AI Automation Index

Will AI Replace Bioinformatics Technicians?

Task-level automation exposure for Bioinformatics Technicians: 19 O*NET tasks scored against their work activities, plus the simulator.

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Automation Exposure

68% weighted task index

19 of 19 tasks scored

Median Salary

$81,490 / year

BLS OEWS, May 2025 — this exact occupation

Employment Level

Low

3,720 people hold this job — size, not growth

Projected 2025–35

+7.1% employment change

200 openings a year · BLS line item

Pay by state

BLS OEWS publishes 13 state estimates for this occupation. Each is its own survey estimate, not the national median adjusted for local prices.

$36,820 – $180,500

Highest medians

California $180,500

North Carolina $101,850

Texas $93,090

Illinois $81,090

New York $79,460

Lowest medians

Oklahoma $36,820

Maryland $42,850

Iowa $49,020

Virginia $59,990

New Jersey $61,400

State figures come from the 15-2099 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 68%

Importance-weighted mean of the scored tasks. This is the headline index.

Physical work 6%

Share of weighted tasks in hands-on or machine-operating work activities.

Judgment & influence 55%

Share in analysis, decisions, teaching, directing, advising, or caring for others.

Routine administration 11%

Share in documenting, recording, and administrative activities.

Rewritten by O*NET 4%

Share of weighted tasks O*NET rewrote in a recent update. Reported, not folded into the score.

Consequence of error 9/100 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

19 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) 78%

15 tasks

Medium exposure (≥ 35%) 22%

4 tasks

Low exposure (below 35%) 0%

0 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 →

Emerging & rewritten tasks

Tasks O*NET added or rewrote for this occupation in its recent updates (05/2025 – 08/2026), with who proposed them. Additions are not in the scored task inventory — they are new work O*NET is still measuring, so they appear here and nowhere in the index. A Revision replaces a task that is scored, which is why only those move the "Rewritten by O*NET" share.

Revision 08/2026 · Incumbent

Train bioinformatics staff or researchers in the use of databases or performance of laboratory tasks.

Replaced: Train bioinformatics staff or researchers in the use of databases.

Constituent Tasks

#17721 · Core · 75% automatable

Conduct quality analyses of data inputs and resulting analyses or predictions.

data analysis — Operational/research data analysis; human interprets

Analyze operational or research data.

#17710 · Core · 48% automatable

Confer with researchers, clinicians, or information technology staff to determine data needs and programming requirements and to provide assistance with database-related research activities.

interpersonal — Real-time coworker coordination is relational work

Confer with coworkers to coordinate work activities.

#17709 · Core · 75% automatable

Analyze or manipulate bioinformatics data using software packages, statistical applications, or data mining techniques.

data analysis — Operational/research data analysis; human interprets

Analyze operational or research data.

#17714 · Core · 80% automatable

Document all database changes, modifications, or problems.

data analysis — Maintain operational records in structured systems

Maintain operational records.

#17708 · Core · 55% automatable

Monitor database performance and perform any necessary maintenance, upgrades, or repairs.

data analysis — Database performance metrics analyzable from logs

Assess database performance. Maintain computer equipment or software.

#17717 · Core · 75% automatable

Participate in the preparation of reports or scientific publications.

text generation — Technical reports drafted; human verifies content

Prepare research or technical reports.

#17716 · Core · 78% automatable

Maintain awareness of new and emerging computational methods and technologies.

information retrieval — AI curates updates; human maintains working knowledge

Maintain current knowledge related to work activities.

#17718 · Core · 68% automatable

Test new or updated software or tools and provide feedback to developers.

text generation — App code generated; human reviews and deploys

Develop computer or online applications.

#17722 · Core · 45% automatable

Confer with database users about project timelines and changes.

interpersonal — Coordinating work through live human interaction

Confer with organizational members to accomplish work activities.

#17711 · Core · 72% automatable

Create data management or error-checking procedures and user manuals.

text generation — AI drafts analysis procedures; human tailors and approves

Develop data analysis or data management procedures.

#17720 · Supplemental · 68% automatable

Write computer programs or scripts to be used in querying databases.

text generation — App code generated; human reviews and deploys

Develop computer or online applications.

#17706 · Supplemental · 68% automatable

Develop or maintain applications that process biologically based data into searchable databases for purposes of analysis, calculation, or presentation.

text generation — App code generated; human reviews and deploys

Develop computer or online applications.

#17707 · Supplemental · 85% automatable

Enter or retrieve information from structural databases, protein sequence motif databases, mutation databases, genomic databases or gene expression databases.

information retrieval — Search files and references for info

Search files, databases or reference materials to obtain needed information. Enter information into databases or software programs.

#17713 · Supplemental · 72% automatable

Develop or apply data mining and machine learning algorithms.

text generation — AI drafts analysis procedures; human tailors and approves

Develop data analysis or data management procedures.

#17715 · Supplemental · 68% automatable

Extend existing software programs, web-based interactive tools, or database queries as sequence management and analysis needs evolve.

text generation — App code generated; human reviews and deploys

Develop computer or online applications.

#17712 · Supplemental · 68% automatable

Design or implement web-based tools for querying large-scale biological databases.

text generation — App code generated; human reviews and deploys

Develop computer or online applications.

#17719 · Supplemental · 40% automatable Revision

Train bioinformatics staff or researchers in the use of databases.

interpersonal — General personnel training is human-led and relational

Train personnel.

#17723 · Supplemental · 88% automatable

Package bioinformatics data for submission to public repositories.

text generation — Document and data formatting automatable

Format digital documents, data, or images.

#17724 · Supplemental · 68% automatable

Perform routine system administrative functions, such as troubleshooting, back-ups, or upgrades.

data analysis — Backup configuration largely automatable with oversight

Create electronic data backup to prevent loss of information. Troubleshoot issues with computer applications or systems.

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Community Sentiment

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Method

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

See the full method →

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