AI at Scale

"AI systems seem to behave differently, think differently than human beings do, which means they need to be managed differently."-  Arti Garg, Chief Technologist at AVEVA

When industrial AI leaves the screen
As industrial AI moves into factories, power grids and critical infrastructure, there’s a lot more to consider than what you see on the screen.  
How do you make industrial AI more capable and more responsible at the same time? That's the big question in this episode of the AI at Scale podcast. Tom Kroczak talks with Arti Garg, Chief Technologist at AVEVA – an astrophysicist by training who has worked across science, technology, business and public policy. Her take: getting industrial AI right takes more than a smart model.

“You need visibility into what your AI systems are doing.” 
— Sam Crowder, Head of Product at LangChain 

The challenge of managing AI agents at scale 

Building an AI agent demo is one thing. Moving it into production at scale is another. There, it interacts with real users, enterprise data and business systems. As AI agents perform tasks and workflows, one question becomes critical: What does it mean to own your AI? 

In this episode of the AI at Scale podcast, Tom Kroczak speaks with Sam Crowder, Head of Product at LangChain. They discuss how to move AI agents beyond experimentation with observability and informed choices. The goal: reliable, real-world applications. They also explore evaluation and the future of technology  as agentic AI scales.

Artificial intelligence is moving from ambition to everyday business operations. Yet many organizations still struggle to turn AI investments into measurable results.

In the latest episode of the AI at Scale podcast, KJ Kusch, Global Field CTO at WalkMe, joins Tom Kroczak to discuss one of the biggest barriers to AI transformation: adoption.

Drawing on her work with global organizations, KJ introduces the concept of the “AI Theater”: AI that looks successful on paper but fails to deliver sustained employee adoption or business impact.

"AI is not replacing quality expertise. It is helping us scale learning, scale expertise, and turn insight into action faster."

In this episode of the AI at Scale Podcast, Brigitte Begasse, Senior Vice President of Customer Satisfaction and Quality at Schneider Electric, explores what AI really changes in two areas where the stakes are high: quality and customer experience.

In this episode, you will learn:

✔ why quality is a promise, not just an inspection step
✔ how AI helps predict and prevent risks earlier, not just detect them
✔ why data needs the right models and ontology to create trusted insight
✔ how AI scales expert knowledge instead of replacing experts
✔ why transformation is about workflows and ownership, not just tools
✔ what predictive quality looks like — and why it matters for customers

Listen to the AI at Scale podcast.

“AI is often framed as a software revolution. But the real constraint isn’t software. It is whether we can power and cool it at scale". 

In this episode of the AI at Scale Podcast, Chris Sharp, Chief Technology Officer at Digital Realty, and Steven Carlini, Thought Leadership Leader at Schneider Electric, explain a fascinating shift in the data center industry. AI is not only about AI models or chips. As data centers are becoming AI factories success depends on physical systems that must keep up.

There is a clear message on planning: decision makers need to design for what is coming, not what exists today. Building only for current demand will not hold up.  

For C-level executives, this is a practical view of where AI can slow down and what needs attention now. 

In this episode, you will learn: 

✔ why power and cooling must be designed as one system to support AI innovation 

✔ how rising power density is changing data center design 

✔ what token economics means for cost, value, and ROI 

✔ why planning ahead is critical to avoid future limits 

✔ how infrastructure decisions affect AI performance and growth