How an Ancient French Company Scales with AI

I keep a list.

Companies that have actually made AI work. Not as a pilot. Not as a press release. Real applications, in production, with measurable results.

The list is shorter than I would like.

Most success stories I find are small. One team. One tool. One isolated use case that works – and then quietly disappears somewhere inside the organization. Nobody knows why it didn't scale. The pilot ran. The project went to sleep.

Then I came across Schneider Electric.

Founded in 1836. Switchboards, energy systems, industrial infrastructure. A company older than the lightbulb.

And today: nearly 100 AI applications in production. No pilot graveyard. Straight into the workflows.

I had to read that twice.

How does a 190-year-old industrial company pull off what most tech firms – with a fraction of that history – can't?

You probably haven't heard about this.

Schneider Electric doesn't make it into the big spotlight. No viral moment. No TED Talk. No single story that explains everything.

Probably because the opposite is true: too many applications, too many small steps, too little drama. That doesn't package into a hero story.

But look closer, and a pattern emerges. Three things, to be precise.

They keep doing what they've always done – just better.

Schneider Electric didn't announce a grand transformation. No "AI-First" manifesto. No reinvention of the business model. They build energy systems. They optimize production processes. They help customers use energy more efficiently. They've done that for nearly two centuries.

AI is not the replacement. AI is the tool – embedded into the things and processes that already exist. A copilot for sales that makes an overwhelming product catalog navigable. A system that pre-processes quote requests so salespeople can respond faster. No new products. No new departments. The same work – with less friction.

This matters more than it sounds. Most companies treat AI as the destination. Schneider treats it as the road. The destination is the same as it always was: better products, faster decisions, happier customers.

They don't expect 100 percent.

This is perhaps the most understated part of the story – and the smartest.

Schneider Electric operates on the principle that 80 to 90 percent accuracy from AI is enough in most situations. As long as a human reviews and improves the output. That sounds obvious. It isn't.

Most companies don't fail at AI because the technology is bad. They fail because they expect perfect results. And when the system doesn't deliver, the project gets killed. The next pilot begins. Nothing scales.

Schneider Electric reverses that logic. The AI doesn't need to be perfect. It needs to be good enough that the people using it can do better work – faster.

That removes an enormous burden from the whole endeavor. You don't have to prove that AI can do everything. You just have to find where it can do enough for you to get better at doing your job.

There is a quiet confidence in that principle. And it is the reason their AI projects survive and scale where others don't.

They do it without drama.

Nearly 100 AI applications in production. That sounds like chaos – endless meetings, a massive central strategy, a transformation office with a budget to match.

The reality at Schneider is different. A team of just over 350 people – out of a global workforce of 160,000 – runs the entire AI program. A structured process with clear gates – from idea to scale. Every initiative has to answer the same question: What is the concrete business value? Not: What is technically possible? But: What problem does this solve – and for whom?

That's not magic. That's good old project work. Define the goal. Set the path. Bring together the people who understand the domain. And then make sure the goal gets reached.

There is something reassuring about that. AI integration is not a new craft. It is the same craft – with a new tool.

What any company can take from this.

First: start with what you already do. Not: how can AI transform my company? But: where does it get stuck today – and can AI help make it flow? That is a far more honest question. And one that has real answers.

Second: stop waiting for 100 percent. You won't get it. No system delivers it. Neither does any human. The question is not: is the AI output perfect? The question is: does it make my people's work better – well enough to be worth it?

Third: age protects neither from success nor from failure. Schneider Electric proves that nearly two centuries of history are no obstacle – if you stop treating AI as a threat or a magic wand, and start using it for what it is. A tool that makes people work better – by taking better decisions faster and acting accordingly.

Which is the most ancient habit of them all.

Where has AI helped you improve – and where was the expectation bigger than the result?

If you want to think that through for your own business – that is exactly what Alexano is here for.

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