We identified two points in time where scientists require analyzing RO optimization data: during and after the optimization process. During RO, scientists have to decide on the next batch of experiments based on all the data from the current and previous cycles. Scientists have to be able to balance exploration and exploitation of the parameter space, otherwise the RO campaign is prone to stop at a local maximum without even considering most of the reaction space. With AI-driven RO, the balancing of exploration and exploitation is done implicitly with the use of acquisition functions. However, users of AI models have to understand the predictions of a model in a way that allows them to judge its actual performance and shortcomings . Only then users can decide whether it is better to comply with an AI's suggestion or to overrule and rely on their own experience. In summary, scientists should be able to understand the RO campaign to make informed decisions. For AI-guided RO, scientists should be able to effectively combine their own chemical intuition with an AI model's computations to make an informed decision on the next batch of experiments. After the RO campaign, scientists want to learn from the collected RO data to gain insights for future RO campaigns. In scenarios where unsatisfactory results have been reached, scientists need to be able to locate the problem of their own or an AI's decision. If the results have been desirable, chemists can learn from their colleagues or the AI model's decisions and improve their own understanding and intuition. Therefore, scientists should be able to effectively review the entire RO campaign to see if the problem was approached in a useful way, or if biased decisions hindered reaching an optimal solution.