How Siemens Is Bringing Generative AI To The Factory Floor

Generative AI is moving out of the chatbot and onto the factory floor.

Siemens is putting the technology to work to help engineers program machinery, automate production and solve one of manufacturing’s biggest problems: a severe shortage of skilled workers.

The industrial giant estimates that the global talent shortage represents an $8.5 trillion challenge, preventing many businesses from fully exploiting AI, automation and other transformative technologies.

Its answer is to augment workers with AI. At the heart of this strategy is its Industrial Copilot, developed with Microsoft, alongside advanced digital twin technology powered by NVIDIA.

And this is already delivering real-world results. PepsiCo says the technology helped improve productivity by 20 percent within three months, while Siemens is deploying it across its own factories as it works towards what it describes as the world’s first fully AI-driven adaptive manufacturing site.

So, what can Siemens’ approach tell us about where AI-powered manufacturing is heading, and what can other business leaders learn from it?

What Is Siemens Doing?

Siemens first launched the Industrial Copilot in 2023, putting a generative AI assistant directly inside its TIA Portal software framework, used globally to manage industrial automation tasks.

This lets engineers describe what they want to do in natural language, such as "the conveyor belt should stop automatically when it detects a jam" or “Give an alert when operating temperature exceeds safe levels”.

According to Siemens, more than 100 companies are now using these capabilities, including Krause Automation (formerly ThyssenKrupp), which uses it to help manufacture batteries and vehicle drivetrains at its global sites. By automatically creating the code that controls its automation machinery, it has cut tasks that previously took several hours to around 30 seconds.

Most recently, Siemens announced the launch of its Digital Twin Composer platform, built around NVIDIA’s AI technology. This lets manufacturers build complete virtual copies of factories, production lines and plants to simulate and test changes before altering anything physically.

Using this platform, PepsiCo simulated its U.S. production facilities, enabling it to catch up to 90 percent of potential issues before they caused problems in the real world and dramatically increasing production efficiency at its Gatorade plant.

Putting its money where its mouth is, Siemens has deployed the technology across its own industrial footprint too. In 2026, it unveiled plans for its Erlangen Electronics Factory plant, where Industrial Copilot runs 24/7, to become the world’s first fully AI-driven adaptive manufacturing site. This shows it can deploy its own technology at scale and achieve meaningful benefits in real-world industrial settings.

What Does Siemens AI Strategy Tell Us About The Future Of Industry And Manufacturing?

With Industrial Copilot, Siemens is showing that generative AI can produce the code needed to automate factory production from straightforward natural language prompts.

This effectively lowers the bar for factory automation, allowing experienced engineers to cover more ground and junior engineers to get up to speed faster. The number of machines that can be programmed, upgraded and maintained increases without a large increase in headcount.

This is why the transformation is framed as augmentation rather than replacement. Humans still need to review and fine-tune the code, and technicians are still needed to implement the physical changes.

At the same time, with its digital twin strategy, Siemens lets its industrial customers (as well as its own factory operators) assess and understand risks before implementing physical changes. This reduces the cost of experimentation and increases confidence that changes will have the desired effect.

This combination of lowered skills bar and more efficient innovation is the true value proposition of Siemens’ strategy. It means decisions can be tested, refined and implemented at scale without waiting for a new generation of engineers and specialists to be trained and deployed.

What Can Leaders Learn?

Siemens started with a real problem affecting its own business and its customers: a shortage of skilled workers and the long lead times involved in hiring and training to close the gap.

Then, it embedded its Industrial Copilot in TIA Portal, the default tool its engineers use to program and configure factory machinery. Rather than asking them to learn something entirely new, embedding AI into existing workflows reduces the friction that often comes with new tools and processes and increases the chances of successful workforce buy-in.

A third important lesson is that it tested its solutions on its home turf at Erlangen. This meant it could test them against real-world challenges it already understood. The bleeding-edge AI-driven factory is a live production environment, where failure or success is immediately visible. This is very different from deploying to customers and relying on them to provide feedback from environments outside its control.

Of course, not every business has Siemens' facilities and scale to do this. But the real lesson is that thoroughly understanding a problem and achieving total visibility of a solution is key to developing AI strategies that work in the real world, not just pilots and demonstrations.