security practitioners face significant staffing shortages. AI can both complicate and simplify security challenges. Organizations must secure AI models and use AI for defense. Visibility, validation, and guardrails are essential for AI security. Specialized AI models can be more effective and cost-efficient. AI agents will enhance productivity and workflow automation. Cisco is innovating rapidly and operating like a startup. Companies must embrace AI to thrive in the future. Chapters 00:00 The Exciting Intersection of AI and Business 02:47 Challenges in AI Adoption and Security 06:34 Securing AI: Visibility, Validation, and Guardrails 12:47 The Rise of Specialized AI Models 18:00 The Future of AI Agents and Automation 25:31 Cisco's Transformation and Innovation 31:10 Embracing AI: A Call to Action Follow us at:  Jeetu Patel  https://www.linkedin.com/in/jeetupatel/ Maribel Lopez https://www.linkedin.com/in/maribellopez/

In this episode, Maribel speaks with  Shub Bhowmick, the CEO and Co-founder of Tredence on how its using AI internally and externally. Bhowmick also provides advice on what's important for enterprise buyers looking to leverage AI.  Takeaways The shift from proof of concept to proof of value is crucial for businesses. AI is enabling organizations to achieve more with fewer resources. Agentic solutions are becoming increasingly relevant in various industries. Internal innovations at Treatance are focused on developing interconnected AI agents. Organizations must prepare for a future where they need to do more with less. Crawl, walk, and run is a practical approach to AI implementation. Creating a robust monitoring and operations foundation is essential. Small language models can be more effective and cost-efficient than larger models. AI can significantly enhance productivity and creativity in the workplace. Health and personal well-being are important considerations in a fast-paced professional environment. Sound Bites "Proof of value is the new proof of concept." "AI is enabling you to do more with less." "Agents are like smart interns, very analytical." "The speed of AI is moving much faster." "AI can 10x your productivity." "Crawl, walk, and run with AI implementation." "Small language models are the new thing." Chapters 00:00 Introduction to Treatance and AI Trends 07:28 Emerging Use Cases in AI 11:46 Real-World Applications of AI in Business 18:33 Internal Innovations at Treatance 30:33 Advice for Organizations on AI Implementation

Summary In this conversation, Maribel Lopez speaks with Markus Nispel about the integration of AI in networking solutions, particularly at Extreme Networks. They discuss the evolution of AI capabilities, the importance of data governance, and the role of AI in enhancing operational efficiency and security. Markus emphasizes the need for trust in AI systems and the potential of agentic AI to transform networking operations. The discussion also touches on the challenges of skill development and the future of AI in the industry.  Extreme Networks, trusted by tens of thousands of customers globally, delivers AI-native cloud networking solutions that seamlessly connect people, applications, data, and devices. Info on Extreme Networks Platform One: https://www.extremenetworks.com/platform-one and an explainer video https://vimeo.com/1036922077/58472f1411?ts=0&share=copy.  Takeaways  AI has been integrated into networking solutions for measurable business value.  Data quality is crucial for effective AI implementation.  Generative AI can significantly reduce the time for knowledge acquisition.  Agentic AI combines various capabilities for enhanced networking solutions.  Trust and transparency are essential for AI adoption in enterprises.  AI can optimize security policy configurations and reduce attack surfaces. The orchestration of agents is vital for achieving automation in networking. AI's role in skill development is critical for new employees. The future of AI in networking will involve more autonomous systems.  Continuous feedback loops enhance trust in AI systems.  Sound Bites "AI allows for a consistent support experience." "Data governance is critical for AI systems." "The orchestration of agents is key to automation." "Trust is essential for AI adoption in enterprises." "The future is dynamic with AI advancements."  Chapters  00:00 Introduction to AI in Networking 03:40 Evolution of AI Integration in Networking Solutions 06:54 Understanding AI's Unique Positioning in Networking 10:18 AI's Role in Skill Development and Knowledge Acquisition 13:02 Defining Agentic AI and Its Current Capabilities 16:54 The Importance of Orchestration in AI Systems 19:45 Addressing Trust and Resistance in AI Adoption 23:19 Demonstrating ROI from AI Implementations 25:29 Future of AI: The Rise of Agentic Systems 

In the category of better late than never, we found the missing recording file with Vijay. Enjoy! Show Notes: In this episode of "AI with Maribel Lopez," host Maribel Lopez sits down with Vijay Sundaram, Chief Strategy Officer at Zoho, at Zoho Day 25 in Austin, Texas. They discuss Zoho's strategic evolution and approach to AI. Key Highlights: Zoho's Market Evolution: Vijay explains how Zoho has expanded from primarily serving small and medium businesses to increasingly being adopted by larger enterprise customers worldwide. This evolution has happened naturally as their products became more sophisticated and larger customers discovered them. Enterprise Adaptation Challenges: To serve enterprise customers, Zoho had to make changes in three areas:Technology (their strength as a product-driven company) Operations (building expertise in account management, solutions consulting, etc.) Transitioning from an inbound to outbound business model AI Implementation Strategy: Vijay clarifies that while generative AI has recently captured public attention, Zoho has been implementing various AI technologies (machine learning, NLP, video recognition) for over a decade. Much of this AI has been "headless" - working behind the scenes in applications rather than through conversational interfaces. Three Levels of AI: Zoho approaches AI implementation through:Contextual AI within business applications Interactive AI for specific purposes Expert-level insights that enable non-experts to gain valuable business intelligence Platform Approach: By integrating applications and creating a comprehensive platform, Zoho can leverage data across domains (finance, sales, HR, operations) to provide more valuable AI-driven insights. AI Market Shift: Vijay predicts that AI differentiation will increasingly move from foundational models to the application layer, where companies like Zoho can add value through their access to business data across domains. Privacy and Security: Zoho maintains a strong stance on privacy (no trackers on their websites) and has built a "trust layer" into their platform to ensure proper data access controls for AI interactions.

Episode: AI with Maribel Lopez featuring Liz Centoni, EVP & Chief Customer Experience Officer at Cisco Guest Profile Liz Centoni brings over 25 years of experience at Cisco where she currently leads a team of 20,000+ people dedicated to helping customers maximize the value of their technology investments. She also serves on the boards of Mercedes-Benz and Workday. Episode Highlights Cisco's Unique Position in the AI Landscape Liz outlines Cisco's three-pillar approach to AI:Investment in back-end AI networks with hyperscalers Enterprise deployment of secure AI use cases Meeting increased capacity requirements for both private and public front-end cloud networks Recent partnership with NVIDIA to accelerate AI adoption and simplify building AI-ready data centers Transforming Customer Experience Vision for customer experience: personalized, proactive, and predictive Goal: Make every customer "feel like they are our only customer" Leveraging data across tech stacks to break down silos and deliver proactive experiences Using AI to reduce cognitive load and workplace friction for employees AI Renewals Agent: A Case Study in Predictive AI Jointly developed with Mistral AI and announced in February 2025 Consolidates data from 50+ signals and sources (both structured and unstructured) Provides real-time sentiment analysis by incorporating customer support data Expected to reduce time spent on renewal proposals from 40% to less than 5% The Future of Agentic AI Moving from AI as a tool to AI as a teammate Current focus on assisting and augmenting tasks, not replacing roles Human oversight remains critical for complex customer networks Evolution from reactive to proactive customer care Impact on Jobs and Work Expectation that everyone needs baseline AI skills Historical pattern of rebalancing versus complete replacement Focus on using AI to eliminate busy work and reduce cognitive load Importance of emotional intelligence and empathy in areas where AI still falls short Closing Thoughts Liz's definition of success: "Customers walk up and say, 'You really know me better than I know myself'... and they feel they can't live without three things: Cisco's security, Cisco's networking portfolio, and Cisco services."

Summary In this conversation, Maribel Lopez speaks with Ivana Bartoletti, the Global Privacy Chief Officer at Wipro, about the intersection of AI, privacy, and governance. They discuss the transformative impact of generative AI, the importance of embedding ethics in AI development, and the role of synthetic data in mitigating bias. Ivana also shares insights on the Audrey initiative aimed at promoting human rights in the digital age and highlights common mistakes in AI regulation. The conversation concludes with a positive outlook on the collaborative efforts to build fair and responsible AI. Takeaways Public trust is essential to harness AI's benefits. Generative AI is transforming how we live and work. Privacy is a crucial collective good that must be respected. Ethics in AI goes beyond compliance with laws. AI should retain human agency and decision-making. Bias in algorithms can perpetuate social inequalities. Synthetic data can help mitigate bias but has limitations. Transparency in data usage is vital for equity. AI regulation should not be seen as opposing innovation. Collaboration across sectors is key to responsible AI governance. You can follow Ivana here: https://www.linkedin.com/in/ivana-bartoletti-77b2b29/ You can follow me here:  https://www.linkedin.com/in/maribellopez/ https://www.youtube.com/@AIwithMaribelLopez https://x.com/MaribelLopez

Episode Overview: Maribel Lopez speaks with Tim Marklein, CEO of Big Valley Marketing, about how AI is changing marketing and communications. The conversation explores the practical applications, limitations, and future of AI in the marketing landscape. Guest: Tim Marklein - CEO of Big Valley Marketing, an award-winning consulting firm that helps technology companies grow and win in various markets including software infrastructure, AI, cybersecurity, digital health, and supply chain. Key Topics Discussed: Current state of AI adoption in marketing: Despite surveys showing varied adoption rates, most professionals are still "dabbling" with AI rather than fully integrating it into workflows Three key areas where AI is proving valuable:As a search alternative for market insights For pattern analysis and audience research For writing and editing assistance The continued importance of original thinking: AI can't replace a company's unique point of view, especially in B2B contexts where buyers want to understand a company's beliefs and perspectives Brand differentiation concerns: Discussion about whether widespread AI adoption might lead to homogenized marketing content and brand positioning AI for audience targeting: How AI can help with audience research but cannot replace strategic decisions about which audiences to prioritize Workflow integration challenges: The disconnect between the ideal AI tools and those integrated into existing workflows AI and marketing metrics: How AI primarily makes it easier to capture existing metrics rather than creating new ones Authenticity and ethics: The research showing that simply disclosing AI use doesn't build trust when 80% of people don't trust AI to begin with Appropriate vs. responsible use: The importance of communicating who is using AI and why, not just how it's being used Skills development for the AI era: The value of experimentation and curiosity over becoming a dedicated "prompt engineer" You can follow Tim Marklein, the Founder and CEO, Big Valley Marketing ( bigvalley.co) at LinkedIn: https://www.linkedin.com/in/tmarklein/ X: @tmarklein You can follow me at: https://www.linkedin.com/in/maribellopez/ https://www.youtube.com/@AIwithMaribelLopez https://x.com/MaribelLopez

Episode Summary In this episode, Maribel Lopez interviews Kate Soule, Director of Technical Product Management for IBM's Granite products. They discuss IBM's third-generation AI models, their focus on efficiency and enterprise readiness, and the latest advancements including vision capabilities and reasoning features. Guest Kate Soule - Director of Technical Product Management for IBM's Granite products Key Topics & Timestamps 00:04 - Introduction Maribel introduces the show and Kate Soule Brief overview of IBM Granite as fit-for-purpose, open-source enterprise AI models 00:48 - What is IBM Granite? Designed as core building blocks for enterprises building with generative AI Focus on efficiency with smaller model sizes Monthly innovation updates to keep pace with rapidly evolving field 02:19 - Understanding AI Reasoning Explanation of reasoning capabilities in AI models How allowing models to generate more text at inference time can improve performance Cost/benefit tradeoffs of reasoning features 03:13 - Enterprise AI Model Selection Criteria Moving beyond "one model to rule them all" thinking Importance of fit-for-purpose models Why smaller models can be customized more easily Trust and transparency considerations 05:38 - AI Governance and Safety How to evaluate models for governance requirements Safety evaluations and benchmarks as table stakes Systems-based approach to safety with guardrails IBM's Granite Guardian and protection mechanisms 08:55 - Benefits of Smaller Models Why size matters: cost, latency, and customization advantages Smaller models are easier to customize and require less computing power IBM's transparent approach to training data 10:13 - Future of AI Evaluation Performance per cost becoming the key evaluation metric The growing importance of flexibility in model selection How the "efficient frontier" between cost and performance will differentiate providers 12:41 - IBM's Vision Models IBM's pragmatic enterprise focus for multimodal capabilities Vision understanding (image in, text out) for practical business use cases Specialization for documents, charts, and dashboards Delivering powerful capabilities in only 2 billion parameters 15:25 - Understanding Model Size Context Evolution from millions to billions of parameters Practical considerations of deploying different-sized models Finding the right cost-benefit trade-off for specific use cases

This conversation explores the transformative impact of AI on business, particularly focusing on Zoho's evolution as it aims to enhance its enterprise offerings. The speakers discuss the importance of understanding customer data, the global dynamics of AI adoption, and Zoho's unique culture that fosters innovation. They also touch on the future of enterprise software and the integration of AI, emphasizing the need for a holistic view of customer engagement. Takeaways: Massive interest in AI is changing markets and valuations. Tools are only as effective as their application in serving customers. Zoho is aggressively moving into the enterprise space with a focus on AI. Integration of data is crucial for a comprehensive customer view. The concept of customer 360 is often misunderstood. Global market dynamics affect how AI is adopted in different regions. Zoho's culture promotes innovation and responsiveness to customer needs. AI will soon be an integral part of enterprise operations. Not every enterprise is a fit for Zoho's offerings. The future of enterprise software will be driven by AI and data integration. Chapters 00:00 The Rise of AI and Its Impact on Business 01:59 Zoho's Evolution and Enterprise Focus 06:10 Understanding Customer Data and Integration 10:08 Global Perspectives on AI and Market Dynamics 13:54 Zoho's Unique Culture and Approach to Innovation 21:56 Future of Enterprise Software and AI Integration