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AI Automation Index
Will AI Replace Tax Preparers?
Task-level automation exposure for Tax Preparers: 12 O*NET tasks scored against their work activities, plus the simulator.
E-E-A-T Verified
Developed & audited by Gini Loh Automation Exposure
70%
weighted task index
12 of 12 tasks scored
Median Salary
$54,920 / year
BLS OEWS, May 2025 — this exact occupation
Employment Level
Medium
76,480 people hold this job — size, not growth
Projected 2025–35
+4.7%
employment change
9,400 openings a year · BLS line item
Pay by state
BLS OEWS publishes 49 state estimates for this occupation. Each is its own survey estimate, not the national median adjusted for local prices.
$33,280 – $71,850
Highest medians
New York $71,850
California $70,030
Utah $66,720
Montana $63,080
Missouri $62,780
Lowest medians
Alabama $33,280
Hawaii $34,720
Arkansas $35,040
West Virginia $35,880
Tennessee $36,600
State figures come from the 13-2082 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 70%
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 48%
Share in analysis, decisions, teaching, directing, advising, or caring for others.
Routine administration 0%
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
12 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) 82%
10 tasks
Medium exposure (≥ 35%) 9%
1 tasks
Low exposure (below 35%) 9%
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
#7358 · Core · 82% automatable
Compute taxes owed or overpaid, using adding machines or personal computers, and complete entries on forms, following tax form instructions and tax tables.
data analysis — Tax calculation with rules; human review
Calculate tax information.
#7360 · Core · 82% automatable
Use all appropriate adjustments, deductions, and credits to keep clients' taxes to a minimum.
data analysis — Tax calculation with rules; human review
Calculate tax information.
#7363 · Core · 55% automatable
Furnish taxpayers with sufficient information and advice to ensure correct tax form completion.
decision support — AI drafts financial guidance; accountability and tailoring human
Advise others on financial matters.
#7361 · Core · 30% automatable
Interview clients to obtain additional information on taxable income and deductible expenses and allowances.
interpersonal — Client financial interviews need trust and judgment
Interview clients to gather financial information.
#7359 · Core · 82% automatable
Prepare or assist in preparing simple to complex tax returns for individuals or small businesses.
data analysis — Tax calculation with rules; human review
Calculate tax information.
#7362 · Core · 65% automatable
Review financial records, such as income statements and documentation of expenditures to determine forms needed to prepare tax returns.
data analysis — Examines records for anomalies; human validates findings
Examine financial records.
#7366 · Core · 82% automatable
Check data input or verify totals on forms prepared by others to detect errors in arithmetic, data entry, or procedures.
data analysis — Cross-checking records against criteria, human spot-checks
Verify accuracy of records.
#20364 · Core · 62% automatable
Explain federal and state tax laws to individuals and companies.
information retrieval — AI explains policies; human handles live questions
Explain regulations, policies, or procedures.
#20363 · Core · 61% automatable
Answer questions and provide future tax planning to clients.
text generation — Written correspondence to customers is largely automatable
Develop financial plans for clients. Correspond with customers to answer questions or resolve complaints.
#7364 · Core · 78% automatable
Consult tax law handbooks or bulletins to determine procedures for preparation of atypical returns.
information retrieval — AI surfaces updates; human internalizes knowledge
Update professional knowledge.
#7365 · Core · 82% automatable
Calculate form preparation fees according to return complexity and processing time required.
data analysis — Tax calculation with rules; human review
Calculate tax information.
#21165 · Core · 80% automatable
Schedule appointments with clients.
decision support — Calendar scheduling automatable; exceptions need human judgment
Schedule appointments.
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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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