Episode 2: Schneider Electric's AI Center of Excellence Addresses the Sustainability Paradox

Author photo: Colin Masson
ByColin Masson

The second in ARC’s Industrial AI Podcast Series features a conversation between Colin Masson, ARC Advisory Group's Director of Research for Industrial AI, and Schneider Electric Chief AI Officer, Philippe Rambach, as they discuss more than 50 years of experience applying AI techniques to industrial use cases.

Listen to Colin and Philippe discuss how Schneider Electric has dramatically ramped up their internal AI resources with almost a tenfold increase in dedicated AI staff since Philippe's appointment as Chief AI Officer two years ago.

Philippe shares some examples of how Schneider Electric has been applying AI techniques over several decades to tackle energy optimization use cases, and how they consider efficiency, cost, and environmental impact before selecting the appropriate AI tools and techniques needed for each use case.

Tune in to the podcast here:


Podcast topics and highlights

AI adoption and deployment at scale. Philippe, Chief AI Officer at Schneider Electric, discusses his role and experience in the field. Philippe explains that Schneider Electric has been working on AI since the early 80s, but two years ago they decided to accelerate AI investments to increase value to customers and employees. They moved from innovation pilots to deployment and scaling of AI solutions, involving a hub-and-spoke model and bringing together diverse teams. Schneider Electric appointed Philippe as its first Chief AI Officer in 2021, reflecting the company's visionary approach to AI and its potential applications. Schneider Electric has built up an AI team with expertise in scientific and mathematical aspects to address complex problems that cannot be solved by standard software engineers.

Industrial AI use cases for industrial automation and energy optimization. Colin and Philippe discuss the importance of focusing on business value for industrial customers, highlighting pragmatic use cases that deliver value today. Schneider Electric has prioritized energy optimization and electrification enablement use cases for industrial AI. AI is already optimizing energy consumption and reducing energy bills for Schneider Electric's customers.

Optimizing energy consumption and modernizing service experiences using Industrial AI. AI can optimize energy consumption in large buildings by learning patterns of energy use and forecasting future consumption, allowing for more green energy use and reduced carbon emissions. AI can help large factories use more green energy by optimizing energy use and purchasing energy from the grid when it is cheapest and greenest, reducing overall carbon emissions. Optimizing energy use can extend asset lifetime value. AI can improve industrial services by predicting asset failure and reducing the need for on-site visits, resulting in cost savings and increased safety. Schneider Electric is using AI to detect potential electrical failures and predict the risk of fires, providing a new service to customers.

Addressing the Sustainability AI 'Paradox'. Colin and Philippe discuss the ethical considerations of using AI for access control, addressing privacy and face recognition concerns. He raises the paradox of using AI to solve sustainability problems while also consuming energy and seeks opinions on this topic. Colin asks Philippe if there is a potential sustainability paradox of using Generative AI Foundation Models and Large Language Models to save the planet, while they consume massive amounts of energy and water, and have large carbon footprints. Philippe and Colin discuss using the right AI techniques and tools for each energy optimization solution, and applying Generative AI judicially to achieve sustainability goals, with a focus on cost-benefit analysis and carbon savings.

Closing: Industrial AI's will have huge impact on manufacturing a more sustainable future! Colin emphasizes the importance of addressing the skills gap in manufacturing and industrials with Industrial AI, while Philippe highlights the need for AI to complement human capabilities rather than replace them.





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