Molecular and Cellular Biologists

Maintain accurate laboratory records and data.

AI: Fully automatable - AI combined with LIMS/ELN systems can reliably capture, validate, organize, and maintain laboratory records and data, automating most recordkeeping tasks with human oversight for exceptions.

Prepare reports, manuscripts, and meeting presentations.

AI: Fully automatable - By 2025, LLMs and document/slide generation tools can produce polished reports, manuscripts, and presentations from data and notes end-to-end, with humans primarily reviewing and validating content.

Design databases such as mutagenesis libraries.

AI: Fully automatable - Designing databases and digital resources like mutagenesis libraries is primarily a computational task that AI can fully automate, including schema design, annotation pipelines, and data integration given appropriate inputs.

Design molecular or cellular laboratory experiments, oversee their execution, and interpret results.

AI: Partial - AI can design experiments and interpret results computationally, but it cannot physically oversee lab execution or manage real-time hands-on troubleshooting without human intervention.

Compile and analyze molecular or cellular experimental data and adjust experimental designs as necessary.

AI: Partial - AI can compile and analyze experimental data and propose iterative design adjustments, yet final decisions and hands-on implementation of changes typically require human judgment and oversight.

Conduct research on cell organization and function, including mechanisms of gene expression, cellular bioinformatics, cell signaling, or cell differentiation.

AI: Partial - AI tools can analyze data, mine literature, and propose hypotheses for cell organization and gene expression, but designing, validating, and interpreting novel experimental research still requires human expertise and wet-lab validation.

Perform laboratory procedures following protocols including deoxyribonucleic acid (DNA) sequencing, cloning and extraction, ribonucleic acid (RNA) purification, or gel electrophoresis.

AI: Partial - Robotic lab platforms and automated protocols can carry out many sequencing, cloning, RNA prep, and electrophoresis steps, yet complex, nonstandard procedures and troubleshooting still need human oversight.

Supervise technical personnel and postdoctoral research fellows.

AI: Partial - AI can assist with scheduling, performance tracking, and providing management recommendations, but nuanced personnel supervision, mentorship, and conflict resolution remain human-led.

Direct, coordinate, organize, or prioritize biological laboratory activities.

AI: Partial - AI can optimize workflows, prioritize tasks, and coordinate logistics, but strategic decision-making, resource allocation in novel situations, and lab leadership require human judgment.

Instruct undergraduate and graduate students within the areas of cellular or molecular biology.

AI: Partial - AI-driven tutoring and courseware can teach concepts and provide feedback at scale, but interactive mentorship, lab supervision, and assessment of research projects need human instructors.

Monitor or operate specialized equipment such as gas chromatographs and high pressure liquid chromatographs, electrophoresis units, thermocyclers, fluorescence activated cell sorters, and phosphorimagers.

AI: Partial - Many instruments offer software automation and remote monitoring that AI can manage, but hands-on setup, complex troubleshooting, maintenance, and some specialized operation still require humans.

Develop assays that monitor cell characteristics.

AI: Partial - AI can design candidate assays, model readouts, and optimize parameters in silico, but empirical assay development, iterative wet‑lab optimization, and validation remain only partly automatable.

Coordinate molecular or cellular research activities with scientists specializing in other fields.

AI: Partial - AI can facilitate coordination through synthesis of data, meeting facilitation, and documentation, yet high-level interdisciplinary negotiation and relationship-building are still human-driven.

Evaluate new technologies to enhance or complement current research.

AI: Partial - AI can rapidly survey literature, benchmark tools, and simulate likely impacts of new technologies, but final evaluation, risk assessment, and adoption decisions typically require human domain judgment and context awareness.

Provide scientific direction for project teams regarding the evaluation or handling of devices, drugs, or cells for in vitro and in vivo disease models.

AI: Partial - AI can synthesize literature, propose experimental plans, and suggest handling considerations, but cannot fully assume accountability or make context-specific safety/regulatory leadership decisions for in vitro/in vivo projects.

Develop guidelines for procedures such as the management of viruses.

AI: Partial - AI can draft and update procedural guidelines (including biosafety protocols) from standards and data, but human experts are required to validate, certify, and adapt them to facility-specific legal and safety constraints.

Evaluate new supplies and equipment to ensure operability in specific laboratory settings.

AI: Partial - AI can analyze specifications and predict compatibility/performance, yet physical testing, on-site validation, and final operational approval in specific lab environments still require human inspection and hands-on verification.

Verify all financial, physical, and human resources assigned to research or development projects are used as planned.

AI: Partial - AI can monitor financial records, inventory, and workforce systems to flag discrepancies and generate audits, but cannot fully verify the real-world use of physical assets or make authoritative personnel judgments without human oversight.

Conduct applied research aimed at improvements in areas such as disease testing, crop quality, pharmaceuticals, and the harnessing of microbes to recycle waste.

AI: Partial - AI accelerates hypothesis generation, experiment design, simulation, and data analysis for applied research, but cannot yet fully conduct or take responsibility for complex wet-lab experimentation, iterative troubleshooting, and translational implementation alone.

Participate in all levels of bioproduct development, including proposing new products, performing market analyses, designing and performing experiments, and collaborating with operations and quality control teams during product launches.

AI: Partial - AI can perform market analyses, propose product ideas, and design experiments and protocols, but end-to-end product development and cross-functional launch coordination require human decision-making, regulatory navigation, and operational execution.

Confer with vendors to evaluate new equipment or reagents or to discuss the customization of product lines to meet user requirements.

AI: Partial - AI can prepare evaluations, compare vendor specifications, and draft communications about customizations, but direct vendor negotiation, hands-on demonstrations, and contractual decisions still need human involvement.