Biologists

Prepare technical and research reports, such as environmental impact reports, and communicate the results to individuals in industry, government, or the general public.

AI: Fully automatable - AI can fully draft technical and research reports, synthesize data, and produce communications tailored to different audiences with high quality, subject to human validation for legal and contextual sign-off.

Program and use computers to store, process, and analyze data.

AI: Fully automatable - AI tools in 2025 can autonomously generate, test, and maintain code, build data pipelines, and perform complex data processing and analysis tasks to a degree that enables full automation of many programming workflows.

Prepare requests for proposals or statements of work.

AI: Fully automatable - Preparing RFPs or statements of work is a structured, template-driven writing and specification task that current AI systems can fully produce given requirements and constraints.

Measure salinity, acidity, light, oxygen content, and other physical conditions of water to determine their relationship to aquatic life.

AI: Fully automatable - Given available sensor networks, autonomous samplers, and ML models, AI can automate measurement of water physicochemical parameters and analyze their relationships to aquatic life end-to-end in many deployed contexts.

Develop and maintain liaisons and effective working relations with groups and individuals, agencies, and the public to encourage cooperative management strategies or to develop information and interpret findings.

AI: Partial - AI can support liaison work through messaging, scheduling, and information synthesis and can simulate outreach, but cannot fully replicate the trust-building and nuanced interpersonal negotiation humans perform.

Collect and analyze biological data about relationships among and between organisms and their environment.

AI: Partial - AI excels at analyzing biological data and can automate many sensor- and image-based collection methods, but comprehensive biological sampling and adaptive field decision-making still need human involvement.

Plan and administer biological research programs for government, research firms, medical industries, or manufacturing firms.

AI: Partial - AI can automate many planning and administrative functions (scheduling, budgeting, proposal coordination) and assist experimental design, but program leadership, stakeholder negotiation, and compliance decisions remain human responsibilities.

Study aquatic plants and animals and environmental conditions affecting them, such as radioactivity or pollution.

AI: Partial - AI can analyze environmental data, model impacts, and summarize literature on aquatic systems, yet hands‑on sampling, in situ measurement, and contextual ecological interpretation require human fieldwork and expertise.

Supervise biological technicians and technologists and other scientists.

AI: Partial - AI can assist heavily with administrative, scheduling, monitoring, and evaluative aspects of supervision, but cannot fully assume managerial responsibilities, accountability, or human leadership roles.

Identify, classify, and study structure, behavior, ecology, physiology, nutrition, culture, and distribution of plant and animal species.

AI: Partial - AI can accurately identify and classify species and analyze many aspects of biology from data, but comprehensive study of behavior, physiology, ecology, and distribution still requires hands-on experiments and contextual fieldwork.

Write grant proposals to obtain funding for biological research.

AI: Partial - AI can draft strong, polished grant proposals and tailor narratives to funders, but developing novel, fundable research visions, institutional approvals, and final competitive strategy need human leadership and domain credibility.

Communicate test results to state and federal representatives and general public.

AI: Partial - AI can draft and tailor clear, audience-appropriate communications and even generate delivery materials, but actual delivery, accountability, and real‑time stakeholder engagement require human oversight and authorization.

Research environmental effects of present and potential uses of land and water areas, determining methods of improving environmental conditions or such outputs as crop yields.

AI: Partial - AI can perform data analysis, scenario modeling, and propose interventions to improve environmental conditions or yields, but comprehensive field studies, stakeholder engagement, and implementation testing require human-led work.

Study and manage wild animal populations.

AI: Partial - AI can analyze survey and tracking data and generate population models and management recommendations, but cannot fully replace fieldwork, stakeholder engagement, and complex ecological judgment.

Prepare plans for management of renewable resources.

AI: Partial - AI can produce data-driven management plans and scenario analyses for renewable resources, but human decision-making, policy negotiation, and multi-stakeholder implementation remain necessary.

Teach or supervise students and perform research at universities and colleges.

AI: Partial - AI can deliver instructional content, grade, and support research tasks, but cannot fully replace the mentorship, academic leadership, lab management, and grant acquisition roles of university faculty.

Represent employer in a technical capacity at conferences.

AI: Partial - AI can create technical presentations and speak or provide virtual avatars, but fully representing an employer at conferences—including live networking, on-the-spot authoritative judgement, and reputation management—still needs humans.

Study basic principles of plant and animal life, such as origin, relationship, development, anatomy, and function.

AI: Partial - AI can synthesize literature, generate hypotheses, and model biological principles, but original empirical discovery, experimental design execution, and novel theoretical breakthroughs require human-led experimentation and domain judgment.

Review reports and proposals, such as those relating to land use classifications and recreational development, for accuracy, adequacy, or adherence to policies, regulations, or scientific standards.

AI: Partial - AI can check documents for internal consistency, flag methodological issues, and verify many compliance items, but nuanced scientific judgement and final regulatory or policy determinations need expert human review.

Study reactions of plants, animals, and marine species to parasites.

AI: Partial - AI can design experiments, analyze molecular and ecological data, and model host–parasite interactions, but cannot perform all required wet-lab or field sampling and interpret complex biological nuance unaided.

Develop methods and apparatus for securing representative plant, animal, aquatic, or soil samples.

AI: Partial - AI can design sampling protocols and propose apparatus concepts based on best practices and standards, but prototyping, field validation, and practical adjustments in variable environments require human/engineering execution.

Develop pest management and control measures, and conduct risk assessments related to pest exclusion, using scientific methods.

AI: Partial - AI can model pest dynamics, optimize control strategies, and support risk assessments, but practical implementation, field validation, and regulatory decision-making require human oversight.