support (Fig. ). The acQve catalyst is proposed to be monomeric, with dimeric species and palladium carbonyl clusters proposed to be off-cycle resQng states that act as reservoirs for on-cycle species. It has been suggested that the palladium hydride bromide complex trans-[Pd(H)(Br)(PAd2 n Bu)2] is an important off-cycle resQng state. A combinaQon of base-mediated reformaQon of [Pd(PAd2 n Bu)2] from this complex, along with oxidaQve addiQon of the aryl bromide to [Pd(PAd2 n Bu)2] is proposed to be the turnover-limiQng step of catalysis. Despite the huge importance of this system, there are clear limitaQons with our understanding: liele is known about the mechanisQc step(s) that involve dihydrogen splifng, it is not clear how the proposed mechanism responds to changes in the aryl bromide substrate or parQal pressure of gases, and robust informaQon regarding catalyst acQvaQon and deacQvaQon pathways are lacking. KineQc analysis would potenQally provide informaQon to address these quesQons. Although several studies have been published on the kineQc analysis of catalyQc carbonylaQve processes, data collecQon can be challenging. ReacQons involving gas-liquid interfaces are typically conducted in batch pressure reactors, where repeated sampling required for offline kineQc analysis has the potenQal to affect pressures and gas-liquid raQos. While in situ monitoring techniques circumvent this problem, they require bespoke reactor setups and do not address another key issue; the need to run large numbers of experiments to form a complete mechanisQc picture (e.g. Hammee analysis, orders in catalyst, orders in reactants, order in base, Eyring analysis, kineQc isotope effects). Automated data collecQon and parallel experimentaQon offers the potenQal to address these challenges and not only increase producQvity in kineQc data collecQon, but also enhance data integrity and reproducibility through the use of roboQcs. In this paper, we report a detailed kineQc analysis of the best-in-class catalyst system for the formylaQon of aryl bromides. We use automaQon to accelerate kineQc analysis allowing insight into effects of catalyst concentraQon, reagent concentraQon, pressure, gas raQo, and temperature across a range of substrates. The kineQc analysis suggests that the turnover limiQng sequence involves a combinaQon of a reversible migratory inserQon step and dihydrogen acQvaQon step. KineQcs were sensiQve to the electronics of the substrate with two disQnct regimes observed for electron-rich (sp < 0) and electron-poor aryl bromides (sp > 0). DFT calculaQons were used in combinaQon with the kineQcs to create a new mechanisQc model for palladium-catalysed formylaQon. This model predicts divergent behaviour of electron-rich and electron-poor aryl bromides due to the influence of substrate electronics in the migratory inserQon step.