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.lopez

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

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

Episode Summary
Agentic AI dominated industry conversation in 2025. But in 2026, enterprise leaders are asking a harder question: How do we deploy AI agents safely, accurately, and in production environments?
In this episode, Maribel Lopez speaks with Peter Cousins, CTO of WorkFusion a UiPath company, about how AI agents evolved from RPA and intelligent automation into production-ready “digital workers.” The discussion focuses on regulated industries, where explainability, auditability, and risk controls matter as much as automation gains.
Rather than hype, this conversation explores what it takes to operationalize AI agents: governance frameworks, confidence thresholds, human oversight, and model risk management.

Sound Bites

• "2025 was the big agentic AI year."
• "You can't just throw it in and it's good to go."
• "It's been great talking to you."

Chapters

00:00
Introduction to Agentic AI and Work Fusion

02:00
Transitioning from RPA to AI Agents

04:38
Operationalizing AI Agents in Business

09:21
Navigating the Hype of Agentic AI

12:04
The Role of LLMs in Regulated Environments

14:47
Multi-Agent Orchestration and Collaboration

17:21
Improving AI Agents through Learning

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

24:06
Closing Thoughts on Adopting Agentic AI

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