Tag: Schneider Electric

  • Schneider Electric CAIO Philippe Rambach on Scaling AI with a Business-First Strategy and Critical Thinking

    Schneider Electric CAIO Philippe Rambach on Scaling AI with a Business-First Strategy and Critical Thinking

    In a wide-ranging interview, Philippe Rambach, Chief Artificial Intelligence Officer at Schneider Electric, shares how the global energy management and automation company is scaling AI across more than 160 factories and 140 countries. The key, he explains, is a disciplined business-first approach that prioritizes real-world value over technological novelty.

    Business Value Over Technology

    Rambach warns against “technology tourism”—the temptation to run hundreds of pilots without a clear business case. Instead, Schneider Electric starts by identifying a specific business impact, then asks if AI can help. Each use case passes through gate reviews that test both technical feasibility and business value. Projects are halted immediately if either criterion fails.

    “We do a pilot that keeps in mind what we want to deliver at scale,” Rambach notes. This rigor prevents “pilot purgatory” and ensures that lab successes translate to real factory results.

    Agentic AI and the Sera Assistant

    Schneider recently launched Sera, an AI agent that transforms how users interact with environmental data. However, Rambach cautions against overhyping generative AI. “Generative AI agents do not replace forecasting, anomaly detection, prediction or optimisation—we still need that,” he asserts. Sera adds a conversational layer, allowing operators to ask complex questions in natural language, moving beyond rigid dashboards.

    Critical Thinking as a Core Skill

    To counter blind faith in AI outputs, Schneider has introduced an “AI for All” training program with the same institutional rigor as safety training. The centerpiece is critical thinking. “We want people to keep critical thinking when AI gives an answer,” Rambach says, acknowledging that AI can be wrong or partially wrong. The company also hosts “promptathons” and a virtual “Genius Bar” for employees to share prompting strategies.

    Edge vs. Cloud: A Matter of Physics and Policy

    Deciding where to run AI depends on data sovereignty and latency. In industrial settings, sensitive data often must stay local, and real-time applications like visual inspection require edge processing for speed. Schneider’s solutions are designed to be versatile enough for both environments.

    Addressing the Energy Density Crisis

    Schneider Electric, which builds data center infrastructure, is acutely aware that AI training consumes vast energy. Rambach notes the irony: AI is part of the solution. The company uses AI to optimize power management for its own factories and partners like NVIDIA to precisely manage energy in AI factories. He also advocates treating AI as an “unreliable component,” building reliable systems with human-in-the-loop checks, as in customer care where AI drafts responses but humans verify them.

    Navigating Regulation and Ethics

    Operating under the EU AI Act, Schneider has published an external Trust Charter that explicitly bans facial recognition. Rambach says compliance with the Act does not slow the company down. The ethical framework is backed by a dedicated responsible AI team within the AI Hub.

    The Future of the CAIO Role

    Rambach admits he is “very scared of becoming the bottleneck” as AI becomes pervasive. He sees the CAIO role evolving toward defining technical policy and supporting large, bespoke use cases. His overarching message is to not forget centuries of human wisdom: “My big message is we should not forget everything we have learned in the last 2,000 years, just because there’s a new technique.”

    For industrial giants, AI is not a replacement for human engineering but its most powerful tool—and mastering it lies in the critical mind of the user.

  • Schneider Electric’s CAIO on Scaling AI with Business-First Strategy and Critical Thinking

    Schneider Electric’s CAIO on Scaling AI with Business-First Strategy and Critical Thinking

    Philippe Rambach, Chief AI Officer at Schneider Electric, discusses how the company integrates artificial intelligence across its global operations while maintaining a pragmatic, business-first approach. In an interview with AI Magazine, Rambach outlines the principles guiding AI adoption in over 160 factories and 140 countries.

    Rather than chasing the latest technology trends, Schneider Electric evaluates AI use cases based on business value and technical feasibility. Every project passes through gate reviews that assess these factors, preventing what Rambach calls “pilot purgatory.” The company prioritizes solutions that can scale from the start, avoiding lab experiments that fail in production.

    The rise of agentic AI is another key topic. Schneider recently launched Sera, an AI agent that allows users to interact with environmental data using natural language. Rambach emphasizes that generative AI agents complement, not replace, classical AI methods like forecasting and anomaly detection. He warns against the common mistake of treating generative AI as a one-size-fits-all replacement.

    To prepare employees for AI adoption, Schneider has implemented a training program called “AI for All.” The curriculum emphasizes critical thinking, helping staff evaluate AI outputs rather than trusting them blindly. The company also hosts “promptathons” and maintains a prompt library to share best practices across teams.

    On the technical side, Rambach explains the decision between edge and cloud AI deployment. Factors include data sovereignty, cybersecurity, and latency—especially in high-speed manufacturing processes where millisecond delays matter. Schneider builds solutions that work in both environments.

    The interview also touches on the energy demands of large AI models. As a supplier to data centers, Schneider Electric uses AI to optimize power management in collaboration with partners like NVIDIA. Rambach views AI as both a contributor to and a solution for the energy density problem.

    Regulatory compliance under the EU AI Act is managed through a responsible AI team, and Schneider has published a public Trust Charter that excludes uses like facial recognition. Rambach notes that regulation has not slowed down innovation.

    Looking ahead, Rambach sees the CAIO role evolving from overseeing all AI initiatives to setting technical policy and supporting large-scale use cases. He warns against becoming a bottleneck and stresses the importance of leveraging existing transformation teams rather than creating new ones.

    The key takeaway: AI in heavy industry must serve real business needs, and human critical thinking remains indispensable. As Rambach puts it, “We should not forget everything we have learned in the last 2,000 years, just because there’s a new technique.”