Organismal morphology was at the core of study of biodiversity for millennia before the formalization of the concept of evolution. In the early to mid-twentieth century, a strong theoretical framework was developed for understanding both pattern and process of morphological evolution, and the 50 years since the founding of this journal capture a transformational period in the quantification of morphology and in analytical tools for estimating how morphological diversity changes through time. We are now at another inflection point in the study of morphological evolution, with the availability of vast amounts of high-resolution data sampling extant and extinct diversity allowing “omics”-scale analysis. Artificial intelligence is accelerating the pace of phenomic data acquisition even further. This new reality, in which the ability to obtain data is quickly outpacing the ability to analyze it with robust, realistic evolutionary models, brings a new set of challenges. Phylogenetic comparative methods have provided new insights into the processes generating morphological diversity, but the reliance on molecular data and resultant exclusion of fossil data from most large phylogenetic trees has well-established negative impacts on evolutionary analyses, as we demonstrate with examples of standard single-rate evolutionary models, mode- and rate-shift models, and a recently described Ornstein-Uhlenbeck climate model. Further development of methods for phylogenetic comparative analysis of high-dimensional data is needed, but existing tools can refine our understanding and expectations of morphological evolution and the generation of morphological diversity under different scenarios, as we demonstrate with analyses of placental skull evolution through the Cenozoic. Fully transitioning the study of morphological evolution into the omics era will involve the development of tools to automate the extraction of meaningful, comparable morphometric data from images, integrate fossil data into large phylogenetic trees and downstream evolutionary analyses, and generate robust models that accurately reflect the complexity of evolutionary processes and are well-suited for high-dimensional data. Combined, these advancements will solidify the emerging field of evolutionary phenomics and appropriately center it around the analysis of deep-time data.

Organismal morphology was at the core of study of biodiversity for millennia before the formalization of the concept of evolution. In the early to mid-twentieth century, a strong theoretical framework was developed for understanding both pattern and process of morphological evolution. The 50 years since the founding of this journal capture a transformational period for the study of evolutionary morphology, in both how it is measured and how changes through time are reconstructed. We are now at another key transition point in the study of morphological evolution, with the availability of vast amounts of high-resolution data sampling living and extinct species allowing “omics”-scale analysis. Artificial intelligence is accelerating the pace of phenomic (high-dimensional, organism-wide) data collection. This new reality, in which the ability to obtain data is quickly outpacing the ability to analyze it with robust, realistic evolutionary models, brings a new set of challenges. Fully transitioning the study of morphological evolution into the omics era will involve the development of tools to automate the extraction of meaningful, comparable morphometric data from images, integrate fossil data into large phylogenetic trees and downstream evolutionary analyses, and generate models that accurately reflect the complexity of evolutionary processes and are well-suited for high-dimensional data. Combined, these advancements will solidify the emerging field of evolutionary phenomics and appropriately center it around the analysis of deep-time data.