How Schneider Electric Is Deploying AI To Improve Energy Efficiency For All

How Schneider Electric Is Deploying AI To Improve Energy Efficiency For All


Schneider Electric is a $34.2B French-based Fortune Global 500 company that specializes in digital automation and energy management. The firm was founded in 1836 by brothers Adolphe and Joseph-Eugene Schneider who took over an iron foundry in Le Creusot, France. Two years later, they founded Schneider-Creusot, the company that would eventually become Schneider Electric. Today, Schneider’s stated purpose is to “empower all to make the most of our energy and resources, bridging progress and sustainability for all.”

Like so many companies across industries today, Schneider Electric is mobilizing to leverage AI to improve productivity and efficiency. A recent survey found that 64.2% of companies believe that Generative AI has the potential to be the most transforming technology in a generation. Like them, Schneider sees AI as having the potential to transform entire industries through greater speed, efficiency, and agility, enabling decision-makers to make better and faster decisions based on data. Schneider aims to leverage the power of AI to ensure greater efficiency and sustainability through data-based insights, and to tackle global challenges such as climate change.

Philippe Rambach is the inaugural Chief AI Officer for Schneider Electric, a role he assumed in November 2021. He previously spent a dozen years in business leadership roles for the company, including running the $6B commercial industry business unit for 3 years. Rambach comments, “My mandate is to create the best possible conditions and partnerships to design, build, and deliver AI solutions that meet the challenges of our customers and improve Schneider’s own internal efficiency.”

At Schneider Electric, AI is seen as a continuation of the process of digital transformation across the organization. Rambach notes, “I think it was an important lever that our leadership team decided to create a dedicated role of Chief AI Officer (CAIO) in 2021, reporting to our Chief Digital Officer in the executive committee”. Rambach reflects, “My educational background is deep mathematics and science, but I’ve spent most of my career developing businesses.” He continues, “When Jean-Pascal Tricoire, our CEO at that time, called me to talk about the CAIO role, I asked if he was calling the right Philippe because I am not a data scientist nor am I an AI expert. But Jean-Pascal was persuaded that we needed somebody who could scale AI and deliver tangible value for customers and for the company internally.” This is Rambach’s mandate as CAIO.

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Rambach believes that establishment of the Chief AI Officer role was timely. He comments, “It proved to be a good decision when GenAI came into play in late 2022. We already had the structure and first AI use cases in the pipeline at that moment and it was easier to grasp the potential of this innovative technology.” At the end of 2021, Rambach and his team launched an AI center of excellence within Schneider Electric, the AI Hub, providing the necessary governance, building data science and AI expertise, and partnering with all lines of businesses and internal departments to infuse AI across the organization.

Partnering with business leaders is key to successful adoption and delivery of business value within Schneider Electric, as is the case with any firm that hopes to ensure business results. Rambach notes, “This job is one of the few where there is both the need for a strong technical foundation and a strong business background. To execute AI at-scale you need to understand what it is and go beyond shiny demos, and also understand opportunities and the impact on business”.

Today, Schneider Electric is deploying AI to deliver business value in three ways: (1) to increase internal productivity, (2) to enhance existing products and services, and (3) to open new value streams. Rambach explains, “For example, internally, AI supports our customer care centers that receive around 7.5 million questions (tickets) a year from our customers.” He continues, “Until we applied AI, our employees would analyze the ticket to guide the request (a question on price, product range, connected offers). This first step is now automatically completed by AI.” Rambach concludes, “AI improves the quality of service, the user experience, and the execution of projects, giving our employees more time to focus on activities related to customer relations that are less tedious and more valuable.”

Generative AI has been transformative for the company due to the amazing rate of business adoption. The biggest opportunities have been from automatic code generation, support for sales teams, generation of marketing content, and access to knowledge. The three biggest risks have been from AI hallucinations, data loss, and cybersecurity issues. After experimenting with public ChatGPT, Schneider decided to deploy Jo-ChatGPT, an internal application giving employees access to the power of OpenAI in a secure way, where data stays within the walls of the company. Rambach adds, “The lack of the necessary amount of quality data is one of the reasons why we close some AI use cases, either already at the ideation stage, or later, when we determine that we don’t have enough data to ensure value for the customer or for an internal function. Quantity and quality of data is crucial.”

By harnessing AI’s capabilities, Schneider Electric envisions a long-term opportunity to revolutionize energy management and to put sustainability strategies into action. There are three areas where AI can bring transformational changes and lower the environmental impact: (1) reducing energy consumption and carbon emissions, (2) optimizing energy demand, and (3) overcoming barriers to the widespread adoption of clean energy sources, which paves the way for a greener future. Rambach notes, “If we look at the many industries we serve, some of our clients aim to produce the most sustainable product available, some focus on price, and others focus on the quality of their product.”

Rambach comments, “With AI we can analyze all variables to formulate an optimal manufacturing set-up that can help to deliver the most sustainable, cost-effective, and highest-quality product possible.” He adds, “As we’ve gone through optimization projects with many clients, we have observed a considerable margin of improvement that is ready to be captured with AI and data analytics.” Schneider is executing a sustainable energy vision with advanced AI algorithms that companies can use to reduce energy consumption by optimizing energy-intensive processes such as HVAC systems in buildings, water desalination, and district heating.

AI can also be used to optimize energy demand over time and alleviate energy consumption peaks. For example, AI-driven solutions can effectively manage microgrid operations, and electric vehicle (EV) charging, reducing the overall carbon intensity associated with energy production at high demand periods of the day. Schneider’s EcoStruxure Microgrid Advisor is being used to optimize the buying, selling, and using decisions thanks to constant analysis of different information sources.

In its North American R&D hub in Boston, the facility’s advanced microgrid includes 1,379 solar modules, alongside photovoltaic inverters for on-site power generation. By leveraging cloud-based analytics from the EcoStruxure Microgrid Advisor, the facility also harnesses weather forecasts and operational information to optimize energy performance across onsite solar, energy storage, and electric vehicle charging. The hub generates over 520,000 kilowatt-hours (kWh) of electricity per year, which is the equivalent of removing annual greenhouse gas emissions from more than 2,400 passenger vehicles.

Schneider Electric is applying AI to break down barriers to the widespread adoption of energy-efficient technologies, streamlining processes such as grid topology discovery, panel insights, and electric equipment AI based services, thereby facilitating the seamless integration of sustainable solutions at scale. Rambach notes, “I believe that energy transition, sustainability, and carbon reduction will simply not happen without AI. There is too much data, too much complexity, and too many parameters.” He adds, “Globally, there is no way the world can do that without AI to help us reach critical climate goals for the future.”

Responsible use of AI is a foundation cornerstone for all of Schneider Electric’s AI initiatives. Rambach comments, “The number one rule we apply when developing AI technology and data science is adhering to the ethics and compliance conformed code of conduct described in our Trust Charter”. Schneider has established a separate governance office and separate data office, and appointed a Digital Risk Leader, dedicated to ensuring responsible AI projects. The company has also launched a Responsible AI (RAI) workgroup focused on frameworks and legislation. To ensure AI is properly managed for every use case, Schneider applies a dedicated framework to evaluate AI-related risks and deliberately shut down some projects or choose not to launch projects that raise high ethical concerns.

Rambach concludes, “As a company that has been developing solutions for clients in critical infrastructure, national electrical grids, nuclear plants, hospitals, water treatment utilities, and more, we know how important trust is. We see no other way forward than to develop AI in the same responsible manner that ensures security, efficacy, reliability, fairness (or the flipside of bias), explainability, and privacy for our customers.” Rambach adds one final piece of advice, “Don’t wait until your data is perfect but do organize robust data for the use case you want to start with”. Wise advice from a company at the forefront of harnessing the power of AI to improve energy efficiency for all.



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