In the context of my research, I am working on implementing artificial intelligence methods for bioprocess development. The focus is on using neural networks to analyze spectroscopic process data and combining this data with predictions from mechanistic process models. This will enable the prediction of biotechnological process trends and real-time correction of any inaccuracies using measured data. The challenges involved include the precise and quickly available conversion of spectroscopic data into process-relevant parameters, estimating overall model and process uncertainty, systematically correcting predictions, and ultimately deriving suitable adjustments to ensure the best possible product quality at the end of the process.