Will AI replace Insurance Underwriters?
In theory, AI could do about 57% of the work in Insurance Underwriters. In practice, as of late 2025, almost no one is actually using it that way — yet.
O*NET-SOC 13-2053
How your 7 core tasks split
Top = what GPT-4 judged AI could speed up. Bottom = how much AI was actually used for these tasks (Anthropic's March 2026 report, usage from Aug & Nov 2025). The gap is the real story.
Back in 2023, GPT-4 judged AI could, in theory, assist with a moderate share of this job's tasks (~57%). By late 2025, real-world AI use had reached about 6% of its task activity (still rare). The gap between that 2023 forecast and today is the real story.
Where this job sits among 738 jobs
Each dot is one of 738 U.S. jobs. Right = AI can do more of it. Up = AI is actually used more.
Don't trust a single AI-risk score here
For this job, the signals disagree sharply. AI's theoretical reach looks moderate (~57%), but real-world use is only ~6%, and how much AI "can" do shifts wildly by model — one 2026 study found the share of "high-risk" jobs swung 2.7% to 51.5% just by changing which AI did the rating. This page shows the spread instead of pretending there's one number.
See all 7 tasks, ratedBased on real task-level AI scores — click to collapse
- Write to field representatives, medical personnel, or others to obtain further information, quote rates, or explain company underwriting policies.
- Examine documents to determine degree of risk from factors such as applicant health, financial standing and value, and condition of property.
- Decline excessive risks.
- Evaluate possibility of losses due to catastrophe or excessive insurance.
- Review company records to determine amount of insurance in force on single risk or group of closely related risks.
- 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.
- Authorize reinsurance of policy when risk is high.
- ⚠️ None — every core task is at least partly within AI's reach. The job won't vanish, but almost all of it changes.
How we measured this — and how fresh it is
AI's theoretical reach data: 2023
From GPTs-are-GPTs (Eloundou et al.), where GPT-4 rated how much of each task an AI tool could meaningfully speed up. This is the most recent open, commercially-usable occupation-level potential dataset — it dates to 2023. Newer multi-model re-runs exist but swing wildly (one 2026 study saw "high-risk" jobs range 2.7%–51.5% by model) and aren't openly licensed, so we show the stable 2023 baseline and pair it with newer real-world data.
Real-world AI use 2026 report
From the Anthropic Economic Index, which observes how real Claude conversations map onto each occupation's tasks. Published in Anthropic's March 2026 labor-market report, based on usage measured in Aug & Nov 2025 (Sonnet 4 / 4.5).
Task list & ratings O*NET 30.3
Tasks come from O*NET 30.3. Each task's "AI can do / speeds up / still on you" tier uses the real task-level exposure scores from GPTs-are-GPTs (E1 / E2 / E0) — not a guess from keywords.
Sources: O*NET 30.3 (CC BY 4.0) · GPTs-are-GPTs (MIT, 2023) · Anthropic Economic Index (CC BY, Aug & Nov 2025). Page compiled June 2026. "O*NET" is a trademark of the U.S. Department of Labor.
This page is for general informational purposes only and is not career, financial, or employment advice. AI exposure reflects research estimates of task overlap, not predictions about any individual's job, employer, or future employment.