AI Automation Index

Will AI Replace Legal Secretaries and Administrative Assistants?

Task-level automation exposure for Legal Secretaries and Administrative Assistants: 14 O*NET tasks scored against their work activities, plus the simulator.

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Developed & audited by Gini Loh

Automation Exposure

67% weighted task index

14 of 14 tasks scored

Median Salary

$55,570 / year

BLS OEWS, May 2025 — this exact occupation

Employment Level

High

156,280 people hold this job — size, not growth

Projected 2025–35

-5.2% employment change

18,100 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.

$31,000 – $99,280

Highest medians

District of Columbia $99,280

New York $72,230

Connecticut $66,220

Delaware $66,130

Minnesota $63,180

Lowest medians

Puerto Rico $31,000

Kansas $36,620

Missouri $38,560

Kentucky $39,300

Arkansas $41,410

State figures come from the 43-6012 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 67%

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

Physical work 9%

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

Judgment & influence 21%

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

Routine administration 52%

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 10/100 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

14 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) 83%

12 tasks

Medium exposure (≥ 35%) 8%

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

#768 · Core · 70% automatable

Organize and maintain law libraries, documents, and case files.

text generation — Recording legal info drafted; human confirms facts

Record information about legal matters.

#764 · Core · 75% automatable

Mail, fax, or arrange for delivery of legal correspondence to clients, witnesses, and court officials.

text generation — Sending digital materials automatable; physical mailing human

Send information, materials or documentation.

#18780 · Core · 68% automatable

Prepare and distribute invoices to bill clients or pay account expenses.

text generation — Compliance docs drafted; human owns regulatory accuracy

Prepare documentation for contracts, transactions, or regulatory compliance.

#21078 · Core · 73% automatable

Prepare, proofread, or process legal documents, such as summonses, subpoenas, complaints, appeals, motions, or pretrial agreements.

text generation — Proofreading text for accuracy is core LLM strength

Proofread documents, records, or other files to ensure accuracy. Prepare legal documents.

#767 · Core · 20% automatable

Make photocopies of correspondence, documents, and other printed matter.

physical manipulation — Physical operation of office machines and jams

Operate office equipment.

#769 · Core · 55% automatable

Assist attorneys in collecting information such as employment, medical, and other records.

information retrieval — Data gathering aided; verification and consent human

Obtain personal or financial information about customers or applicants.

#774 · Core · 68% automatable

Complete various forms, such as accident reports, trial and courtroom requests, and applications for clients.

text generation — Compliance docs drafted; human owns regulatory accuracy

Prepare documentation for contracts, transactions, or regulatory compliance.

#765 · Core · 72% automatable

Receive and place telephone calls.

interpersonal — Call routing automatable; nuanced calls need human

Answer telephones to direct calls or provide information.

#766 · Core · 80% automatable

Schedule and make appointments.

decision support — Calendar scheduling automatable; exceptions need human judgment

Schedule appointments.

#771 · Core · 67% automatable

Draft and type office memos.

text generation — Routine business correspondence fully draftable by AI

Prepare business correspondence. Issue documentation or identification to customers or employees.

#773 · Supplemental · 72% automatable

Submit articles and information from searches to attorneys for review and approval for use.

text generation — Providing routine information to coworkers is text-based

Provide information to coworkers.

#21079 · Supplemental · 80% automatable

Make travel arrangements for attorneys.

information retrieval — Booking travel and reservations via tools

Make travel, accommodations, or entertainment arrangements for others.

#770 · Supplemental · 72% automatable

Attend legal meetings, such as client interviews, hearings, or depositions, and take notes.

text generation — Summarize meeting proceedings; human verifies accuracy

Record information from meetings or other formal proceedings.

#772 · Supplemental · 85% automatable

Review legal publications and perform database searches to identify laws and court decisions relevant to pending cases.

information retrieval — Search files and references for info

Search files, databases or reference materials to obtain needed information.

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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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