Mapping the spatial distribution of chemical and isotopic analytes by laser ablation inductively-coupled plasma mass spectrometry is a widely employed approach used for various materials and sample types. This is typically achieved by fully measuring a target area, row-by-row, to build a fully populated picture of chemistry on the sample’s surface. Instrumental development in fast aerosol transfer systems coupled to high-repetition-rate laser ablation instruments have increased the mapping rate significantly. Yet, physical dimensions of mapping areas remain typically confined to a few square mm. With fast mapping instruments, greater areas are within reach but economic and time constraints need to be considered. Accordingly, new questions arise about how experiments can be designed to decrease measurement time further. One such question is: does the whole surface area require sampling for effective, i.e. representative, mapping? We show here how artificial intelligence techniques can help to significantly reduce the amount of experimental data collected without sacrificing the map’s information content through post-processing.