Industrial AI forms the basis for a resilient industry

Industry continues to be under growing pressure: supply chains repeatedly stall, energy prices remain volatile, regulatory requirements are increasing, geopolitical tensions are changing markets, and at the same time the pressure to produce more sustainably is growing. Sabine Scheunert, chairwoman of the management board in Central Europe at Dassault Systèmes, therefore sees the industry at a turning point. "This volatility has become part of the environment in which we operate," she said at her company's 3D-Experience conference.

What was long considered an exceptional situation has become the new reality. Companies must make decisions faster and better under uncertainty. Resilience is thus becoming the new competitive factor.

Companies must ask themselves whether their processes are designed to withstand constant turbulence, Scheunert emphasizes. At the same time, the speed of global competition is increasing. For Europe, it is crucial not to simply copy other markets, but to combine its own strengths with new technologies.

“We in Europe are not starting from zero,” says the manager. “Despite the negative public discussion, we have incredible industrial strength and excellent engineers.”

Nearly every company in Germany is now dealing with artificial intelligence. The decisive question has thus shifted: “No longer the whether, but the how determines the discourse - and which form of AI creates real economic added value,” she explains.

Why industrial AI works differently from generative AI

What is important to the expert here is the distinction between generative AI and industrial AI. Generative AI can make knowledge-based work more efficient, automate content, and accelerate decision-making processes. But that is not enough for industrial value creation. “Anyone who infers from this that complex industrial systems could be controlled through prompts and assistants falls short,” she explains in a commentary.

The reason lies in the special requirements of industrial processes. It is not about statistically plausible answers, but about reliable decisions under technical, regulatory, and economic conditions. On the shop floor, it is not enough if something sounds plausible. When making decisions about vehicles, production lines, or medical devices, results must be reliable, traceable, and connected to reality, Scheunert explained in her keynote.

How AI is intended to relieve engineers

For industrial AI to be able to deliver reliable results at all, according to Scheunert, it needs the right context. This is created through data, models, simulations, engineering knowledge, and processes that are brought together in a digital twin. “An error in the simulation costs nothing. An error in reality can cost millions,” the manager summarizes.

The digital twin thus becomes the rehearsal room for products, work processes, and supply chains.

In the keynote, Scheunert also classifies the role of AI with a view to people. Product complexity and the amount of available data have risen sharply, while human capacity is not growing to the same extent. This results in cognitive overload for decision-makers and engineers.

“The answer cannot be to simply hire more engineers,” she explained. And she continued: “The answer must be to give every engineer a tool that scales with this complexity. This tool is industrial AI.” At the same time, the industry expert emphasizes that AI is not intended to replace humans: “AI is the co-pilot, but you remain the pilot.”

From industrial AI to the Generative Economy

This change also has effects on the economic target vision. “Our economy is at a turning point,” writes the Central Europe head of Dassault Systèmes in a guest article.

What is meant is the transition from linear economic models to regenerative systems. Because, from the perspective of the guest article, classic growth models are reaching their limits. The reason: resources are limited.

One response to this is intended to be the Generative Economy. “The Generative Economy opens up the opportunity to shape the economy of tomorrow in a regenerative, resilient and innovation-driven way,” explains Scheunert.

This is not only about resource efficiency, but about value creation that is sustainable in the long term. The generative economy is thus a further development of the Circular Economy. It combines growth, innovation and sustainability and creates systems that are regenerative, resilient and future-proof. “The generative economy is not a trend, but a strategic turning point,” says Scheunert.

What industrial AI means for product life cycles

For industry, this approach means not viewing products in isolation. In a generative economy, every product is part of a larger system. Design, production, use and recycling must be designed in such a way that materials remain in circulation as much as possible.

Modern PLM approaches, for example, show what potential arises when companies think about products holistically. Because when data, processes, and supply chains are connected, it is possible to trace where raw materials come from, how they are used, and how they can be returned to the cycle.

Which brings us back to digital twins and industrial AI: They provide the technical foundation for simulating, evaluating, and optimizing such life cycles.

A new architecture for industrial value creation

It can therefore be clearly said: Industry is under permanent pressure to change. In the future, it will above all be those companies that master complexity, safeguard decisions, and optimize their value creation across the entire life cycle that will be competitive.

Industrial AI and digital twins are not the sole solution for this, but they are central tools. “Generative AI provides selective support. Industrial AI, however, determines the innovative strength and competitiveness of the industry of tomorrow,” says Scheunert.