Schneider Electric is a global energy technology company focused on electrification, automation, and digitalization across homes, buildings, data centers, factories, infrastructure, and power grids. As AI reshapes how energy is produced, managed, and consumed, the company is embedding AI across its portfolio to improve efficiency, reduce emissions, and simplify increasingly complex energy systems at global scale.

In this first installment of a three-part CDO Magazine interview series, Philippe Rambach, Chief AI Officer at Schneider Electric, speaks with Dr. Julian Schirmer, Co-Founder at OAO and Academic Director at HEC Paris, about why AI has become central to Schneider Electric’s strategy. The conversation explores how the company is moving beyond pilots toward AI that delivers measurable value at scale. The discussion spans customer energy optimization, grid balancing, operational simplification, and employee productivity.

A strategy built around efficiency, cost, and carbon

Rambach frames Schneider Electric’s strategy in operational terms: helping customers do more with less energy, use more decarbonized energy, and improve efficiency across operations. He describes the ambition simply: “We want to be the global technology partner and energy technology partner for our customers.”

He grounds this ambition in the real-world environments where energy is produced, distributed, and consumed. “We provide everything you need to manage your electricity and your energy from homes, to buildings, to data centers,” he says, adding the industrial layer: “We also provide what customers need to automate production in industrial factories.”

The “why” of AI is inseparable from this portfolio. AI is not a standalone initiative bolted onto the business. It is the next mechanism for delivering the same customer promise, only better, faster, and at greater scale.

AI that delivers value at scale

If there is one theme Rambach returns to, it is scale. Deploying AI broadly enough to change outcomes, not just presentations.

“The first thing we want to achieve with AI is to make sure that we deploy AI at scale,” he says. “Our obsession as a company is to do AI that impacts customers and helps employees work in a better way, at scale.”

He is explicit about the risk Schneider wants to avoid: “doing too many pilots and proofs of concepts and delivering no value.” For Rambach, AI is not about novelty. It is about measurable impact that can be repeated across a global customer base.

Making comfort smarter, not harder

Rambach’s first customer value proposition is simple: “How can we help them use less energy?” The logic is both economic and environmental. Lower energy use reduces cost and emissions. “If you use less energy for the same outcome, you decrease your cost and reduce the impact on the planet.”

To make this tangible, he points to temperature control in buildings. Schneider has long sold room controllers connected to building management systems, so heating and cooling run only when needed. AI now pushes that outcome further.

“Now we use AI to improve how this works,” he explains. “Machine learning learns the thermal behavior of the room. As a result, we can deliver greater comfort and achieve up to 20% additional energy savings by embedding AI into home control.”

Customer needs remain stable even as the technology evolves. “The customer still wants energy savings and comfort. AI’s role is to take it to the next level,” he says.

AI meets the grid

Rambach then widens the lens from a single room to the energy system itself. As electrification accelerates, balancing the grid becomes more complex and more critical.

“You always need to balance production and demand. The grid hates peak demand,” he says.

He describes a demand-side AI approach that forecasts consumption and production, especially where renewables introduce variability. Using a building with solar panels as an example, he outlines the decision loop: “AI will forecast the consumption of the building. It will forecast the production of solar panels, and then it will optimize and decide whether it is better to buy from the grid, sell to the grid, or store for later use.”

The result is lower cost and reduced reliance on peak energy, which he notes is both carbon-intensive and expensive. “When people think about moving to green electricity, they always think about the production side. But the demand side is also vital.”

Behavior change, without the burden of change

When asked whether customers must change how they use energy, Rambach’s answer is pragmatic. The first step is making complex systems easier to operate.

“AI can make that easier,” he says. Schneider is embedding generative AI, natural language interfaces, and agent-based tools to simplify energy management and help users make better decisions. His preferred version of behavior change is frictionless: customers benefit without needing to consciously change anything.

AI for employee efficiency

Rambach draws a clear line between external and internal impact. “Half our efforts are toward customers. The other half is focused on increasing employee efficiency,” he says. “When you improve employee efficiency, you improve customer satisfaction.”

Internally, Schneider focuses on knowledge support and automation.

On knowledge, he highlights tools that help employees find answers faster, especially in customer care and finance. The company has already deployed solutions that reduce the time employees spend searching for information.

On automation, Rambach points to dispatching field service engineers. “The scheduling of thousands of field service visits was done by hand until recently,” he says. AI now automates scheduling to improve speed and reduce friction for technicians. “We see increases in both customer and employee satisfaction.”

Retaining expertise through AI

Responding to Schirmer’s question about demographic shifts and knowledge loss as experienced employees retire, Rambach recalls an early internal pilot that became well known inside the company.

“The knowledge solution was called Marco because Marco was about to retire and Marco knew everything about everything,” he says.

The goal is not only to capture expertise but to make institutional knowledge easy to navigate. “Every company has millions of pages of knowledge,” he explains. AI helps turn that sprawl into practical support, accelerating onboarding and enabling less experienced employees to work more independently.

CDO Magazine appreciates Philippe Rambach for sharing his insights with our global community.