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AI Automation Index
Will AI Replace Statisticians?
Task-level automation exposure for Statisticians: 19 O*NET tasks scored against their work activities, plus the simulator.
E-E-A-T Verified
Developed & audited by Gini Loh Automation Exposure
69%
weighted task index
19 of 19 tasks scored
Median Salary
$105,650 / year
BLS OEWS, May 2025 — this exact occupation
Employment Level
Medium
29,030 people hold this job — size, not growth
Projected 2025–35
+11.0%
employment change
1,900 openings a year · BLS line item
Pay by state
BLS OEWS publishes 46 state estimates for this occupation. Each is its own survey estimate, not the national median adjusted for local prices.
$49,730 – $140,670
Highest medians
District of Columbia $140,670
New York $136,020
California $135,960
Maryland $132,620
Illinois $119,760
Lowest medians
Puerto Rico $49,730
Mississippi $64,670
South Carolina $65,450
Missouri $66,330
North Dakota $75,890
State figures come from the 15-2041 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 69%
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 82%
Share in analysis, decisions, teaching, directing, advising, or caring for others.
Routine administration 16%
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 5/100
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) 80%
15 tasks
Medium exposure (≥ 35%) 17%
3 tasks
Low exposure (below 35%) 4%
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
#8956 · Core · 82% automatable
Analyze and interpret statistical data to identify significant differences in relationships among sources of information.
data analysis — Trend and relationship detection in data
Analyze data to identify trends or relationships among variables.
#8958 · Core · 64% automatable
Evaluate the statistical methods and procedures used to obtain data to ensure validity, applicability, efficiency, and accuracy.
data analysis — Recommends analysis methods; human selects
Evaluate project designs to determine adequacy or feasibility. Determine appropriate methods for data analysis.
#8953 · Core · 74% automatable
Report results of statistical analyses, including information in the form of graphs, charts, and tables.
data analysis — Analytical reports drafted; human validates analysis
Prepare graphics or other visual representations of information. Prepare analytical reports.
#21100 · Core · 67% automatable
Determine whether statistical methods are appropriate, based on user needs or research questions of interest.
data analysis — Recommends analysis methods; human selects
Evaluate technical data to determine effect on designs or plans. Determine appropriate methods for data analysis.
#8957 · Core · 82% automatable
Prepare data for processing by organizing information, checking for inaccuracies, and adjusting and weighting the raw data.
data analysis — Data preparation for analysis automatable
Evaluate data quality. Prepare data for analysis.
#8966 · Core · 55% automatable
Develop and test experimental designs, sampling techniques, and analytical methods.
decision support — Study designs aided; scientific accountability human
Design research studies to obtain scientific information.
#8955 · Core · 82% automatable
Identify relationships and trends in data, as well as any factors that could affect the results of research.
data analysis — Trend and relationship detection in data
Analyze data to identify trends or relationships among variables.
#20194 · Core · 55% automatable
Present statistical and nonstatistical results, using charts, bullets, and graphs, in meetings or conferences to audiences such as clients, peers, and students.
interpersonal — Presentation delivery and Q&A are human; AI drafts slides
Present research results to others.
#8961 · Core · 55% automatable
Design research projects that apply valid scientific techniques, and use information obtained from baselines or historical data to structure uncompromised and efficient analyses.
decision support — Study designs aided; scientific accountability human
Design research studies to obtain scientific information.
#8965 · Core · 80% automatable
Adapt statistical methods to solve specific problems in many fields, such as economics, biology, and engineering.
data analysis — Applies math/statistics to problems; human validates
Apply mathematical principles or statistical approaches to solve problems in scientific or applied fields.
#8959 · Core · 78% automatable
Evaluate sources of information to determine any limitations, in terms of reliability or usability.
data analysis — Data quality profiling automated, human sets standards
Evaluate data quality.
#8954 · Core · 85% automatable
Process large amounts of data for statistical modeling and graphic analysis, using computers.
data analysis — Data preparation for analysis automatable
Prepare data for analysis.
#20193 · Core · 62% automatable
Develop software applications or programming for statistical modeling and graphic analysis.
text generation — Design drafts aided; system architecture needs human judgment
Design software applications.
#20192 · Core · 65% automatable
Report results of statistical analyses in peer-reviewed papers and technical manuals.
data analysis — Analytical reports drafted; human validates analysis
Present research results to others. Prepare analytical reports.
#8960 · Core · 64% automatable
Plan data collection methods for specific projects, and determine the types and sizes of sample groups to be used.
data analysis — Recommends analysis methods; human selects
Determine appropriate methods for data analysis. Design research studies to obtain scientific information.
#8964 · Core · 72% automatable
Apply sampling techniques, or use complete enumeration bases to determine and define groups to be surveyed.
data analysis — Recommends analysis methods; human selects
Determine appropriate methods for data analysis.
#8967 · Core · 80% automatable
Examine theories, such as those of probability and inference, to discover mathematical bases for new or improved methods of obtaining and evaluating numerical data.
information retrieval — AI scans trend sources; human weighs relevance
Update knowledge about emerging industry or technology trends.
#8963 · Core · 32% automatable
Supervise and provide instructions for workers collecting and tabulating data.
interpersonal — IT staff supervision needs human judgment and accountability
Supervise information technology personnel.
#21101 · Supplemental · 66% automatable
Prepare and structure data warehouses for storing data.
text generation — Code generation strong; human reviews and integrates
Write computer programming code. Implement security measures for computer or information systems. Install computer software.
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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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