Biologists
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AI Prompt Tool for Biologists
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Research or study basic principles of plant and animal life, such as origin, relationship, development, anatomy, and functions.
The occupation "Biologists" has an automation risk of 43.3%, which is only slightly below the base risk of 44.0% for this field. This relatively moderate risk stems from the varied responsibilities of biologists, which blend both routine and highly specialized tasks. Modern automation technologies, including artificial intelligence and data analytics, have advanced to the point where certain aspects of data collection and report generation are increasingly automatable, making the overall risk for this occupation noteworthy. However, biologists are still required to exercise expertise and judgment that machines cannot easily replicate, leading to an automation risk that is significant but not overwhelming. Among the most automatable tasks for biologists are "preparing technical and research reports and communicating results," "developing and maintaining liaisons to encourage cooperative strategies," and "collecting and analyzing biological data." These tasks often involve structured, well-defined processes such as data processing, report formatting, and standardized communication—all of which can be substantially aided or even replaced by advanced software. Automated systems can handle large volumes of data with more efficiency and consistency than humans, making it likely that these repetitive or predictable elements of the job will see increasing automation. In contrast, the tasks with the greatest resistance to automation involve a higher degree of creativity, human judgment, and hands-on experience. These include “developing pest management and risk assessments using scientific methods,” “teaching or supervising students and conducting research at universities and colleges,” and “preparing plans for renewable resource management.” Each of these activities requires not just technical knowledge, but also adaptability, interpretative skills, and interpersonal engagement—areas where AI and robotic systems still face significant challenges. This resistance is underscored by bottleneck skills such as Originality, which has a low automatable component (3.3–3.9%), highlighting the importance of innovative thinking and problem-solving that currently remains beyond the reach of automation technologies.