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AI Automation Index
Will AI Replace Title Examiners, Abstractors, and Searchers?
Task-level automation exposure for Title Examiners, Abstractors, and Searchers: 17 O*NET tasks scored against their work activities, plus the simulator.
E-E-A-T Verified
Developed & audited by Gini Loh Automation Exposure
64%
weighted task index
17 of 17 tasks scored
Median Salary
$58,650 / year
BLS OEWS, May 2025 — this exact occupation
Employment Level
Medium
48,580 people hold this job — size, not growth
Projected 2025–35
+2.1%
employment change
5,300 openings a year · BLS line item
Pay by state
BLS OEWS publishes 48 state estimates for this occupation. Each is its own survey estimate, not the national median adjusted for local prices.
$38,470 – $77,600
Highest medians
Colorado $77,600
West Virginia $77,230
Oregon $76,930
Alaska $75,960
Nevada $71,050
Lowest medians
Louisiana $38,470
Puerto Rico $44,570
Indiana $46,150
Iowa $46,330
South Carolina $46,740
State figures come from the 23-2093 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 64%
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 26%
Share in analysis, decisions, teaching, directing, advising, or caring for others.
Routine administration 36%
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 48/100
Moderate
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
17 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) 52%
9 tasks
Medium exposure (≥ 35%) 42%
7 tasks
Low exposure (below 35%) 6%
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
#9241 · Core · 58% automatable
Prepare and issue title commitments and title insurance policies, based on information compiled from title searches.
text generation — Draft legal documents; attorney owns final
Prepare legal documents.
#9231 · Core · 64% automatable
Examine documentation such as mortgages, liens, judgments, easements, plat books, maps, contracts, and agreements to verify factors such as properties' legal descriptions, ownership, or restrictions.
information retrieval — Legal research strong, decision human
Evaluate information related to legal matters in public or personal records. Research relevant legal materials to aid decision making.
#9234 · Core · 62% automatable
Examine individual titles to determine if restrictions, such as delinquent taxes, will affect titles and limit property use.
information retrieval — Legal record evaluation aided, human decides
Evaluate information related to legal matters in public or personal records.
#9235 · Core · 58% automatable
Prepare reports describing any title encumbrances encountered during searching activities and outlining actions needed to clear titles.
text generation — Draft legal documents; attorney owns final
Prepare legal documents.
#9230 · Core · 58% automatable
Prepare lists of all legal instruments applying to a specific piece of land and the buildings on it.
text generation — Draft legal documents; attorney owns final
Prepare legal documents.
#9233 · Core · 58% automatable
Copy or summarize recorded documents, such as mortgages, trust deeds, and contracts, that affect property titles.
text generation — Draft legal documents; attorney owns final
Prepare legal documents.
#9236 · Core · 60% automatable
Verify accuracy and completeness of land-related documents accepted for registration, preparing rejection notices when documents are not acceptable.
information retrieval — Legal record evaluation aided, human decides
Evaluate information related to legal matters in public or personal records. Prepare legal documents.
#9232 · Core · 84% automatable
Read search requests to ascertain types of title evidence required and to obtain descriptions of properties and names of involved parties.
information retrieval — Gather technical info from documents is core
Read documents to gather technical information.
#9243 · Core · 81% automatable
Retrieve and examine real estate closing files for accuracy and to ensure that information included is recorded and executed according to regulations.
information retrieval — Search files and references for info
Obtain property information. Search files, databases or reference materials to obtain needed information. Review documents or materials for compliance with policies or regulations.
#9237 · Core · 33% automatable
Confer with realtors, lending institution personnel, buyers, sellers, contractors, surveyors, and courthouse personnel to exchange title-related information or to resolve problems.
interpersonal — Court staff conferral is relational and context-bound
Meet with individuals involved in legal processes to provide information and clarify issues. Confer with court staff to clarify information.
#9240 · Core · 82% automatable
Obtain maps or drawings delineating properties from company title plants, county surveyors, or assessors' offices.
data analysis — Compiling data or documentation is largely automatable
Obtain property information. Compile data or documentation.
#9238 · Core · 85% automatable
Enter into record-keeping systems appropriate data needed to create new title records or to update existing ones.
data analysis — Database updates routine and automatable
Update computer database information. Enter information into databases or software programs.
#9239 · Core · 55% automatable
Direct activities of workers who search records and examine titles, assigning, scheduling, and evaluating work, and providing technical guidance as necessary.
decision support — Legal scheduling coordination is largely clerical with judgment
Coordinate legal schedules or activities.
#9242 · Core · 53% automatable
Summarize pertinent legal or insurance details, or sections of statutes or case law from reference books for use in examinations or as proofs or ready reference.
information retrieval — Legal research strong, decision human
Collect evidence for legal proceedings. Evaluate information related to legal matters in public or personal records. Research relevant legal materials to aid decision making.
#9244 · Supplemental · 54% automatable
Prepare real estate closing statements, using knowledge and expertise in real estate procedures.
text generation — Compliance docs drafted; human owns regulatory accuracy
Prepare contracts or other transaction documents. Prepare documentation for contracts, transactions, or regulatory compliance. Contract real estate to clients.
#9245 · Supplemental · 66% automatable
Determine whether land-related documents can be registered under the relevant legislation, such as the Land Titles Act.
information retrieval — Legal research strong, decision human
Research relevant legal materials to aid decision making. Evaluate applicable laws and regulations to determine impact on organizational activities.
#9246 · Supplemental · 78% automatable
Assess fees related to registration of property-related documents.
data analysis — Cost estimation from inputs; human validates
Estimate costs of products, services, or materials.
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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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