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.

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**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.

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**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]

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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