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This Company Saved Millions with AI – Here’s How

Schneider Electric's vision, “data and AI first," is already paying dividends. How? It has prioritized operationalizing AI across the company.

Schneider Electric has done what many companies have found difficult: get a return on an AI investment. Their approach can work for your generative AI plans.

The big trend

You can’t scan the headlines lately without seeing buzz around generative artificial intelligence (AI). The product innovations are only beginning. But even with the best technology out there, you’ll still be faced with a key question: How can you implement AI at scale in a way that maximises the return on your investment? Let’s look at one model company you can learn from.

Breaking down silos

Schneider Electric, a global energy management and industrial automation company, has formalised an AI program under a new Chief AI Officer and scaled it to every corner of the company. Its vision, “data and AI first,” is already paying dividends. For example, the company has saved millions by using AI to more accurately forecast and manage inventory demand. 

The backstory you might need

Enterprise AI use has already doubled since 2017, but few companies are seeing significant return on their upfront costs, and a majority have failed to scale AI beyond the pilot stage. Analysts say the reasons include a lack of skills, complex programming models, upfront costs, and a lack of business alignment.

What you can do now

Take cues from Schneider Electric: 

  • Formalise AI efforts under one senior executive 
  • Understand the immense impact of AI – this is not like any technology that’s come before
  • Hire dedicated AI and data experts
  • Consider creating an AI centre of excellence to work with business unit leaders on AI projects

AI success requires AI at scale

Schneider had already been using AI in a decentralised fashion for years when, in 2021, it began its AI at Scale initiative and appointed its first Chief AI Officer, Philippe Rambach, to formalise its AI strategy.

Madhu Hosadurga, global vice president of enterprise AI at Schneider, said it’s important to have such a top-down approach.  

“If you want to drive AI at scale and get value from it, top management has to motivate it as a corporate-wide objective,” said Hosadurga. “Without the C-suite, everyone tries different things at a departmental and individual level.”

He said a departmental approach typically involves highly technical people that understand the technology but “lack the influence and power to make change management happen.” 

Bring business and tech leaders together to scale AI

The company has implemented a global hub and spoke AI operating model. Each business function “spoke” (marketing, sales, service, etc.) has an AI product owner and change agent who works with the tech competency centre “hub” to find new uses for AI, deliver the technology, and ensure employee adoption. The hub is comprised mainly of technologists who help the business leaders identify AI opportunities and put them into use. 

For example, supply chain leaders wanted to use AI for, among other things, balancing inventory based on projected demand, and its ability to deliver based on those projections. With 200 factories and tens of thousands of suppliers, it’s impossible for humans to ensure optimal inventory levels, Hosadurga said. 

AI analytics and predictive modeling helped it reduce inventory levels to avoid a glut while balancing its ability to efficiently deliver products like transformers, switches, and prefabricated substations. He said that improvement alone has resulted in about $15 million in savings, measured by how much excess inventory it reduced, and capital allocated to other projects. 

“We targeted $5 million to $10 million in value, so that was a pleasant surprise,” he said, adding that it plans to use new AI capabilities to pare an additional five percent of inventory. 

Hire AI and data experts for better decision-making

Schneider’s AI at Scale program included adding more than 200 AI and data experts. These two are inexorably linked, as AI is the linchpin to extracting more value from data and therefore making better, faster decisions. 

For many business leaders, it’s still a challenge. Salesforce research shows a deep disconnect between business leaders and their data. Half of business leaders lack understanding of data because it’s complex or not accessible, and the vast majority aren’t using it to make better decisions. 

According to Yuval Atsmon, senior partner at McKinsey, this is a missed opportunity. 

“For a top executive, strategic decisions are the biggest way to influence the business, other than maybe building the top team, and it is amazing how little technology is leveraged in that process today,” he said on a recent podcast.  

It’s extremely hard to synthesise huge amounts of data, let alone detect patterns, make recommendations and predictions. This is the promise of AI-driven systems. 

Hosadurga offered this advice for companies looking to formalise their own AI program:

  • Bring AI to the mainstream. Don’t view it as just another tool in your tech toolbox but as a new business capability that can change the way you operate, sell to customers, and enhance your employee experience.
  • Organise with IT and business partnering from the get-go. Often, AI is relegated to the IT team. When that happens, IT will ask the business for a use case, but the business usually doesn’t know what to do with AI. At Schneider, people come together from both sides, with a mix of about 70% business and 30% tech. 
  • Don’t wait until your data is perfect, in terms of quality and being all in one place, before embarking on a companywide AI initiative. “Many organizations believe they can’t use AI without perfect data,,” Hosadurga said, “but it’s more of a mindset issue where each business use case has to find the data, which is there in one form or another or in different places.” 

Need help with your generative AI strategy?

This guide is your roadmap to delivering a trusted program blending data, AI and CRM.

AI is not like other technology

Business people dominate most AI projects at Schneider, Hosadurga said, which is one thing that makes it different from any other technology project. 

“Every use case — and we have use cases in almost every function — has people from both the AI Hub and business,” Hosadurga said.

It’s entirely possible to deliver AI at scale, but unlike some other major business technologies, AI requires an entrepreneur’s mindset.

“If you look at a typical IT culture, things are well defined, you know what you get from them and they can be programmed with a long-term plan,” he said. “But AI tools move so fast that it requires a very agile, quick-win, fail-fast culture. We operate more like a standup where we find an idea, incubate it quickly, and move on to the next phase.” 

Schneider Electric, which invests tens of millions of dollars in AI each year, plans to apply more AI and automation to its finance, sales, marketing, IT, and human resources functions over the next year. The company has launched an AI knowledge library, featuring blogs, ebooks, podcasts, training, courses, and other resources, prepared by its AI experts, so others can learn from its experience. 

“It’s as applicable as Excel in business,” Hosadurga said “It’s everywhere.” 

How Schneider Electric generates up to 500 sales opportunities a day

The company created a Digital Opportunity Factory that uses AI to identify sales prospects with an unmet need.

Lisa Lee

Contributing Editor Lisa Lee is a contributing editor at Salesforce. She has written about technology and its impact on business for more than 25 years. Prior to Salesforce, she was an award-winning journalist with and other publications.

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