AI revolutionizes steel coil quality for carmakers and manufacturers

To explain industrial AI, Neurologiq founder Simon Sack uses the example of a "giant steel toilet paper roll." That's how he describes the appearance of steel coils—massive rolls of steel used in industries like automotive manufacturing, where they must be galvanized to precise specifications. Even the smallest deviations can make or break a product's value. "Too little zinc means scrap, a loss in material quality. Too much zinc means my profit margins shrink," he explains.

This example not only highlights the demand for precision in industry but also illustrates where industrial AI can make a difference. Artificial intelligence helps achieve the optimal galvanization level for steel coils. To do this, Sack and his company, Neurologiq, first analyzed production and machine data from their client, SMS group, identified which data points were relevant, and then used machine learning to build a system that fine-tunes the galvanization process.

This has little in common with AI chatbots like ChatGPT. Instead, industrial AI is about leveraging data effectively and developing specialized software that operates reliably in the background. According to Sack, this doesn't just cut costs and boost productivity—it can also lead to better machines and technologies.

Yet in Germany, this approach has yet to gain widespread traction in industry. Many companies, Sack says, still struggle with the long-discussed challenge of collecting and using data effectively. "The issue is that German industry is very, very spoiled. They want solutions they can just take out of the box," he notes in the podcast Arbeit in Progress.

Implementing tailored AI systems, by contrast, requires more lead time—a shift that many companies find difficult. The starting point is often problematic, as factories may not even have their machines properly networked. As a result, Sack argues, the much-touted "data treasure trove" remains largely untapped.

To change this, he says, small and medium-sized enterprises (SMEs) need accessible solutions. The "Eigen Engineering Agent" unveiled by Siemens at Hannover Messe 2026, for example, goes beyond what most SMEs currently require. It becomes truly compelling, Sack suggests, when it's about orchestrating multiple autonomous agents capable of handling tasks independently.

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