Integrative insect systematics is entering a phase in which genome-scale sequencing, high-resolution imaging, artificial intelligence and increasingly reproducible computational infrastructure can be combined within the same evidential workflow. The opportunity is substantial, but the principal difficulty is inferential rather than technological: genomic resolution is not equivalent to an unambiguous species history, visible three-dimensional morphology is not automatically homologous or diagnostic, and machine recognition is not taxonomic adjudication. This horizon-scanning review examines these emerging interfaces and develops a proposed evidence-accountability perspective for keeping observation, measurement, computational prediction and systematic inference analytically distinct. Recent insect phylogenomics demonstrates both increased resolution and persistent conflict among loci, data types and histories. Micro-computed tomography extends morphological accessibility to historical and fossil material, while deep-learning systems increasingly extract useful signals from digitized and field imagery. Their transferability nevertheless depends on taxon representation, life stage, imaging domain, annotation and treatment of unknown taxa. The emerging frontier is therefore best understood not as progressive automation of taxonomy, but as tighter integration of complementary evidence with explicit validation boundaries. Reproducibility, provenance and specimen-linked multimodality become essential when computationally generated observations are expected to support durable systematic conclusions.
