AI at Scale

Can AI really spot risks faster than any sensor? How far are we from the autonomous supply chain?  In this episode of the AI at Scale podcast, Mike Labhart, North America Global Supply Chain Director for Smart Operations & Data Analytics of Schneider Electric, walks us through the most valuable AI use cases in manufacturing and what’s next in smart factories. 

What you will learn from this conversation : 
 
- how AI detects safety risks earlier than traditional sensors, from smoke to missing Personal Protection Equipment (PPE), 

- how deep‑learning and image recognition improves quality and helps to identify ergonomics risks across dozens of sites, 

- how machine learning prevents costly downtime thanks to predictive maintenance, 

- why clean, consistent data naming is key to scaling AI across factories,  

- what agentic AI means for the future of manufacturing, 

- why autonomous supply chains are within reach now. 

 

Get a unique chance to hear directly from a seasoned smart factory leader on what it means to build AI that powers the next generation of factories.

What does it take to lead an AI company? In this episode of AI at Scale, we sit down with Katya Lainé — CoFounder & CEO of TALKR.ai, President of the AI Commission at Numeum, CoFounder and VicePresident of Le Voice Lab, and CoFounder of OpenLLM France — to get a unique glimpse into the daily life of an AI company founder.

 
For any leader navigating business strategy and AI implementation, this conversation offers rare clarity on what truly drives AI success inside enterprises: being prepared to change processes and ways of working. 



What will the future demand from those at the forefront? 



Learn from this conversation. 



What you will learn:



How AI companies can innovate when data, tools, and ecosystems are limited 



How leaders balance product, ethics, and execution in customer‑facing AI 



What an AI founder’s day really looks like behind the scenes 



How voice‑AI transforms customer journeys and internal processes 



What skills and mindsets future AI leaders must build now

"We often discuss the green transition and digital transformation in isolation, without systematically mapping where these skill sets actually overlap, or more importantly, where they don't." says Siddhi Pal, Senior Policy Researcher at Interface. In this episode of the AI at Scale podcast Siddhi explains why the combination of AI and green skills — named “twin transition skills” — is emerging as one of the most valuable capabilities in today’s economy. 

In this episode, you’ll learn: 

why defining and measuring green skills is critical for future workforce planning, 

which green skills are common among AI professionals, and which are still rare, 

how companies can balance internal reskilling with strategic external hires, 

what actions can help close the gender gap and expand talent pools. 

If you’re planning for growth in an era of climate action and AI acceleration, this episode delivers insights you can act on today.

“AI will always keep the human at the center, it’s a guidance tool, not a replacement,” says Kim Custeau, Executive Vice President at AVEVA. What does that mean for the future of industrial operations? In this episode of AI at Scale, Kim shares how AI is moving beyond hype to become a practical enabler of efficiency, resilience, and sustainability.

In this episode, you’ll learn: 



Why AI adoption should begin with clear use cases. 



How embedded AI assistants democratize access to trusted data and preserve critical knowledge for new employees. 



How AI-driven automation of complex tasks boosts efficiency while keeping humans in control. 



Practical steps leaders can take today to prepare their teams for dynamic, personalized AI-driven operations.

AI is just a tool, but when strategically deployed, it can become the key to more efficient future of energy and industry. In this special episode of AI at Scale, recorded live at Innovation Summit Copenhagen, Schneider Electric’s Chief AI Officer, Philippe Rambach, shares how the company innovates with AI by implementing it across the entire portfolio. This deployment at scale is focused on solving real-world problems: optimizing energy consumption, extending asset lifecycles, and empowering engineers with intelligent tools that boost productivity and reduce operational workloads.