The use of automated mineralogy has been critical in understanding all stages of a mine cycle including; identification of potential ore phases for exploration projects; routine composites for monthly mineralogy trends including liberation of target phases plus identification of penalty phases; for mine closure; and the potential of reactions like carbon sequestration from mine waste in order to ensure efficient remediation. Whilst automated mineralogy is clearly a key technique, the quality of the data and how ‘automated’ the actual automated mineralogy process is, is driven by the accuracy and efficiency of sample preparation techniques. The need for on-site operational mineralogy is growing with the need for rapid turnaround of data to drive processing plants.This paper discusses the effects of poor sample preparation on the quality of automated mineralogy data outputs including liberation metrics, mineral abundance and grain size. Potential sources for error from preparation techniques include; inefficient cleaning of all utensils and the block itself; poor screening, deagglomeration and polishing of the sample; and the pressure to streamline these preparation techniques for optimisation of mine site laboratories in order to rapidly generate data to monitor daily processing plant activity. A further discussion touches on how automated is automated mineralogy, establishing that a large percentage of the process depends on the efficiency and accuracy of sample preparation and that research should be focused on optimising this stage of the process.