The three biggest GenAI priorities for industrial manufacturers

Industrial manufacturers see the massive potential in GenAI, but they may not be focusing their efforts on the areas with the biggest impact. Strategy& Germany recently collaborated with VDMA Software and Digitisation (Germany’s biggest trade group for machinery and equipment manufacturers) on an in-depth survey of 247 industrial manufacturers across Germany, Switzerland and Austria. Of that group, 91% planned to invest in GenAI in 2025, but only 29% have rolled out at least one GenAI use case, and only 7% have taken a systematic approach to implementing the technology. 

Most of the efforts thus far have been proof-of-concept projects, primarily in support functions. That’s a logical place to start, because it’s lower risk than implementing GenAI into core business functions. Yet it also limits the potential value of GenAI. Support functions can yield productivity gains from the technology, primarily by reducing operating expenses, but they don’t affect a significant chunk of the cost base—so their potential upside is limited. Also, these applications are likely to be standard across the industry, so they won’t lead to a lasting advantage. They’re necessary but not sufficient.

The survey also analysed 45 potential GenAI use cases—in both support functions and core business operations—and modelled the profit potential from them. Just ten of the 45 use cases account for more than half of the total margin improvement from GenAI, and all ten are in core business functions. (Notably, the model shows that if companies only implemented the GenAI use cases that address core business operations, they could capture 86% of the technology’s potential value.)

Of the full list of 45 potential use cases, three are clear winners in terms of potential margin increase.

•    Flagging changes in the supply chain. By automatically identifying changes in demand and transport, as well as supply chain disruptions, GenAI can suggest actions based on capacities and inventory data. It enhances demand forecasting accuracy by analysing unstructured data to spot trends, sentiments, and events that influence demand. For example, if a product received a high-profile endorsement in a given market, the technology would flag that, giving the company time to move resources around and meet a potential spike in demand.

•     Developing new products. GenAI enables the development of product designs, allowing for rapid iterations and visualization of prototypes. This process includes data generation, simulation and automated test case generation.

•     Personalizing products to meet customer preferences. GenAI translates customer preferences into changes of product designs, improving sales and creating a better customer experience.

All three have significant potential impact on profits, and they impact a large portion of the revenue base. They are harder to implement than initiatives in support functions, but also harder for competitors to replicate. In that way, they represent a bigger source of value for industrial manufacturers looking to capitalize on GenAI.