Catalysis relies on a complex interplay of interdependent variables that must be optimised to meet a set of performance metrics. The challenge is exemplified by multicomponent photocatalytic systems where the parameter space is increasingly difficult to navigate due to the increasing number of variables required to provide supramolecular control (e.g., concentrations of reagents, additives, experimental variables). The optimisation target is also unclear as there is limited standardisation and a multitude of metrics to optimise (e.g., yield, quantum yield, selectivity, turnover number and frequency). Crucially, established and intuitive heuristic/human optimisation approaches can only maximise 1 or 2 performance metrics simultaneously. This situation has led to selective optimisations where some metrics are prioritised in ways that have limited meaning to overall system performance because they use conditions that sacrifice other metrics; such as using a very low catalyst loading to reach a high turnover frequency (TOF) but having negligible product yield. The fundamental problem is that optimisation to improve all figures of merit (holistic optimisation) is not possible with established protocols. The parameter space is too large to evaluate, the interactions of variables that affect multiple metrics are too complex, and no holistic figure of merit has been defined. New strategies are required to navigate the large parameter space and extract deeper understanding into how multiple variables interact and affect each metric to control overall system performance. Holistic optimisation first requires all figures of merit to be connected via a mathematical description (an objective function) that evaluates to a single scalar value representing overall system performance.