Industrial Artificial Intelligence (AI) requires highly specific data that no single company can provide on its own. According to FAZ, data spaces are a promising solution to enable access to these essential pieces of information. These data spaces could lay the foundation for collaborative AI capable of significantly enhancing economic value in areas such as autonomous vehicles and the automation of logistics centers.
The importance of industrial AI is evident in its application for predictive maintenance of machines. While a manufacturer can collect operational data from its own facilities, this data set is often too small to develop reliable models. Access to data from multiple organizations is necessary to capture rare failure cases and varying operational conditions.
A central issue is the availability and sensitivity of the data. Industrial data is often business-critical and subject to strict data protection regulations. Additionally, industrial environments are continuously changing, which means that AI models must be regularly updated to avoid performance degradation.
The opportunities presented by the use of industrial AI are enormous. However, success depends not only on algorithmic advancements but also, crucially, on access to high-quality, domain-specific data. Therefore, companies must collaborate to consolidate the necessary data and develop robust AI models.
