For each substrate, a total of 18-36 experiments were conducted within an 8-16-hour timeframe. This comprised 8 initialization experiments followed by refinement experiments for each optimization campaign until a sufficient yield/hypervolume was achieved. Notably, substantial yield improvements were observed compared to their respective model counterparts in batch reactions. The platform demonstrated also a remarkable increase in productivities, ranging from 70 to 100 times higher. Next, the reaction conditions selected by the AI model were successfully employed for scale-up to a 5 mmol scale. In all cases, the isolated yields obtained during the scale-up process closely matched the NMR yields observed with the AI-found reaction conditions. Upon further analysis of the AI-discovered reaction conditions, several interesting observations arise. The AI algorithm refines the reaction conditions to achieve optimal reactivity and selectivity for each specific substrate. Notably, the Bayesian Optimization algorithm identifies experimental conditions that deviate significantly from the standard conditions reported by Koenig et al. One remarkable finding is the substantial differences in reaction/residence time and light intensity, which are parameters that experienced chemists often overlook initially. To illustrate this, we can compare the results obtained for trifluoromethylthiolated Sclareolide (5) and Ambroxide (6). It becomes evident that the catalyst loading and light intensity are notably lower for Ambroxide. This can be rationalized by the fact that Ambroxide can undergo an additional reaction with another equivalent of Phth-SCF3, resulting in a double functionalized final product. However, such a reaction is not possible with Sclareolide, as the α-to-O carbon position is blocked by the carbonyl group. By reducing the catalyst loading and light intensity, the AI algorithm successfully enhances the yield and selectivity of the mono-functionalized product (6).