We varied all material and process parameters of interest together in a global sensitivity analysis in order to investigate which properties of an amine-functionalised adsorbent are critical to improved solid adsorbent direct air capture performance. From this, we demonstrated the fingerprint of optimal direct air capture adsorbents based on our set of process-informed adsorbent design guidelines, whilst also showing which process parameters impact performance the most. As part of our guidelines, we made suggestions on which amine and support combination to pursue, as well as how adsorbents should be structured and what alternative heating methods could be investigated further. To allow us to perform the global sensitivity analysis, we developed two artificial neural network models from a detailed process model for a temperature vacuum swing adsorption process and a steam-assisted temperature vacuum swing adsorption process, without which the global sensitivity analysis would not have been possible. The key contribution of this study was that it showed precisely which adsorbent properties are critical for direct air capture processes and which are not to focus further adsorbent design.