A dataset coming from RO workflows can consist of the following variables: parameters, lab measurements, metadata, AI predictions, and AI explanations. A usual RO workflow starts with a scientist deciding on the parameters (e.g., temperature, base, solvent) they use to optimize one or more target values (e.g., yield, selectivity, side components). The combination of all possible settings of parameter values defines the parameter space and is the basis of an RO dataset. Each combination of parameter values is one specific experiment setting that can be tested to get lab measurements. Scientists can add additional metadata to this dataset, for example, molecular descriptors of chemical components that can be used during the RO campaign. The RO workflow varies depending on the chosen method for the optimization campaign. We illustrate the typical workflow for iterative approaches in Figure . It usually starts with scientists analyzing the RO data (i ) and choosing a batch of initial experiments (ii ). The experiments are performed in the laboratory (iii ). After the experiments have been completed, the RO dataset is augmented by the measurement data (iv ), for example, the measured yield or number of the cycle in which the experiment was performed. With the updated data, scientists analyze the updated dataset (i ) to decide on the next batch of experiments (ii ). The process is repeated until a satisfactory outcome is reached (e.g., the yield is sufficient). To illustrate the AI-guided workflow in addition to the scientists-driven workflow, we consider an additional feedback loop where the RO dataset is given to an AI for processing (see Figure ). For example, for EDBO (Experimental Design via Bayesian Optimization) the RO dataset can be augmented with the AI predictions for each possible experiment and the corresponding uncertainty with which the prediction was made (e.g., predicted yield and variance). These predicted values are used to decide which batch of experiments to perform next using a socalled acquisition function. In addition to prediction outcomes, we can utilize XAI methods to add any kind of AI explanations that might be helpful to understand the optimization process (e.g., SHAP values tell us which parameters of an experiment were important for the prediction).