AI Automation Index

Will AI Replace Insurance Underwriters?

Task-level automation exposure for Insurance Underwriters: 7 O*NET tasks scored against their work activities, plus the simulator.

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Developed & audited by Gini Loh

Automation Exposure

53% weighted task index

7 of 7 tasks scored

Median Salary

$81,370 / year

BLS OEWS, May 2025 — this exact occupation

Employment Level

High

105,420 people hold this job — size, not growth

Projected 2025–35

-3.8% employment change

6,800 openings a year · BLS line item

Pay by state

BLS OEWS publishes 50 state estimates for this occupation. Each is its own survey estimate, not the national median adjusted for local prices.

$46,990 – $106,640

Highest medians

Massachusetts $106,640

New Hampshire $103,940

Nevada $101,440

Connecticut $100,690

Colorado $100,640

Lowest medians

Puerto Rico $46,990

Mississippi $59,250

Alabama $62,220

Wyoming $62,290

Idaho $62,530

State figures come from the 13-2053 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 53%

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 84%

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 3/100 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

7 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) 60%

4 tasks

Medium exposure (≥ 35%) 0%

0 tasks

Low exposure (below 35%) 40%

3 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.

New 08/2018 · Occupational Expert

Answer agents' questions about insurance coverage.

Constituent Tasks

#21045 · Core · 67% automatable

Examine documents to determine degree of risk from factors such as applicant health, financial standing and value, and condition of property.

data analysis — Statistical analysis of health data; clinician verifies

Assess financial status of clients. Analyze health-related data.

#1261 · Core · 32% automatable

Decline excessive risks.

decision support — Financial authorization carries legal accountability

Authorize financial actions.

#1262 · Core · 62% automatable

Write to field representatives, medical personnel, or others to obtain further information, quote rates, or explain company underwriting policies.

information retrieval — AI explains policies; human handles live questions

Explain regulations, policies, or procedures.

#1263 · Core · 60% automatable

Evaluate possibility of losses due to catastrophe or excessive insurance.

decision support — Risk assessment needs organizational context and judgment

Assess risks to business operations.

#1265 · Core · 82% automatable

Review company records to determine amount of insurance in force on single risk or group of closely related risks.

data analysis — Cross-checking records against criteria, human spot-checks

Verify accuracy of records.

#1264 · Core · 32% automatable

Decrease value of policy when risk is substandard and specify applicable endorsements or apply rating to ensure safe, profitable distribution of risks, using reference materials.

decision support — Financial authorization carries legal accountability

Authorize financial actions.

#1266 · Core · 32% automatable

Authorize reinsurance of policy when risk is high.

decision support — Financial authorization carries legal accountability

Authorize financial actions.

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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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