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In the era of all-sky surveys and the upcoming era of extremely large telescopes, exoplanet astronomers will need to select transiting exoplanets for atmospheric study via transmission spectroscopy from a large and growing sample of confirmed planets. In this new asset-starved regime, it will no longer be practical to manually select planets for follow-up on a limited number of telescopes, and so an automated method must be developed. This work describes the development and implementation of such a method, centred around a decision metric, which uses primary transit observables and telescope properties to select planets with the maximal predicted atmospheric signals. This metric is integrated into a wider Pipeline for Ranking Exoplanets For Atmospheric CharactErization (PREFACE), which both selects targets for observation and identifies the best upcoming transits to target from a specified site and observing window. This framework has been validated by observing and analysing targets that it has selected, and is now guiding and supporting survey observations by the Spectroscopy and Photometry of Exoplanet Atmospheres Research NETwork (SPEARNET) team, using a world-wide network of small and medium ground-based telescopes. As an end-to-end demonstration of the wider SPEARNET strategy, this work includes case studies of two SPEARNET targets, the hot Jupiters WASP-104b and WASP-43b, including sets of new light curves obtained with SPEARNET telescopes and subsequent broad-band optical transmission spectra.