The overall motivation for the development of an information system for beef cattle improvement is the belief that knowledge of breeding values and heterosis effects allows one to determine the consequences of alternative selection and mating options. With this information, livestock managers can easily shift populations in a desirable direction. The foundation principles for establishing a sound breeding program, including the prediction of animal performance for economically relevant traits and their incorporation into a single index of aggregate economic merit, have been well established over the last half century. Rather than this goal-based approach, the industry adopted a data-driven approach to the production of genetic evaluations that has been characterized by an overemphasis on the evaluation of productive traits, notably BW at various ages, with inadequate regard for other economically important traits, such as reproduction, animal health, and feed requirements. Production of evaluations is breed association centered, and this has delayed the introduction of national across-breed evaluations for all breeds and crosses of cattle. The computational aspects of producing evaluations are now migrating from land-grant universities to breed associations, but not yet to a single entity. The introduction of genomic information in the form of high-density SNP panels will introduce threats, challenges, and new opportunities for the production of evaluations, and represents the largest force to alter the structure of the beef improvement industry since the advent of AI. The use of evaluations has, until recently, stopped short of the provision of index merit as a basis for selection. Accordingly, the value propositions associated with annual improvement or the selection of alternative sires has not been well communicated. Technology, along with economic and other issues related to stakeholder acceptance, will collectively determine the future nature of the industry in terms of the production and use of evaluations.