Inspired by successes of non-platinum group metal (PGM) catalysis in the pharmaceutical industry and our lab's application of nickel catalysis to an active pharmaceutical ingredient (API), we challenged our optimisation workflow with a Ni-catalysed Suzuki-Miyaura coupling. Suzuki couplings are fundamental to pharmaceutical development, ranking among the top five reactions performed by medicinal chemists and the second largest reaction class at Roche and AbbVie . Although Pd-catalysed Suzuki reactions dominate the industrial landscape, palladium's increasing scarcity, high cost, and substantial environmental impact (3, 880 kg CO 2 equivalents per kg of palladium) present challenges for its continued use . In contrast, nickel offers a more sustainable and cost-effective alternative, with only 6.5 kg CO 2 equivalents per kg and a 3, 000-fold lower cost than palladium on a molar basis. These benefits become even more pronounced in process chemistry, where reactions are conducted on multi-(hundred) kilogram scales . However, Ni-catalysed Suzuki reactions are more challenging, prone to substrate inhibition, and generate more byproducts compared to their palladium counterparts. Consequently, the development of Ni-catalysed Suzuki reactions is important for the pharmaceutical industry to address the challenges associated with palladium catalysis. We selected a challenging nickel-catalysed heterocyclic Suzuki reaction between substrates 1 and 2 as an experimental case study (Fig. ). Based on expert knowledge, we defined a search space comprising 50 monophosphine ligands, 4 nickel precatalysts, 4 bases, 10 solvents, 3 co-solvents, and 5 temperatures. After removing conditions where the reaction temperature exceeded the solvent boiling point, we obtained a final reaction condition search space of 88, 000 combinations. Given the large number of categorical variables, one-hot-binary encoding (OHE) featurisation was impractical. Instead, we employed DFT descriptors to represent ligands and solvents (see Methods for full reaction condition parameterisation). All experimental details are fully specified in Supplementary Information section 5.