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AI Automation Index
Will AI Replace Bioinformatics Scientists?
Task-level automation exposure for Bioinformatics Scientists: 20 O*NET tasks scored against their work activities, plus the simulator.
E-E-A-T Verified
Developed & audited by Gini Loh Automation Exposure
58%
weighted task index
20 of 20 tasks scored
Median Salary
$98,920 / year
BLS OEWS, May 2025 — this exact occupation
Employment Level
Medium
55,850 people hold this job — size, not growth
Projected 2025–35
+4.7%
employment change
4,300 openings a year · BLS line item
Pay by state
BLS OEWS publishes 52 state estimates for this occupation. Each is its own survey estimate, not the national median adjusted for local prices.
$63,290 – $121,680
Highest medians
Maryland $121,680
California $113,530
Delaware $113,400
Connecticut $111,940
Washington $108,110
Lowest medians
Missouri $63,290
Louisiana $69,590
South Dakota $73,920
Nevada $74,210
Puerto Rico $76,400
State figures come from the 19-1029 row in the May 2025 state panel. States where the estimate was suppressed or too small to publish are absent.
What the score is made of
Each task is mapped to its work activities, scored for AI automation potential, and weighted by how important those activities are to the job. These are the components.
Automation potential 58%
Importance-weighted mean of the scored tasks. This is the headline index.
Physical work 0%
Share of weighted tasks in hands-on or machine-operating work activities.
Judgment & influence 81%
Share in analysis, decisions, teaching, directing, advising, or caring for others.
Routine administration 5%
Share in documenting, recording, and administrative activities.
Rewritten by O*NET 0%
Share of weighted tasks O*NET rewrote in a recent update. Reported, not folded into the score.
Consequence of error 11/100
Low
O*NET's own rating of how serious an uncorrectable mistake would be here — respondents, not us. Deliberately not in the index: the score measures what AI can do with the task content, and multiplying it by stakes would be our judgment dressed as data.
Constituent Task Mix
20 tasks for this occupation, weighted by work-activity importance. Shares below are of total task weight, not task count.
High exposure (≥ 60% of the task is automatable) 74%
15 tasks
Medium exposure (≥ 35%) 21%
4 tasks
Low exposure (below 35%) 5%
1 tasks
How would you survive?
Move your week away from the high-exposure tasks and toward the ones that need judgment. The simulator recalculates the index as you shift the mix.
Simulate Upskilling →Constituent Tasks
#16794 · Core · 64% automatable
Consult with researchers to analyze problems, recommend technology-based solutions, or determine computational strategies.
decision support — New-tech advice needs organizational context judgment
Develop software or applications for scientific or technical use. Advise others on the development or use of new technologies.
#16781 · Core · 62% automatable
Provide statistical and computational tools for biologically based activities, such as genetic analysis, measurement of gene expression, or gene function determination.
text generation — Scientific code drafted; domain validation by humans
Develop software or applications for scientific or technical use.
#16795 · Core · 35% automatable
Analyze large molecular datasets, such as raw microarray data, genomic sequence data, or proteomics data, for clinical or basic research purposes.
physical manipulation — Biological sample analysis needs wet-lab execution
Analyze biological samples.
#16790 · Core · 62% automatable
Create novel computational approaches and analytical tools as required by research goals.
text generation — Scientific code drafted; domain validation by humans
Develop software or applications for scientific or technical use.
#16791 · Core · 45% automatable
Compile data for use in activities, such as gene expression profiling, genome annotation, or structural bioinformatics.
data analysis — Bioinformatics aided; wet-lab work human
Research genetic characteristics or expression.
#16792 · Core · 72% automatable
Communicate research results through conference presentations, scientific publications, or project reports.
text generation — Reports drafted; presentations need human delivery
Prepare scientific or technical reports or presentations.
#16780 · Core · 62% automatable
Test new and updated bioinformatics tools and software.
text generation — Scientific code drafted; domain validation by humans
Develop software or applications for scientific or technical use.
#16789 · Core · 62% automatable
Design and apply bioinformatics algorithms including unsupervised and supervised machine learning, dynamic programming, or graphic algorithms.
text generation — Scientific code drafted; domain validation by humans
Develop software or applications for scientific or technical use.
#16786 · Core · 62% automatable
Develop new software applications or customize existing applications to meet specific scientific project needs.
text generation — Scientific code drafted; domain validation by humans
Develop software or applications for scientific or technical use.
#16793 · Core · 65% automatable
Manipulate publicly accessible, commercial, or proprietary genomic, proteomic, or post-genomic databases.
text generation — Technical DB design drafted; domain validation human
Develop technical or scientific databases.
#16787 · Core · 65% automatable
Develop data models and databases.
text generation — Technical DB design drafted; domain validation human
Develop technical or scientific databases.
#16777 · Core · 82% automatable
Keep abreast of new biochemistries, instrumentation, or software by reading scientific literature and attending professional conferences.
information retrieval — AI can search and summarize literature, human curates
Review professional literature to maintain professional knowledge.
#16785 · Core · 32% automatable
Direct the work of technicians and information technology staff applying bioinformatics tools or applications in areas such as proteomics, transcriptomics, metabolomics, or clinical bioinformatics.
interpersonal — Technical supervision requires judgment and accountability
Supervise scientific or technical personnel.
#16783 · Core · 40% automatable
Instruct others in the selection and use of bioinformatics tools.
interpersonal — Technical procedure training needs live demonstration and correction
Train personnel in technical or scientific procedures.
#16784 · Core · 62% automatable
Improve user interfaces to bioinformatics software and databases.
text generation — Scientific code drafted; domain validation by humans
Develop software or applications for scientific or technical use.
#16776 · Core · 62% automatable
Recommend new systems and processes to improve operations.
decision support — Drafts advice; accountability and context owned by human
Advise others on business or operational matters.
#16782 · Supplemental · 45% automatable
Prepare summary statistics of information regarding human genomes.
data analysis — Bioinformatics aided; wet-lab work human
Research genetic characteristics or expression.
#16778 · Supplemental · 60% automatable
Confer with departments, such as marketing, business development, or operations, to coordinate product development or improvement.
decision support — Technical design collaboration aided; humans own decisions
Collaborate with technical specialists to resolve design or development problems.
#16779 · Supplemental · 61% automatable
Collaborate with software developers in the development and modification of commercial bioinformatics software.
text generation — Scientific code drafted; domain validation by humans
Develop software or applications for scientific or technical use. Collaborate with technical specialists to resolve design or development problems.
#16788 · Supplemental · 62% automatable
Create or modify web-based bioinformatics tools.
text generation — Scientific code drafted; domain validation by humans
Develop software or applications for scientific or technical use.
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How this was scored
- 1. Tasks. O*NET task statements for this occupation, from a release whose newest record is dated 08/2026.
- 2. Work activities. Each task maps to 1–5 Detailed Work Activities; each activity belongs to one of 41 Generalized Work Activities that O*NET rates for importance (1–5) in this occupation.
- 3. Automation potential. Every work activity carries one score for how far current AI can perform it end-to-end. Tasks inherit the mean of their activities.
- 4. Weighting. Task weight = 0.35 + 0.65 × (importance − 1)/4, scaled by how often the task is performed and by 0.7 for Supplemental tasks. Importance is the task's own O*NET rating where it exists (18,369 of 18,838 tasks); the rest inherit their work activities' importance. The index is the weighted mean.
Check: across the 910 occupations where O*NET also rates how far machine speed sets the pace, this index moves opposite to it (Spearman ρ = -0.49). The more a job is paced by equipment, the less of its task content AI can address — the relationship you would expect if the scoring is working.
Sources · O*NET 31.0 (task records to 08/2026) · BLS OEWS May 2025 · BLS projections 2025-2035 · rubric v2.1 · tasks for O*NET, not a forecast
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