The AI with Maribel Lopez (AI with ML): Recent Episodes
Maribel Lopez
In the AI with Maribel Lopez podcast, technology industry analyst and keynote speaker Maribel Lopez, interviews leading thinkers, experts and innovators on the latest trends in Artificial intelligence areas such as machine learning, deep learning, image recognition, natural language processing (NLP), neural networks and AI ethics. It connects you with top researchers, data scientists, engineers and business leaders in the data, analytics and AI field. Guests will share advice, strategies and techniques on how to use AI solutions such as chatbots, computer vision and automation to make businesses more efficient. New episodes are released every two weeks, on Tuesdays.
Enterprises have agents. Most can't run them at scale. IBM's Suzanne Livingston explains what changes when you have hundreds — not two.
Full Show Notes
Scaling agentic AI is not the same problem as building it. At IBM Think 2026 in Boston, I sat down with Suzanne Livingston, VP of Product for IBM watsonx Orchestrate, to talk about where enterprise organizations actually are on this journey — and what it takes to move from a pilot to a production environment running hundreds of agents across dozens of departments.
Suzanne walks through the full watsonx portfolio, then goes deep on the challenge she hears from customers constantly: the agent worked in the demo, but now it needs to run reliably at scale, with proper governance, observable across the estate, and permissioned correctly for every user and every system it touches. That is a fundamentally different problem than building the agent in the first place. The new Orchestrate Agent Control Plane is IBM's answer to it.
This episode is for enterprise technology leaders who have moved past "should we do agents" and are now asking "how do we run them well." If your organization is somewhere between first pilot and full production deployment, this conversation is the one to listen to this week.
What We Cover
- Why the jump from generative to agentic AI changes the operating model, not just the technology
- What agent orchestration means in practice when you have 40 sub-agents reporting to one master agent
- What the Orchestrate Agent Control Plane does and why cross-estate visibility matters more than per-agent optimization
- How enterprises are treating AI agents like digital employees — with identities, goals, managers, and performance reviews
- Why governance isn't optional in an agentic environment and what "governance light" looks like for organizations just getting started.
Guest Bio
Suzanne Livingston is Vice President of Product Management for IBM watsonx Orchestrate, IBM's enterprise AI orchestration platform. She leads the product team responsible for agent building, orchestration, evaluation, and the recently announced Orchestrate Agent Control Plane. Suzanne presented at IBM Think 2026 in Boston.
- IBM Think profile: https://www.ibm.com/think/author/suzanne-livingston
Resources Mentioned
- IBM watsonx Orchestrate 30-day free trial: https://www.ibm.com/products/watsonx-orchestrate
- IBM Think 2026 content: https://www.ibm.com/think
- Lopez Research blog: https://www.lopezresearch.com/research/
📢 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
🔍 ABOUT MARIBEL LOPEZ
Maribel Lopez is founder and principal analyst at Lopez Research, a technology research and strategy firm focused on enterprise AI. She advises CIOs, CDOs, CMOs, IT leaders and technology vendors on AI adoption, agentic systems, AI governance, and AI-driven customer experience. Her insights have been featured in mainstream TV and print media such as Bloomberg, CGTN, Marketwatch, Reuters, Wall Street Journal, and Yahoo Finance. She's also a contributor to Forbes.com, and her research is used by organizations navigating the gap between AI capability and enterprise deployment reality.
SEO Keywords I
Dell's CTO built a 4-category agent framework from real production deployments. Most enterprises are ignoring two of the categories that matter most.
Full Show Notes
Enterprise leaders are mapping AI agents to org charts — building digital employees, agentic teams, AI workers — and then wondering why the results fall short. Dell's Global CTO John Roese has been running agents in production long enough to know exactly why that framing fails, and what to do instead.
In this episode, Roese shares a framework Dell developed from actual production deployments, not pilots. It identifies four categories of AI agents defined by two dimensions: how much autonomy you grant the agent, and how complex the underlying process is. Most enterprises are focused on one category. Two of the four are widely overlooked — and they may represent the fastest path to measurable ROI.
This is a practical, grounded conversation about where agents are actually delivering value today, how to think about infrastructure cost in the context of agent economics, and why the sequence in which you deploy agents matters as much as which agents you build. If your organization is trying to move from AI experimentation to production, this episode is required listening.
3. Chapter titles:
- [00:00] — Introduction: Dell's dual role as tech vendor and enterprise AI user
- [01:38] — Why the org chart model for agents fails
- [03:12] — Decoupling human capacity from work capacity for the first time
- [04:23] — The two-by-two framework: autonomy vs. process complexity
- [06:14] — Productivity agents: what most enterprises already have
- [07:00] — Hygiene agents: the overlooked category that fixes foundational data problems
- [08:01] — The CRM data example: why every CRM is inaccurate and how agents fix it
- [10:05] — Latent infrastructure capacity: running agents in GPU white space to cut costs to cents
- [13:53] — Facilitation agents: removing entropy from complex cross-functional workflows
- [17:30] — The sequencing insight: hygiene and facilitation as the path to expert agents
- [19:24] — Why coordination agents aren't agentic bosses — and where human control actually lives
- [22:21] — Roese's closing advice: become literate, pick a few, get them into production
4. Guest Bio
John Roese is the Global Chief Technology Officer and Chief AI Officer at Dell Technologies, where he is responsible for technology strategy, AI deployment, and research and development across the company. He has held senior technology leadership roles at Nortel, Enterasys Networks, Broadcom, and EMC. At Dell, he operates at a rare intersection: leading AI strategy for a major technology vendor while also deploying AI internally at enterprise scale — which means his frameworks are tested against real production constraints, not just market positioning.
- LinkedIn: linkedin.com/in/johnroese
- Dell Technologies: dell.com
About This Podcast
AI with Maribel Lopez is a podcast for enterprise technology leaders navigating AI adoption, agentic systems, AI infrastructure, and AI governance. Host Maribel Lopez covers enterprise technology and advises CIOs, CDOs, CMOs, and technology vendors on how to move from AI experimentation to measurable business outcomes. New episodes published bi-weekly.
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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
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:
- Vinci: https://www.getvinci.ai
- Lopez Research blog: https://www.lopezresearch.com/research/
📢 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
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.
KEYWORDS
enterprise AI agents, agentic AI security, NemoClaw NVIDIA, OpenClaw enterprise deployment, AI agent governance, enterprise AI strategy, AI governance enterprise, agentic AI risks
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.
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:
- Read the full article: SaaS Isn't Dead. But Its Revenue Model Is Under Pressure — Lopez Research
- Referenced: Cathay Capital on agentic AI and B2B software
- Connect with Maribel on LinkedIn
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
KeywordsAI, 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
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
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
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.
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/
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Tags: #AI #Analytics #CustomerExperience #GenAI #AgenticAI #BusinessIntelligence #CXAutomation #DataDriven #DigitalTransformation #Verint
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
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
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 Cisco
- Nathan Jokel, SVP of Corporate Strategy and Alliances at Cisco
Recorded 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 reasons
- Wave 3: AI moving to the edge with physical and embodied AI requiring new infrastructure for robots and devices
From Deterministic to Probabilistic Computing
Vijoy explains the fundamental shift happening in computing:
- Traditional computing: deterministic, machine-speed but limited
- Human intelligence: agentic but slow
- New paradigm: AI agents with human-like behavior operating at machine speed and scale
The Internet of Agents
A collaboration platform where AI agents from different vendors can:
- Get discovered and authenticated
- Compose workflows together
- Execute tasks collaboratively
- Be evaluated for performance
Real-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 capacity
- Solutions:
- 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 infrastructure
- Focus on smaller, specialized models instead of racing for maximum parameters
Cisco's Specialized AI Models
- Foundation SAC 8B: 8 billion parameter model specialized for security policy
- Deep 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, collaboration
- Time/Risk: Higher-risk ventures that can't enter at Cisco scale initiallyCurrent Big Hairy Audacious Goals (BHAGs):
- Internet of Agents
- Quantum Internet - building quantum networks for distributed quantum computing
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."
SummaryIn 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.
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
In this episode, Maribel Lopez of Lopez Research interviews Kevin McInturff Chief Technology Officer of Logility. We explore the transformative impact of artificial intelligence on supply chain management and the key considerations for successful implementation. Our discussion covers critical insights for business leaders and practitioners navigating the AI landscape.
Key Discussion Points
Throughout our conversation, we delve into how artificial intelligence is fundamentally reshaping supply chain operations. The democratization of information through generative AI has opened new possibilities, though organizations continue to grapple with data integration challenges. We examine the critical balance between innovation and responsibility, particularly regarding ethics and data security in AI deployment.
The discussion reveals how real-world applications of AI are enhancing decision-making processes across supply chain operations. We explore emerging AI technologies that are revolutionizing forecasting methods, while acknowledging the ongoing evolution of ROI measurement for AI investments. Building trust in AI systems emerges as a fundamental requirement for successful adoption.
Our conversation emphasizes the importance of practical experimentation with AI solutions. Organizations must understand the interplay of different roles and technical languages in AI implementation. This approach allows companies to develop effective, tailored solutions while maintaining ethical considerations and data security.
Episode Resources
If you'd like to learn more about the topics discussed in this episode, follow me on social media at Youtube for the video version of this podcast and LinkedIN and X (Twitter) for AI research updates and insights.
Kevin McInturff Expert Bio:
Kevin McInturff, Chief Technology Officer of Logility, has 20+ years of experience in product and engineering roles. He spent his early career as an engineer on a plant floor working in industrial automation and plant information systems before moving into enterprise SaaS software. Under his leadership Logility has accelerated the pace of innovation and focused on delivering high quality product, a superior user experience and solutions that enable supply chain organizations to anticipate disruptions as opportunities to reap competitive advantages. He is passionate about understanding and meeting client needs with innovative solutions while building great engineering and product culture within his team.
Outside of his work with Logility he actively volunteers with the 501st Legion a non-profit who partners with other organizations to brighten the lives of the less fortunate and to bring awareness to positive causes on both a local and global scale. Kevin is a lifelong learner, an artist, and avid practitioner of the art of tsundoku.
He has earned a BS in Computer Science from the Georgia Institute of Technology, and a Masters of Science, Management of Technology from Georgia Tech Scheller College of Business. Kevin lives in Smyrna, Georgia with his wife and three daughters.
Maribel Lopez of Lopez Research hosted a podcast at AWS Reinvent, discussing QuickSight with ATracy Daugherty GM, QuickSight at Amazon Web Service and Travis Muhlestein, Chief Data and Analytics Officer at GoDaddy. QuickSight, a cloud-based BI tool, enables real-time data sharing and decision-making through dashboards, pixel-perfect reports, and Q for asking data questions. In the podcast, Muhlstein shares how QuickSight has transformed GoDaddy's approach from static dashboards to real-time, interactive data exploration and analysis, enabling more agile, data-driven decision-making across the organization.
Follow the guests at:
Maribel Lopez https://www.linkedin.com/in/maribellopez/
Travis Muhlestein, Chief Data and Analytics Officer at GoDaddy https://www.linkedin.com/in/travis-muhlestein/
Tracy Daugherty GM, QuickSight at Amazon Web Services https://www.linkedin.com/in/tracy-daugherty-28a1014/
Every industry, including the quick service restaurant (QSR) market, plans to transform its business with artificial intelligence (AI). Several years ago, Wendy's embarked on its AI journey, leveraging cloud services and generative AI to enhance employee and customer experiences. The drive-thru experience presents numerous challenges for QSR restaurants due to the complexities of menu options, limited-time offers, special requests, and ambient noise.
Wendy's chose to tackle the drive-thru experience with AI because 75 to 80 percent of Wendy's customers choose the drive-thru as their preferred ordering channel. The company saw a tremendous opportunity to improve the customer experience by creating a seamless ordering experience using AI automation in the drive-thru.
In an interview with Lopez Research, Wendy's CIO Matt Spessard shared how its AI program had advanced over the past year and shared advice for other leaders looking to tackle AI within their business.
At Amazon Re:Invent, Maribel Lopez met with several industry analysts to discuss their perspectives on what happened at one of the industry's premier cloud computing and AI trade shows.
https://reinvent.awsevents.com/
Episode Summary:
In this episode, Maribel Lopez speaks with Dell’s Chief Technology Officer and Chief AI Officer John Roese about Dell’s enterprise AI technologies from their development to their future. Roese explains how the initial magical thinking around artificial intelligence has shifted into a more practical approach that aims to maximize the benefits of each implementation. He also discusses the emerging trends and ideas that he is seeing in the AI space.
Key Themes:
Maribel and John start by delving into enterprise AI and AI markets in general. John explains the types of AI markets and how enterprise AI differs from other applications. The conversation then moves into the challenges of AI and the steps that Dell is taking to address them.
Next, John and Maribel reflect on the near-universal reactive approach that companies took to AI two years ago and how parts of that approach backfired or fizzled out. While this technology could have been approached better, its widespread use has provided companies with a real world understanding of LLMs and their applications. Now, companies take a more practical approach to AI while continuing to innovate.
The key innovation that Roese highlights is agentic architecture. This technology differs from previous generative AI applications because it can operate autonomously and is highly specialized. Individual “agents” can have job descriptions that they are trained for much like a human being, and they can interact with each other as a human team would.
For detailed show notes, navigate the episode using the time stamps below:
[1:26] Maribel introduces the guest of the episode, John Roese. Roese is the CTO and Chief AI Officer at Dell Technologies.
[1:59] The AI market is not a singular market – there is a traditional market, a training market, and an enterprise market. The enterprise market is very pragmatic in its applications.
[4:10] Maribel asks about the challenges businesses see in enterprise adoption. Early discussions of new AI technology treated it like magic. Now that we have real world use cases and a better understanding of the technology, Dell is able to have grounded conversations about AI applications with real impacts.
[7:48] Roesch explains the challenges that Dell is facing with AI. One of the challenges was determining where to prioritize as a company. Another is the process by which you develop application ideas. Dell had this issue when bringing ideas that were not fully formed to their legal team.
[11:44] No one got AI perfectly right. Almost universally, companies reacted at the technical level before looking at business priorities. Roese encourages companies to move toward a more thoughtful approach to AI technology.
[13:36] Dell learned that its goal was to add in the minimum sufficient AI structure to address the maximum use cases. In Dell’s case, half of their use cases were related to converting proprietary data into generative outcomes. Creating one model to handle all of these cases is the most efficient approach.
[15:05] Maribel asks Roese about the trends Dell is seeing in AI. Roese points to the emergence of agentic architecture. The idea behind agentic architecture is that they are autonomously performing agents with very specialized purposes. They can be combined much like a team of human beings.
Follow John Roese: https://www.linkedin.com/in/johnroese/
Learn more about Dell’s AI solutions: https://www.dell.com/en-us/shop/scc/sc/artificial-intelligence
Follow Maribel Lopez on X/Twitter: https://x.com/maribellopez
Subscribe to Maribel Lopez on YouTube: https://www.youtube.com/c/MaribelLopezResearch
Follow Maribel Lopez on LinkedIn: https://linkedin.com/in/maribellopez/
Episode Summary:
In this episode, Maribel Lopez speaks with Google Cloud Product Manager Bobby Allen about the current benefits and future possibilities of artificial intelligence in the context of Google’s AI services. They explore the flexibility of Googles services that sets them apart, the environmental impacts of LLMs in comparison with their predecessor NLMs, and how companies can take a human approach to AI to make peoples lives better.
Key Themes:
Maribel and Bobby begin by discussing Google’s AI services. Allen explains the wide variety of AI services offered by Google, which fall into three main categories: building AI, building with AI, and using AI. Most organizations are currently interested in using AI, and they have seen tangible benefits from doing so.
Bobby refers to these benefits as the “four I’s”: insight, increase, improvement, and innovation. Companies that adopt AI can see increases in productivity, gain insights into large documents through AI summarization, and more. Ai also has growing applications in compliance and query creation to analyze large datasets.
Last, Maribel and Bobby discuss the future of AI. Bobby points to a human-first future with a focus on the impacts of AI applications, including sustainability and marginalization. He believes that AI should solve real problems and male peoples lives better.
Follow Bobby Allen: https://www.linkedin.com/in/ballen-clt/
Learn more about AWS: http://aws.amazon.com/
Visit Maribel Lopez’s Website: https://www.lopezresearch.com/
Follow Maribel Lopez on X/Twitter: https://x.com/maribellopez
Subscribe to Maribel Lopez on YouTube: https://www.youtube.com/c/MaribelLopezResearch
Follow Maribel Lopez on LinkedIn: https://linkedin.com/in/maribellopez/
Episode Summary:
In this episode, Maribel Lopez speaks with Qventus co-founder and CEO Mudit Garg. Mudit explains how automation can help patients receive efficient care and hospitals maximize their performance. Learn how Qventus is helping hospital systems cut down their “excess days,” why efficiency is essential to care, and Mudit’s predictions for the future of AI in healthcare.
Key Themes:
Maribel begins the episode by speaking with Mudit about how Qventus is changing the hospital system for the better. Mudit explains that AI can be extremely helpful for hospital coordination. There are many cases in healthcare where the patient and the hospital system are aligned in their goals, like booking a surgery for a patient, but administrative complexities make those goals difficult to accomplish.
Qventus bases its system on two crucial components – behavioral science and machine learning. Machine learning is a great tool for determining patterns for coordination and scheduling, but factoring human behavior is crucial to create a model that actually works. Mudit credits the success of Qventus to the combination of these factors.
Maribel and Mudit also discuss the future of artificial intelligence in hospitals. Many industries are adopting AI in a wide range of applications, but Mudit suggests that healthcare systems should focus in on perfecting technology that benefits both patients and hospitals. He also notes his interest in Ai’s potential for data siloing, which would cut down administrative work.
Follow Mudit Garg on LinkedIn: https://www.linkedin.com/in/gargmudit/
Learn more about Qventus: https://qventus.com/
Visit the 3Blue1Brown YouTube Channel: https://www.youtube.com/@3blue1brown
Visit Maribel Lopez’s Website: https://www.lopezresearch.com/
Follow Maribel Lopez on X/Twitter: https://x.com/maribellopez
Subscribe to Maribel Lopez on YouTube: https://www.youtube.com/c/MaribelLopezResearch
Follow Maribel Lopez on LinkedIn: https://linkedin.com/in/maribellopez/
Episode Summary:In this episode, Maribel Lopez speaks with Roni Jamesmeyer about the changing role of AI in healthcare. Roni Jamesmeyer, the the Senior Healthcare Marketing Manager for Five9, has over twenty years of experience in IT sales, giving her an understanding of the complexity of healthcare delivery. She focuses on Five9's healthcare strategy to help health systems, payers, and life sciences move their contact centers to the cloud and close the gaps in patient communications. Maribel and Roni discuss technological advancements in AI, different uses of AI in healthcare, and Roni’s research findings.
Key Themes:Maribel and Roni open the episode by discussing the healthcare industry’s past attempts to improve the patient experience and how its goals have shifted. Currently, Roni is seeing healthcare companies working toward an omnichannel experience for their customers – meaning that they can interact over many communication channels.
AI is helping the industry move forward. Intelligent Virtual Agents (IVAs) improve operations in four major ways: security, patient experience, revenue generation, and reduced administrative backend work. Different companies may focus more on some of these categories than others, but all four functions are extremely important to the healthcare industry.
Roni also discusses her AI research findings. She found that AI tuning is crucial to improvement, allowing models to pick up and retain information. As these models are used, they become more personalized and more intelligent and can take on more work as a result. She also found that AI agents open up phone lines, allowing previously missed calls to be answered.
Visit Maribel Lopez’s Website: https://www.lopezresearch.com/
Follow Maribel Lopez on X/Twitter: https://x.com/maribellopez
Subscribe to Maribel Lopez on YouTube: https://www.youtube.com/c/MaribelLopezResearch
Follow Maribel Lopez on LinkedIn: https://www.linkedin.com/in/maribellopez/
Learn More About Five9: https://www.five9.com/
Download AI in Healthcare: How AI Drives Value for Five9 Customers: https://www.five9.com/resources/ebook/how-ai-drives-value-healthcare-customers#:~:text=Partners-,AI%20in%20Healthcare%3A%20How%20AI%20Drives%20Value%20For%20Five9%20Healthcare,customer%20surveys%2C%20and%20analyst%20insight.
Attend Roni’s Webinar with Exact Sciences: https://www.five9.com/registration/2024/exact-sciences-webinar
Follow Roni Jamesmeyer on LinkedIn: https://www.linkedin.com/in/roni-jamesmeyer-5733461/
Episode Summary:
In this episode, Maribel Lopez discusses generative AI with Dr. Sherry Marcus, the Director of Generative AI Sciences at Amazon Web Services. Her insights into the artificial intelligence needs of businesses gives her a unique perspective on the future of generative AI. Learn about the concept of agents in AI, why customers are moving toward the use of multiple models, and the ways AI might evolve in the future.
Key Themes:
Maribel and Sherry begin their conversation by discussing Amazon Bedrock, which is Amazon’s AI building service. The technology allows AWS customers to create their own AI models by offering choices of foundational models that can be customized.
Next, Sherry and Maribel discuss AI agents. In AI, Agents can retrieve real-time data to assist LLMs in providing information they cannot access in their training data. They also discuss how customers are currently using artificial intelligence, and why there is a shift away from specific modes and toward using multiple models for different use cases.
Dr. Sherry Marcus also explains how customers have historically used RAG (Retrieval Augmented Generation) to answer questions, and how that technology is evolving. Last, she explains why companies are using synthetic data to train their models, her predictions for the future of AI, and her favorite primer on AI.
Read What Is Chat GPT Doing… and Why Does It Work? by Stephen Wolfham: https://www.amazon.com/What-ChatGPT-Doing-Does-Work/dp/1579550819
Follow Dr. Sherry Marcus: https://www.linkedin.com/in/sherry-marcus-ph-d-4a4110/
Learn more about AWS: http://aws.amazon.com/
Visit Maribel Lopez’s Website: https://www.lopezresearch.com/
Follow Maribel Lopez on X/Twitter: https://x.com/maribellopez
Subscribe to Maribel Lopez on YouTube: https://www.youtube.com/c/MaribelLopezResearch
Follow Maribel Lopez on LinkedIn: https://linkedin.com/in/maribellopez/
In this episode, I interview Kevin McCartan, Senior IT Delivery Engineer at Musgrave, about how he leverages AI (Open AI's ChatGPT and Juniper Mist AI) to streamline network operations.
You can find a copy of the video here. https://www.youtube.com/watch?v=g1ZoMXn6c6M
Case studies here
Musgrave Stores Undergoes Transformation with AI-Native Network Enhancements
https://www.juniper.net/us/en/the-fee...
Three Steps on the AI Path to Retail Operational Zen
https://blogs.juniper.net/en-us/ai-na...
Episode Summary: In this episode, Maribel Lopez discusses responsible AI governance with Credo AI’s Head of Product Susannah Shattuck. Susannah builds AI governance tools that help organizations design, develop, and deploy ethical AI at scale. She has been working in Machine Learning Operations and AI governance for the last five years; her passion for AI governance can be traced back to her days on the IBM Watson implementations team, where she saw firsthand all of the things that can go wrong during the ML development lifecycle. Maribel and Susannah discuss the difference between responsible AI strategy and AI governance, the EU AI Act, and how she helps teams build governance plans that work for them.
Key Themes: Maribel and Susannah begin their conversation by discussing the risks of generative AI. Large language models have overlapping risks with older models such as algorithmic biases, but they also come with new risks such as hallucinations, privacy risks, and security risks.
Many companies want to implement AI, but those same companies recognize that they are not prepared for its risks. Susannah helps teams create AI governance plans that protect against risks without holding them back. She notes that it is not possible or practical to eliminate all risks, and that part of building a good strategy is allowing for low-risk use cases.
Later in the conversation, Maribel and Susannah dive into how Credo AI works with organizations to implement responsible governance. They also discuss the EU AI Act, which will shape AI governance in the years to come. Last, Susannah shares her predictions for the future of AI implementation and AI governance.
Visit Maribel Lopez’s Website: https://www.lopezresearch.com/
Follow Maribel Lopez on X/Twitter: https://x.com/maribellopez
Subscribe to Maribel Lopez on YouTube: https://www.youtube.com/c/MaribelLopezResearch
Follow Maribel Lopez on LinkedIn: https://linkedin.com/in/maribellopez/
Follow Susannah’s LinkedIn: https://www.linkedin.com/in/susannah-shattuck/
Follow Susannah’s Twitter: https://x.com/shshattuck?lang=en
Credo AI LinkedIn: https://www.linkedin.com/company/credo-ai/
Credo AI Twitter: https://x.com/credoai?lang=en
Episode Summary:
In this episode, Maribel Lopez speaks with Five9’s CTO and Head of AI Jonathan Rosenberg on AI’s potential in the Customer Experience (CX) and Contact Center as a Service (CcaaS) space. Currently, customers are often unsatisfied with chatbot communication services at contact centers. However, AI’s increasing generative capabilities show potential for exciting future applications.
Key Themes:
Maribel and Jonathan begin by discussing the current state of AI in CcaaS, and how many customers are unsatisfied with their experiences with automated calls. Contact centers have long attempted to automate aspects of the CX experience, first with DTMF and later with directed dialogue.
Unlike these technologies, generative AI has seen widespread adoption. Consumer familiarity with artificial intelligence will lead to them understanding how to interact with generative AI over the phone. Jonathan and Maribel also discuss their predictions for how AI will impact jobs in at contact centers. Jonathan believes that jobs will change, but they will not disappear.
Last, Jonathan defines open platforms and explains how their unique features allow for useful CX capabilities. The future of AI in CX depends on how quickly companies eliminate AI hallucinations. Once models overcome this obstacle, Jonathan predicts that AI will see wide adoption in the CX space.
For detailed show notes, navigate using the time stamps below:
Follow Jonathan Rosenberg on LinkedIn: https://www.linkedin.com/in/jonathanrosenberg1/
Follow Jonathan Rosenberg on X/Twitter: https://x.com/jjrosenberg?lang=en
Read Jonathan Rosenberg’s articles in the Forbes Technology Council: https://www.forbes.com/councils/forbestechcouncil/people/jonathanrosenberg/
Read the Broken Earth series: https://www.amazon.com/Broken-Earth-Trilogy-Season-Obelisk/dp/031652719X
Episode Summary: In this episode, recorded live at the IBM Think event in Boston, Maribel Lopez moderates a panel on AI governance with key figures from IBM and AWS. The discussion revolves around the current state and future of AI governance, the challenges and opportunities it presents, and the role of innovation and regulation in shaping responsible AI adoption.
Speakers:
Maribel Lopez, Lopez Research
Karthik Krishnan from Amazon SageMaker
Heather Gentile from IBM Watsonx.Governance
Kush Varshney from IBM Research.
Key themes:
This panel discussed the necessity for organizations to manage data across hybrid multi-cloud environments while ensuring robust governance is discussed. Gentile highlighted the strategic importance of AI governance for organizations aiming to align AI adoption with their ethics, culture, and values
The panel discussed how companies are shifting from siloed AI projects to enterprise-wide governance frameworks driven by generative AI innovations as well as the need to closely follow changes in the regulatory landscape. Krishnan discusses the collaboration between AWS and IBM Watson to integrate governance tools with AI and ML workflows. This integration aims to simplify risk management and regulatory compliance for customers using generative AI. Collaboration between AWS and IBM is seen Varshney shared insights on ongoing research in AI governance, particularly in addressing issues like hallucination in generative AI and developing algorithms for regulatory compliance.
This podcast was recorded as a LInkedIn Live on managing AI risk, governance, and explainability. We also talk about the EU AI Act's impact on the overarching global regulatory environment.
Bio:
Heather Gentile, Director of watsonx.governance Product Management, IBM Data and AI Software. (Learn more about Watson X here. https://www.ibm.com/watsonx)
Heather Gentile is Director of Product in IBM’s Data and AI software division. Heather focuses on opportunities to apply AI technologies to develop innovative solutions for IBM’s watsonx.governance and governance, risk and compliance portfolio. She works with organizations to enable responsible, transparent and explainable AI.
Heather has a passion for innovation and works with IBM’s technology and design teams to lead user experience workshops to solve for AI governance, risk and compliance challenges. Her experience expands across financial services, regulatory agencies, and other highly regulated industries.
Prior to joining IBM, Heather was responsible for overseeing the compliance analytics division of regulatory solutions at Wolters Kluwer. She received her undergraduate degree from Bryant University in Business Administration and her MBA from the University of Massachusetts.
Social Links
You can follow Heather at: https://www.linkedin.com/in/heathergentile/
You can follow Maribel at:
X/Twitter: https://twitter.com/maribellopez
LinkedIn: https://www.linkedin.com/in/maribellopez
YouTube: https://www.youtube.com/c/MaribelLopezResearch
In today’s episode, we delve into the transformative role of artificial intelligence in modern contact centers. We had the pleasure of speaking with Carmit DiAndrea, Director of AI and Data Management at NICE, who shared her invaluable insights on integrating AI to enhance customer and agent experiences.
Episode Highlights:
The Evolution of AI in Contact Centers: Understand how AI has transitioned from automating basic tasks to powering sophisticated, conversational interactions.
Special purpose AI vs. General AI: A discussion on how organizations need AI that's purpose-built for CX
Generative AI vs. Traditional AI: Discover the differences between these two AI types and their unique roles in improving customer experiences and operational efficiency.
AI's Impact on the Workforce: Carmit DiAndrea debunks common misconceptions about AI, highlighting new career opportunities emerging within the industry.
Bio:
Carmit DiAndrea, Director, AI Data Management, NICE
Carmit DiAndrea is a contact center, analytics, and AI expert with over 20 years of experience translating customer feedback, operational and contact center data into business strategies that transform organizations.
In her role at NICE, Carmit is responsible for leveraging NICE's significant interaction data assets to develop new Conversational AI and Generative AI-powered digital solutions, furthering NICE's dominance in the Digital Customer Experience space. Her previous experience includes leadership roles at Blue Shield of California, Verint Systems, Spectrum/Time Warner Cable, Concentra, and TPG TeleManagement.
Carmit is also an educator, teaching statistics, research methods, and algebra as an adjunct instructor at Kaplan and National American Universities.
Social Links
You can follow Carmit at: https://www.linkedin.com/in/carmitd/
You can follow Maribel at:
X/Twitter: https://twitter.com/maribellopez
LinkedIn: https://www.linkedin.com/in/maribellopez
YouTube: https://www.youtube.com/c/MaribelLopezResearch
In this episode, we dive deep into the world of Edge AI with a focus on the embedded edge market, featuring insights from Krishna Rangasayee, the founder and CEO of SiMa.ai. In this podcast we discuss the need to scale AI and ML technologies in the physical industrial realm, understanding what purpose-built AI looks like for the embedded edge market. We also talk about the potential of large multimodal models (LMM) in shaping the future of AI on the edge. Learn how these models promise enhanced accuracy and efficiency, along with reduced costs and power consumption. The episode wraps up with recommendations for further learning, including the book "Good to Great," which offers valuable insights into scaling and navigating the technology industry.
Bio
Krishna Rangasayee is Founder and CEO of SiMa.ai. Previously, Krishna was COO of Groq and at Xilinx for 18 years, where he held multiple senior leadership roles including Senior Vice President and GM of the overall business, and Executive Vice President of global sales. While at Xilinx, Krishna grew the business to $2.5B in revenue at 70% gross margin while creating the foundation for 10+ quarters of sustained sequential growth and market share expansion. Prior to Xilinx, he held various engineering and business roles at Altera Corporation and Cypress Semiconductor. He holds 25+ international patents and has served on the board of directors of public and private companies.
Social Links
You can follow Krishna at: https://www.linkedin.com/in/krishnarangasayee/
You can follow Maribel at:
X/Twitter: https://twitter.com/maribellopez
LinkedIn: https://www.linkedin.com/in/maribellopez
YouTube: https://www.youtube.com/c/MaribelLopezResearch
AI has the potential to revolutionize healthcare in areas that range from drug discover to the patient experience. In this podcast, Heather Lane from athenahealth shares the challenges and opportunities of using AI to improve the patient and clinician experience.
Heather's Bio:
Heather has a PhD from Purdue, where she focused on developing machine learning methods for the computer security problem of anomaly detection. She's worked at the MIT AI Lab (now CSAIL) working with Leslie Kaelbling on reinforcement learning and decision-theoretic planning, Markov decision processes, and the tradeoff between stochastic and deterministic planning.
In 2002, she moved to the University of New Mexico as an assistant professor in the Department of Computer Science. There she worked on a number of application areas of ML, including the bioinformatics of RNA interference, genomics, and computational neuroscience (inference of brain activity networks from neuroimaging data). Much of that work involved Bayesian networks and dynamic belief networks.
In 2008, she was promoted to associate professor at UNM and was granted tenure.
In 2012, she moved from academia to industry, joining Google in Cambridge, MA. working on Knowledge Graph, Google Books, Project Sunroof, and Ads Latency.
In 2017, she joined athenahealth to lead a Data Science team working to use athena's immense store of healthcare data to improve healthcare experiences for clinicians and patients.
Social Links
You can follow Heather at: https://www.linkedin.com/in/terranlane/
You can follow Maribel at:
X/Twitter: https://twitter.com/maribellopez
LinkedIn: https://www.linkedin.com/in/maribellopez
YouTube: https://www.youtube.com/c/MaribelLopezResearch
Hashtags: #AI, #Healthcare #PatientExperience
In this podcast we discuss how leading business create an AI mindset and how to overcome technical AI challenges.
About Arun Kumar:
Arun Kumar, EVP, Data & Insights at Hero Digital
With over a decade of experience delivering analytical customer experience solutions, Arun believes organizations need to combine technology at scale with the power of human insight and empathy to develop meaningful, relevant, and experience-based relationships with constituents. He has led teams for some of the top agencies in the world including Wunderman Thompson, and Publicis Sapient. Arun has helped build multi-channel touchpoints and direct-to-consumer strategies for brands like The American Red Cross, Bose, Carnival, Newell Brands, and TD Bank.
Social Links:
- LinkedIn: https://www.linkedin.com/in/kumararun/
- Hero Digital author page: https://herodigital.com/insights/author/arunkumar/
You can follow Maribel at:
X/Twitter: https://twitter.com/maribellopez
LinkedIn: https://www.linkedin.com/in/maribellopez
YouTube: https://www.youtube.com/c/MaribelLopezResearch
Hashtags: #AI, #BusinessStrategy #AIEthics #AIGovernance
Here is a link to the Hero Digital AI Readiness report: https://herodigital.com/insights/ai-readiness-report/
In this bonus podcast episode, I interviewed Juniper Networks' Chief AI Officer, Bob Friday, to discuss changes in AI and how AI impacts the networking industry. Mr. Friday also defines AIOps and what it means to deliver what Juniper Networks calls an AI-native platform.
Bob Friday’s Bio:
Bob Friday, Chief AI Officer and CTO, Juniper Networks
Bob is the co-founder of Mist Systems and currently serves as the Chief AI Officer at Juniper Networks and CTO of Juniper’s enterprise business following Juniper’s acquisition of Mist. Bob started his career in wireless at Metricom (Ricochet wireless network) developing and deploying wireless mesh networks across the country to connect the first generation of Internet browsers. After Metricom, Bob co-founded Airespace, a start-up focused on helping enterprises manage the flood of employees bringing unlicensed Wi-Fi technology into their businesses.
After Cisco’s acquisition of Airespace in 2005, Bob became the VP/CTO of Cisco enterprise mobility and drove mobility strategy / investments in the wireless business (e.g. Navini, Cognio, ThinkSmart, Phunware, Wilocity, Meraki) and product / industry innovation (e.g. CMX, Cleanair, HS2.0 / Passpoint, indoor location). He holds more than 15 patents.
Social Links
You can follow Bob at: https://www.linkedin.com/in/bobfriday/
You can follow Maribel at:
X/Twitter: https://twitter.com/maribellopez
LinkedIn: https://www.linkedin.com/in/maribellopez
YouTube: https://www.youtube.com/c/MaribelLopezResearch
Juniper Social Links:X/Twitter: https://twitter.com/JuniperNetworks
LinkedIn: https://www.linkedin.com/company/juniper-networks/
Facebook: https://www.facebook.com/JuniperNetworks
Hashtags: #JuniperNetworks #AI, #AIOps, #datacenter
Amazon Web Services held its annual cloud computing conference in Las Vegas at the end of November. In this podcast, Maribel Lopez recaps some of the key artificial intelligence announcements from her attendance at the event. There were key product launches that span AI chips, models and services. There is also a video recording of the podcast from the show floor that can be found on her YouTube channel at www.youtube.com/@MaribelLopezResearch
You can follow Maribel on LinkedIn at https://www.linkedin.com/in/maribellopez/
And on X at https://twitter.com/MaribelLopez
@awscloud #reinvent #GenAI #cloudcomputing
**It's the year of generative AI and every technology category changed as a result of access to new foundation models. In this podcast, I speak with Elastics's CPO Ken Exner about how enterprise search analytics and other categories, such as how security and observability are evolving.
About Ken Exner**
Chief Product Officer, Elastic
"**Helping customers gain actionable insights from data is increasingly important in a world of ever-increasing volumes of data. At Elastic, I have the privilege of leading our cross-functional product teams. Nothing is more exciting than seeing engineering, product, and design teams working in rhythm to deliver great experiences for our customers. I am passionate about building customer-oriented solutions that balance flexibility and ease of use, and I don’t believe customers should have to compromise for either."
Exner joined Elastic after three decades in various technology companies leading product and engineering teams. Most recently, he spent 16 years at Amazon Web Services (AWS), where he built and managed dozens of products used by millions of customers worldwide.**
He holds a bachelor of science degree from the Haas School of Business at the University of California, Berkeley. He and his family live on the outskirts of Seattle, where they spend time with their pets, which include dogs, cats, chickens, goats, and alpacas.
Follow Ken at https://www.linkedin.com/in/ken-exner-b914542/
Follow Maribel at https://www.linkedin.com/in/maribellopez/
About Elastic
Elastic is a leading platform for search-powered solutions. Elastic understands it’s the answers, not just the data. The Elasticsearch platform enables anyone to find the answers they need in real-time using all their data, at scale. Elastic delivers complete, cloud-based, AI-powered solutions for enterprise security, observability and search built on the Elasticsearch platform, the development platform used by thousands of companies, such as well-known brands Uber, Slack, Microsoft, and more than 50% of the Fortune 500.
Elastic is a platform for search-powered solutions that helps everyone — organizations, their employees, and their customers — find what they need faster, while keeping applications running smoothly, and protecting against cyber threats.
**The company offers three main product categories that include Elastic Enterprise Search, Observability, and Security solutions. Some of its customers include well known brands such as Uber, Slack, Microsoft, and thousands of others who rely on us to accelerate results that matter.
Follow Elastic at https://www.elastic.co/**
In this episode, we take on questions such as how do hallucinations in AI impact the CX space? Are developers still necessary in the age of AI and what advice Verint would offer to companies investigating AI for their customer experience projects.
Jaime Meritt serves as Verint's Chief Product Officer. Jaime joined Verint in 2015 and is a key player in defining the company’s product and technology strategy, and a driving force for cloud and AI initiatives.
You can follow Jaime’s on LinkedIn at: https://www.linkedin.com/in/jaime-meritt-90aa19/
You can follow Maribel at: http://twitter.com/MaribelLopez and https://www.linkedin.com/in/maribellopez/
Everyone talks about AI in the cloud. However to achieve the full business benefits of Artificial Intelligence, companies need to create a foundational software and hardware infrastructure that can support AI at the device level, edge and cloud. In this podcast, I speak with Intel's Sachin Katti about changes in the market to support AI anywhere and everywhere.
Bio: Sachin Katti is senior vice president and general manager of the Network and Edge Group (NEX) at Intel Corporation. He is responsible for driving technology and product leadership throughout the network to the intelligent edge.
Katti previously served as vice president and chief technology officer for NEX, a role responsible for technical strategy and vision across the group.
Prior to joining Intel in 2021, he served as the vice president of Telco & Edge Strategy at VMware, where he defined both the product and technology vision to capitalize on the cloudification of the network and edge.
Katti is a professor of electrical engineering and computer science at Stanford University. He is also the co-chair of the Technical Steering Committee for the O-RAN Alliance and was founding director of the xRAN Foundation before its merger with the O-RAN Alliance. Katti is co-founder and former CEO of Uhana (now part of VMware), which built a network AI platform to monitor and optimize mobile networks and applications. He previously co-founded Kumu Networks, which commercializes breakthrough research from his lab on full duplex radios.
Katti received his doctorate in electrical engineering/computer science from MIT in 2009.
In this podcast, I interview Kavitha Prasad. She is Intel's VP for Artificial Intelligence Strategy. We started the conversation by defining the various types of AI accelerators to deliver clarity. We move on to discuss the barriers to unlocking value from AI and advice on how to improve AI implementations. We close the podcast with what's new and exciting in the space.
Her bio.
As an experienced Business and Technology leader across a wide range of system applications, system architecture, business development, and go-to-market strategies, she has delivered competitive solutions to Cloud, Enterprise and Embedded markets.
Kavitha has a masterful track record of building high performance teams that consistently deliver high quality products at an aggressive cadence. As an established leader in the semiconductor industry, Kavitha’s broad hardware and software proficiency enables her to deliver multiple successful products in ASICs, SOCs, FPGAs and servers across multiple process nodes.
Best known for leveraging her passion for building authentic customer relationships and coupling it with her technical engineering expertise, Kavitha brings best-in-class AI and Machine Learning solutions in training and inference across edge to cloud.
You can follow Kavitha here:
https://www.linkedin.com/in/kavitha-prasad-2b38737/
Wendy's CIO shares his thoughts on building a next generation customer experience, and designing with cross-functional teams. Vaconi shares how cloud computing, mobility and AI provide the technical foundation for delivering on new business opportunity.
Relevant Blog: Wendy’s Unveils New Global Restaurant Design Standard
BIO:
Kevin VasconiChief Information Officer
Kevin joined Wendy’s as our Chief Information Officer in 2020. In this role, he is responsible for all aspects of Wendy’s global technology efforts, including Consumer-facing Digital, Restaurant Technology, Enterprise Architecture and Technology, and Information Security.
Prior to joining the Company, Kevin served as Executive Vice President, Chief Information Officer at Domino’s Pizza, Inc. At Domino’s, he served on the executive leadership team and was responsible for developing and leading all domestic and international technology capabilities. Emphasis on technology innovation helped Domino's achieve more than half of all global retail sales in 2019 from digital channels, primarily online ordering and mobile applications.
He has been recognized in the Top 10 of Nation’s Restaurant News’ Power List for his technology and innovation leadership, overseeing the team that spearheaded digital innovations that changed the pizza landscape and have served as inspiration for the broader QSR category.
Kevin has more than 30 years of technology experience across multiple industries including automotive, hardware, software and retail. His tenure prior to Domino’s includes service as CIO and VP of Engineering for the Stanley Security Solutions division of Stanley Black & Decker; SVP & CIO, R.L.Polk & Co.; and Chief Technology Officer for a number of business units and platforms within the Ford Motor Company.
You’ll find the Son of Baconator in his favorites on the Wendy’s mobile app.
Kevin received his B.S. in Technology from Purdue University.
In this podcast with Accenture's Christie Smith, we discuss the organizational learnings from the company's recent book Radically Human and the changes in required to successfully navigate the future of work.
Her bio:
Christie Smith is the global lead of Talent & Organization/Human Potential at Accenture. Christie joined Accenture in 2020 to lead and expand the transformation offerings that address the complex change management challenges facing CEOs, C-suites and boards, and assumed leadership of global Talent & Organization/Human Potential in March 2021. In 2022, she became Accenture’s Executive Pride Sponsor, representing and promoting LGBTIQ+ equality at the highest level of the organization.
Previously, Christie was with Apple where she was the global vice president for Inclusion & Diversity (I&D), I&D business partners and I&D solutions. She managed the global expansion of the function and the team in all regions and countries where Apple had offices and retail stores. Her leadership at Apple was integral to iconic products such as Memoji, iPhone Camera and iOS software in multiple languages. In addition, at Apple, she was instrumental in the growth of female and underrepresented minorities, expansion of scalable solutions in recruiting, global pay equity and compensation, and development of leaders.
Prior to her role at Apple, Christie served as interim CHRO at Grail, a start-up cancer detection company, and was a principal with Deloitte Consulting. As a national Human Capital leader at Deloitte, she was responsible for working with global clients on their talent management, organizational design, workforce planning, compensation strategies, I&D and HR technology solutions. In this role, Christie led large-scale technology implementations, change management, risk and communication strategies.
She also has deep expertise in CEO consulting around global talent strategies, along with CEO succession and executive team structure, management and capability. As a strategist, she led new product offering strategies for Deloitte in the United States and global markets, including the development of customer strategy in BRIC (Brazil, Russia, India and China) and EMEA (Europe, Middle East and Africa) countries. As part of her strategic framework, she leveraged deep data analytics to drive predictive socioeconomic and political factors that would impact business and market strategies.
Her leadership in business development and expansion includes being the west region managing principal of Deloitte Consulting, where she led growth in consulting, technology, human capital and strategy. She also led Deloitte’s west region Life Sciences industry practice. Christie was part of the leadership team that drove expansion of Deloitte Consulting into China and greater APAC. Christie also was responsible for the founding and leadership of the Deloitte University Centers for Inclusion and Community Impact.
She is a frequently sought-after speaker on leadership, strategy, I&D, and people analytics. She has been covered by The New York Times, Wall Street Journal, Harvard Business Review, Fortune, Forbes and CNN. Christie has been recognized as a Most Influential Woman in San Francisco (2012-2015), Women to Watch from Diversity Magazine (2012), Top 50 Diversity Leader (2020) and a leader in Fast Company’s Queer 50 list (2021). She is a member of the Committee of 200 (C200) and World 50 Women.
Her recommendations:
https://mastersofscale.com podcast
Michael Gervais on Finding Mastery
“Radically Human: How New Technology is Transforming Business and Shaping Our Future” by Paul R. Daugherty and H. James Wilson.”
In this podcast we discuss the intersection of digital identity, security and artificial intelligence.
Joe Burton, Chief Executive Officer, Telesign
Joseph Burton is the CEO of Telesign. Before Telesign, he served as CEO of Plantronics (now Poly) from 2016 to 2020, after joining the company in 2011 as Chief Technical Officer and then serving as Chief Commercial Officer. He started his career in 1990 as a software engineer, was acquired into Cisco in 2001 and served as Cisco's CTO for Unified Communications until 2010. Burton specializes in digital transformation, growth acceleration, corporate and go-to-market strategies, and has extensive expertise in technology and product development. He was most recently a Senior Advisor to the management consulting company McKinsey & Company. He holds a Bachelor of Science in Computer Information Systems and completed the Stanford Executive program at Stanford University.
You can follow Joe at https://www.linkedin.com/in/joe-burton/ and Maribel at https://www.linkedin.com/in/maribellopez/ and @MaribelLopez
In this podcast, I speak with Michelle Donnelly, the Chief Revenue Officer at Groq, about how advancements in AI accelerators are enabling companies to process workloads that were previously unimaginable.
Michelle's bio.
Michelle Donnelly, the Chief Revenue Officer at Groq, a startup known for accelerating systems with real-time artificial intelligence and high performance computing solutions. Michelle is a results-oriented executive with 20+ years of experience leading and operating distribution and go-to-market functions in hyper-growth environments.
Known to be customer-obsessed and a “growth hacker,” Michelle joined Groq from Salesforce where she led Retail and Consumer Goods sales organizations focused on cloud strategy, strategic partnerships, ecosystem development, and revenue growth. Under her leadership, her teams shaped customer demand, scaled for rapid growth, and exceeded revenue targets year-over-year.
When Michelle is not working, she is hanging out in San Francisco with her husband, her teenage daughter, and her pandemic puppy. Michelle has traveled the world and is rumored to have hung out with gorillas in the Congo, trekked the Himalayan mountain ranges, and studied yoga in India.
In this podcast, we talk about Groq's customer work, including the recent Army Validation Report conducted with Groq customer, Entanglement AI™, on their cybersecurity anomaly detection capability that runs on Groq hardware.
Report: https://apps.dtic.mil/sti/citations/AD1180411
Video: US Army Confirms 1000x Performant Cybersecurity Technology by Entanglement AI™ Run on Groq™ Hardware https://www.youtube.com/watch?v=oQ8V8kMxGfs
The book recommendation can be found here:
Turn the Ship Around! L. David Marquet https://davidmarquet.com/turn-the-ship-around-book/
You can follow Michelle at: https://www.linkedin.com/in/michelledonnelly/
You can follow Maribel at : https://www.linkedin.com/in/maribellopez/ & on Twitter @MaribelLopez
In this podcast, Sharon Mandell describes how AI helps her team at Juniper and Juniper Networks' IT customers to deliver better experiences. Links to information discussed in the podcast.
2022 Juniper Networks AI Report: https://juni.pr/3CyRdyu
Assessing AI Security Risk: https://www.microsoft.com/security/blog/2021/12/09/best-practices-for-ai-security-risk-management/
Cassie Kozyrkov’s AI Lectures: https://www.youtube.com/playlist?list=PLRKtJ4IpxJpB_2ei8-5eWU31EZ6uSj9_s
Bill Schmarzo’s Big Data MBA Series: https://www.youtube.com/playlist?list=PLaQqEd_Tx4Ep1bOIFEzDJNXI9r64rjhXL
Ethical Machines by Reid Blackman (Book): https://www.reidblackman.com/ethical-machines/
A Beginner Guide on Synthetic Data: https://towardsdatascience.com/synthetic-data-key-benefits-types-generation-methods-and-challenges-11b0ad304b55
The Real Promise of Synthetic Data: https://news.mit.edu/2020/real-promise-synthetic-data-1016
Bio.
Sharon Mandell is the Senior Vice President and Chief Information Officer leading Juniper’s global information technology team. In this role, she leads the ongoing enhancement of the company’s IT infrastructure and applications architectures to support the growth objectives of the company. She and her team are also responsible for showcasing Juniper’s use of its technologies to the world. Prior to joining Juniper in 2020, Mandell was the Chief Information Officer for TIBCO Software and previously developed her leadership strategy at Harmonic, Black Arrow (now Cadent), Knight Ridder and the Tribune Company. Throughout her career, Mandell developed a level of expertise in cyber security and compliance, enterprise architecture and road mapping, data and analytics, digital transformation and customer service. She is passionate about supporting women in STEM careers and in her free time Mandell serves on various arts and education related boards. She also proudly serves on the computer science advisory board at Temple University. Mandell holds a bachelor’s degree in computer science from Temple University and an MBA from the University of Chicago Booth School of Business.
https://www.linkedin.com/in/sharon-mandell-juniper/ @sharonomink
Peter Scott (AI leader and author of Artificial Intelligence and You) is an expert on all things AI, Peter Scott is on a mission to help us to get along with artificial intelligence. He has given TEDx talks, spoken to audiences as diverse as transformational leaders, executives, and British parliamentarians, and created a program to train coaches in helping clients become resilient to exponential disruption. A Master’s degree in computer science from Cambridge University led him to spend more than thirty years working for NASA’s Jet Propulsion Laboratory, helping advance our exploration of space. A parallel pursuit of the human development field as a certified coach positioned him to recognize and address technological disruption. The births of his children brought him into a mission, to help people understand, use, and advance AI for the betterment of all.
In 2020, Scott started the Artificial Intelligence and You Podcast, bringing together expert guests as diverse as politicians, CEOs, philosophers, developers, and artists to help audiences understand this incredibly complex thing called AI. He has recorded over 100 episodes and generated an audience of more than 1,000 listeners. Scott is also the founder of Next Wave Institute, an international educational organization teaching how to understand and leverage AI to thrive through technological disruption.
Tracy Pizzo Frey is the Founding Partner, Uncommon Impact Ventures. Founder, Restorative AI. She was a one time dancer, teacher, forest explorer, Googler and is currently a mom.
Uncommon Impact Ventures invests in technology solutions led by founders who share our values to create products, equity, and profits with integrity. To support them, it created a proprietary model of startup development that combines the best of VCs, incubators, and accelerators. Its holistic playbook de-risks a new venture to create a higher win ratio by providing companies with capital, infrastructure, and connectivity.
You can learn more about and follow Tracy at https://www.linkedin.com/in/tracy-frey/ and https://tracypfrey.medium.com
Alex Hagerup, the CEO of Vic.ai, shares the difference between using AI for automation versus autonomy in accounting.
About Alex:
Alexander is a serial tech entrepreneur with a strong passion for artificial intelligence. Prior to launching Vic.ai, he founded two other technology companies; his last one was funded by Northzone Ventures and later acquired by NASDAQ-listed J2 Global Inc., in 2014. He has a finance and accounting background and is a former board member of 24SevenOffice.com, the largest cloud accounting & ERP system in the Nordic region.
Where you can find him:
https://www.linkedin.com/in/alexanderhagerup/
https://www.linkedin.com/company/vic.ai/
https://twitter.com/VicDotAi
You can follow me at: http://twitter.com/MaribelLopez https://www.linkedin.com/in/maribellopez/ and http://www.lopezresearch.com
You can subscribe to the podcast on your favorite channel and the newsletter by visiting https://aiwithml.com
There are numerous challenges to working with distributed data. How do we secure, analyze and govern data in a hybrid cloud world? In this podcast, Michael Factor from IBM Research describes what a hybrid data fabric is and how it helps companies gain value from data in distributed locations.
Michael Factor's bio.
Dr Factor is an IBM Fellow with a focus on cloud data, storage and systems. He has a B.Sc., Valedictorian (1984) in Computer Science from Union College, Schenectady, NY. M.Sc. (1988), M.Phil. (1989) and Ph.D. (1990) in Computer Science from Yale University. Since graduating, Dr. Factor has worked at the IBM Research -- Haifa.
His current main focus area is hybyrd cloud data. Among his responsiblities is as a global lead for all work on Hybrid Data form IBM Research. In this role, he and the global team are defining future directions to ensure 1) it is easy to get the right data for a task, 2) that data is always used in a secure and governed fashion and 3) that IBM has high-performance, secure, highly-functional and cost efficient data stores and processing engines. In addition, he serves as the main focal point in moving IBM Research innovations from the Lab into the IBM public cloud where his team has contributed to services such as IBM Cloud Object Storage, IBM SQL Query Service, and various Spark related services. Beyond his Research efforts, he also works closely with both the IBM Public Cloud and the IBM Data and AI team to provide guidance and expertise on directions such as serverless computation, data lakes and future enhancements to object storage.
You can follow Michael's research here.
You can follow me on Twitter @MaribelLopez and on LinkedIn here.
Machine Learning Operations (MLOps or ML Ops) is a set of practices that aims to deploy and maintain machine learning models in production reliably and efficiently, as defined in various publications. In this podcast we take on the topic of MLOPs. What is it and is it like DevOps for AI? Turns out it’s broader than you might think including everything monitoring to governance and explainability. Adewumni shares why it's both necessary and exciting.
For her 30 second recommendation, Ade shared the Cloudera Fast Forward Labs blog which can be found here. She also mentioned a report by the Algorithmic Justice League on bug bounties for algorithmic harms which can be found here.
You can follow Ade on Twitter @Adewunmi and @FastForwardLabs . You can also find her on Medium medium.com/@adeadewunmi and LinkedIn here.
You can follow me on Twitter @MaribelLopez and on LinkedIn here.
In this podcast we talk about automation trends including AI-enabled low code automation, API management, and how to deal with unstructured data.
Machine learning and cybersecurity are tied at the hip. In this podcast, Chris Pedigo, the Go-to-market CTO for Lacework, discusses key trends, common misconceptions and advice for navigating a rapidly evolving security market.
You can follow Chris at https://www.linkedin.com/in/chrispedigo/
You can follow Maribel at http://twitter.com/MaribelLopez and https://www.linkedin.com/in/maribellopez/
You can subscribe to the podcast on your favorite channel and the newsletter by visiting https://aiwithml.com
About Lacework
Lacework is a security company for the cloud. The Lacework Polygraph® Data Platform automates cloud security at scale. It collects, analyzes, and correlates data across an organization’s AWS, Microsoft Azure, Google Cloud, and Kubernetes environments, and narrow it down to the handful of security events that matter. It was founded in 2015 and is headquartered in San Jose, California. Learn more at www.lacework.com.
How can computer vision and machine learning change retail? In this podcast, I interview Richard Schwartz of Pensa Systems. The company offers an automated retail shelf intelligence solution that uses patented computer vision and artificial intelligence to scan all products and categories within a store. In this podcast, we talked about how AI can help retailers with instantaneous access to actual shelf inventory conditions, enabling them to improve sales, optimize labor and deliver better shopping experiences.
In this podcast, I speak with Afsana Akhter about how Amelia Virtual care uses newer technology such as augmented reality, virtual reality and artificial intelligence to help individuals overcome fears from their homes and at professional facilities.
Her bio
Afsana Akhter, CEO of Amelia Virtual Care
With 20+ years of experience across Tech and Digital Health, Afsana Akhter is driving the expansion and adoption of Amelia Virtual Care’s VR platform for mental healthcare. Afsana has held commercial leadership roles at Livongo, Prealize Health, and Medullan. Afsana holds BS and MEng degrees in E.E.C.S. from MIT.
You can follow her at:
https://www.linkedin.com/in/afsana-akhter/ & @AfsanaNow
You can follow me on Twitter @MaribelLopez & LinkedIN https://www.linkedin.com/in/maribellopez/
AI Automation is a hot topic in enterprise IT circles but getting it right requires more than a set of Robotic Process Automation tools. In this podcast, I speak with Anisha Biggers from NTT DATA Services on the how and why of automation. We discuss topics such as return on value versus return on investment.
Were to find us:
You can follow me on Twitter at http://twitter.com/MaribelLopez and LinkedIN at https://www.linkedin.com/in/maribellopez/
You can follow Anisha at https://www.linkedin.com/in/anishapbiggers/
Whether it's the virtual assistant on your phone, a chatbot in your mobile banking app or on a website, we've all experienced the good and the bad of virtual agents. Everytime there's a negative experience we blame the technology. Yet conversational AI interfaces have reinvented the way we interact with the world. In this podcast, Don White, the CEO of Satisfi Labs, shares his takes on this topic. We discuss where AI fits and how to build success AI assistants by focusing on specific tasks. To mix things up, I also asked Don for his opinions on the meta verse.
You can follow
Don White on Twitter at @TheDonnyWhite and @satisfi
On LinkedIN at
https://www.linkedin.com/in/don-white-he-him-9a9bb69
Maribel Lopez on Twitter at @MaribelLopez
On LinkedIN https://www.linkedin.com/in/maribellopez/
Website for Don https://satisfilabs.com
Book mentioned - Only the Paranoid Survive: How to Exploit the Crisis Points That Challenge Every Company By Andy Grove - An oldie but a goodie!
In this episode, Dr Flores shares the opportunities for AI and federated learning . She discusses examples in healthcare including, Gatortron, the largest clinical language model.
About Dr Flores.
Mona G. Flores, M.D. - Global Head of Medical AI at NVIDIA
Dr. Mona G. Flores is the global head of medical AI at NVIDIA, where she oversees AI initiatives
in medicine and healthcare to bridge the chasm between those industries and technology.
Dr. Flores first joined NVIDIA in 2018 with a focus on healthcare ecosystem development.
Before joining NVIDIA, she served as the chief medical officer of digital health company Human-
Resolution Technologies, following over 25 years working in medicine and cardiothoracic
surgery.
Dr. Flores received her medical degree from Oregon Health and Science University. She
completed a general surgery residency at the University of California, San Diego, a postdoctoral
fellowship at Stanford, and a cardiothoracic surgery residency and fellowship at Columbia
University.
Dr. Flores also has a master’s degree in biology from San Jose State University, and holds an
MBA from the University at Albany School of Business. She initially worked in investment
banking for a few years before pursuing her passion for medicine and technology.
Where to follow us:
Maribel Lopez on Twitter at @MaribelLopez and LinkedIN https://www.linkedin.com/in/maribellopez/
You can find Mona on Twitter @Monagflores and @NVIDIA
You can find here on LinkedIN at https://www.linkedin.com/in/monagflores/
You can find more information on GatorTron here. https://www.nvidia.com/en-us/on-demand/session/gtcspring21-s32030/
Heidi Williams of Grammarly shares thoughts on the differences between building a Grammarly for consumers versus businesses. She also shares how the company approached creating inclusivity in AI.
Heidi’s Past podcast interviews:
How to Handle Hypergrowth w/ Grammarly's Heidi Williams
Short Byte: Heidi WIlliams - Building a startup within a startup
Past speaking:
https://events.bizzabo.com/349632/agenda/speakers/1341894
Social Links:
Heidi Williams biography
Heidi Williams is Head of Engineering for Grammarly Business, our newest product offering for professional teams and organizations.
At Grammarly, Heidi is inspired by the potential impact the product can have as a platform, with the opportunity to help reduce conflicts and misunderstandings in communication and educate people on how to be more inclusive and equitable.
Before coming to Grammarly, Heidi served as VP of Platform Engineering at Box, founded WEST Diversity and Inclusion, and was co-founder and CTO of tEQuitable, a confidential platform addressing issues of bias, discrimination, and harassment in the workplace. Heidi was at Adobe for 17 years and most notably was a founding engineer on Dreamweaver, which democratized web development in the late 1990s.
Heidi volunteers as a technical advisor for PaymentWorks, Raise For Good, and CaregivingHQ and is a mentor for FastForward.org’s tech nonprofit accelerator program. Her expertise and perspective have been featured in Built In SF and the podcasts Stayin’ alive in Technology, Dev Interrupted, and CTO Connection.
As a lifelong soccer player, Heidi’s often on the pitch; she’s also an avid hiker, bicyclist, and kayaker. She once hiked with her husband across England, 192 miles coast to coast (with B&Bs and pub stops along the way).
Heidi studied at Brown University, where she earned a BS in computer science. She also attended Stanford University’s Executive Institute.
In this episode, Maribel Lopez of Lopez Research shares her thoughts on four components required to create better AI strategies.
You can find an article I wrote about this at https://cloud.google.com/blog/topics/transformation/4-tasks-to-ensure-your-companys-ai-is-ethical You can find links to the research I mentioned herehttps://www.fico.com/en/solutions/fico-responsible-aihttps://www.pewresearch.org/internet/2021/06/16/experts-doubt-ethical-ai-design-will-be-broadly-adopted-as-the-norm-within-the-next-decade/
You can follow me on Twitter at @MaribelLopez and LinkedIN https://www.linkedin.com/in/maribellopez/
Please visit the website to for links to follow the show and to subscribe to the newsletter at
https://aiwithml.com
Alice Xiang is the Head of AI Ethics Office the AI Ethics Office in Sony Group Corporation, and also leads the AI Ethics Research Flagship in Sony AI. She joined Sony AI after working as the Head of Fairness, Transparency, and Accountability Research at the Partnership on AI. A lawyer and statistician by trade, Alice’s work sits at the intersection of social justice and AI. Alice is recognized as one of the 100 Brilliant Women in AI Ethics. In this podcast we discuss the discuss the growing focus on AI Ethics among technology companies.
Follow us on social at:
Alice Xiang
https://twitter.com/alicexiang and https://www.linkedin.com/in/alice-xiang-3832aa18/
Maribel Lopez
https://twitter.com/MaribelLopez & https://www.linkedin.com/in/maribellopez/
For advanced show notes, articles and research, please subscribe to my weekly podcast newsletter at http://eepurl.com/hWzLu9
Hillary Ashton is the Chief Product Officer for Teradata. She leads the global products organization, a diverse team responsible for innovation, product management, engineering and quality. Recently she's focused on Teradata's the hybrid multi-cloud platform. In 2019 she was named to the National Diversity Council’s (NDC) annual list of the Top 50 Most Powerful Women in Technology. In this episode we discuss data gravity, how to think about balancing cost versus analytical performance and focusing on outcomes versus the technology. Ashton also provides advice and tips for individuals seeking a career in data.
Follow us on social at:
https://twitter.com/Hillary_Ashton and https://www.linkedin.com/in/hillaryashton/
Maribel Lopez
https://twitter.com/MaribelLopez and https://www.linkedin.com/in/maribellopez/
For advanced show notes, articles and research, please subscribe to my weekly podcast newsletter at http://eepurl.com/hWzLu9
Dr. Carlotta A. Berry is a Professor in the Department of Electrical and Computer Engineering at Rose-Hulman Institute of Technology. She is also the 2021-2024 Dr. Lawrence J. Giacoletto Endowed Chair for Electrical and Computer Engineering.
Her research interests are in robotics education, interface design, human-robot interaction, and increasing underrepresented populations in STEM fields. She has a special passion for diversifying the engineering profession by encouraging more women and underrepresented minorities to pursue undergraduate and graduate degrees. She feels that the profession should reflect the world that we live in in order to solve the unique problems that we face.
In this podcast, Dr Berry shares her journey in robotics and how she approaches robotics education. She also shares her insights and strategies on eliminating artificial intelligence bias in robotics and creating a more diverse AI field.
Links to her recommendations from this podcast.
https://blackinrobotics.org
https://blackinengineering.org
Social handles for these organizations @BlackInRobotics @BlackInEngineering as well as @BlackandSTEM
Her book Mobile Robotics for Multidisciplinary Study can be found on Amazon
Follow us on social at:
Dr. Carlotta Berry
https://www.noiresteminist.com
https://twitter.com/DrCABerry & https://twitter.com/NoireSTEMinist. @DrCarlottaBerry
Maribel Lopez
https://twitter.com/MaribelLopez & https://www.linkedin.com/in/maribellopez/
For advanced show notes, articles and research, please subscribe to my weekly podcast newsletter at http://eepurl.com/hWzLu9
Natural Language Processing in AI isn't a new field, but it's advanced rapidly since 2019. Are we at the human-level of understanding with NLP and what can be done today? Models trained on generic data sets often fail to retrieve the right information for businesses. Today, technology companies are developing solutions that allow enterprises to extract meaningful insights from textual data. In this podcast Shila Ofek-Kiofman, the Director of Language Technologies for IBM Research AI, shares what's happening in NLP research and how it will help companies create better models using business-specific terms. You can follow Shila at https://www.linkedin.com/in/shila-ofek-koifman-1660701/