Industrially widespread laser processes such as welding, cutting, and structuring are nowadays usually only monitored and at best controlled in partial areas such as process aborts. On the other hand, process data can be obtained with the aid of acoustic and optical sensors as well as thermal cameras and then processed further. Data acquired in this way are linked to quality characteristics such as welding seam formation and porosity for joining, and burr formation and edge roughness in cutting on the one hand, and to process parameters such as feed rate and laser power on the other. A prerequisite for controlled manufacturing processes is whether AI-supported algorithms can be used to make reliable predictions about the expected process result based on the gained process data. Large amounts of data originating from welding, cutting, and structuring are used to train AI and linked to quality criteria. In the first part of the presentation, the solution for recording and synchronizing the data from the various sensors are presented and discussed. Within a second step, the fast and partly newly developed equipment which uses FPGA architectures is utilized to gain comprehensive data from experimental trials. Using examples relating to laser welding, cutting, and structuring, the possibilities offered by AI-supported evaluation of sensor data obtained from the process in terms of quality predictions and improvements are discussed. Furthermore, within an outlook, opportunities for closed loop process control of cutting, joining, and surface structuring processes are shown which will be a prerequisite for autonomous manufacturing.