AI is currently used in manufacturing primarily in quality control (image recognition for defect detection), predictive maintenance, process optimization (intelligent production planning), and robotics. Concrete examples include automated visual inspections, AI-supported sensor analysis for machine conditions, and self-learning manufacturing robots. AI enables real-time defect detection, optimizes maintenance cycles, and increases overall efficiency and product quality. AI can also be used very effectively when based on simulation data.

The biggest challenges often include insufficient data quantity or quality, the complex integration into existing systems, security and data-protection requirements, a shortage of skilled specialists, and potentially high investment costs. In addition, clear standards and best practices for large-scale AI deployment are still lacking in some areas. These factors can delay implementation and require close cooperation between IT, production, and management to successfully introduce AI.