Geneticists

Research and study the inheritance of traits at the molecular, organism or population level. May evaluate or treat patients with genetic disorders.

The occupation of geneticists has an automation risk of 47.0%, indicating a moderate likelihood of being impacted by automation technologies. The base risk for this occupation is slightly higher, at 47.9%, which reflects the overall susceptibility of the core activities involved in genetics research to being automated. Geneticists frequently engage in tasks that combine both routine and non-routine elements, which leads to this medium level of risk. While many analytical activities can increasingly be handled by algorithms and data-driven tools, the occupation still requires a significant amount of expert oversight and qualitative interpretation, dampening the potential for full automation. As technology progresses, certain aspects such as data analysis and reporting are becoming more streamlined, but the inherently novel and investigative nature of much genetic research provides some resistance to automation. The top three most automatable tasks in the geneticist role largely involve supervisory, research, and reporting activities. For instance, supervising or directing the work of other professionals can be partly automated, especially as project-management software and workflow tools gain sophistication. Planning or conducting basic genomic and biological research is also increasingly aided by automated lab equipment, AI-powered analytics, and machine learning, which can perform repetitive experiments or analyze large-scale datasets with minimal human intervention. Further, preparing results of experimental findings for conferences or journals can be semi-automated through advanced data-visualization tools and automated report-writing technologies, reducing the manual effort required by geneticists in processing and presenting their research findings. Despite these advances, several tasks remain highly resistant to automation due to their complexity and the need for specialized human judgment. Activities such as planning curatorial programs for species collections or participating in the development of endangered species breeding programs require creativity, contextual evaluation, and intricate decision-making that AI and machines are not yet able to replicate reliably. Similarly, designing and maintaining genetics computer databases often involves a deep understanding of both biological systems and database architecture, requiring adaptability and problem-solving skills that go beyond rule-based automation. The relatively high demand for originality in this field—measured at 3.8% and 4.0% for relevant tasks—acts as a bottleneck skill, emphasizing the ongoing need for unique ideas, innovation, and critical thinking. These factors together ensure that, while genetics may see an increasing use of automation, certain core responsibilities will continue to require human expertise.