The use of genomics in beef cattle evaluation has experienced relatively widespread adoption. Breed associations and improvement programs have used genomic information to varying degrees, particularly for well-characterized traits. The opportunity for transformational change from genomics, however, lies with novel traits that are typically more difficult to capture or describe during normal production processes. Genomic associations have been reported for novel traits such as those related to feed efficiency, animal health, meat quality and consumer preference, reproduction, healthfulness of beef and effects on human health, the environment and animal welfare. While several interesting genomic regions have been identified across traits, there is scant evidence of application in structured breeding programs for beef cattle. The collection of phenotypes for some novel traits can have near-term direct genetic and economic impact. For example, improvements in individual feed intake or even disease resistance can generate measurable increases in beef system profitability. Other novel traits of interest, such as fatty acid composition or methane production, have large perceived consumer impact, but remain aspirational in terms of providing a direct economic benefit or even a logical selection objective. Regardless of the trait, the optimal use of genomics in a beef improvement program requires a structured data collection system, or large project-based phenotyping efforts, to realize the benefits and deliver a truly differentiated product. Ultimately, the value derived from genomics-enabled differentiation must at a minimum cover the cost of the investment. The value of genomics in the beef industry is difficult to elucidate without some measure of actual or “virtual” integration of the value chain, which is often lacking. Commercial breeding programs, such as Genus ABS, have developed the structure to deliver well-defined genetically improved products to the beef supply chain. At the core of this improvement is the ability to make rapid progress for economically relevant traits, which is often optimally achieved with the aid of genomic information.