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/

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🔍 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.

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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.

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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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📢 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


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