Will AI replace Claims Adjusters, Examiners, and Investigators?
Work in Claims Adjusters, Examiners, and Investigators sits in the in-between: AI reaches some of it (~52% in theory) but is only measured doing about 8% today — part human, part machine.
O*NET-SOC 13-1031
How your 21 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 (~52%). By late 2025, real-world AI use had reached about 8% of its task activity (growing but still limited). 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 (~52%), but real-world use is only ~8%, 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 21 tasks, ratedBased on real task-level AI scores — click to collapse
- Enter claim payments, reserves and new claims on computer system, inputting concise yet sufficient file documentation.
- Examine claims forms and other records to determine insurance coverage.
- Analyze information gathered by investigation and report findings and recommendations.
- Pay and process claims within designated authority level.
- Investigate, evaluate, and settle claims, applying technical knowledge and human relations skills to effect fair and prompt disposal of cases and to contribute to a reduced loss ratio.
- Verify and analyze data used in settling claims to ensure that claims are valid and that settlements are made according to company practices and procedures.
- Review police reports, medical treatment records, medical bills, or physical property damage to determine the extent of liability.
- Investigate and assess damage to property and create or review property damage estimates.
- Interview or correspond with agents and claimants to correct errors or omissions and to investigate questionable claims.
- Interview or correspond with claimants, witnesses, police, physicians, or other relevant parties to determine claim settlement, denial, or review.
- Resolve complex, severe exposure claims, using high service oriented file handling.
- Adjust reserves or provide reserve recommendations to ensure that reserve activities are consistent with corporate policies.
- Confer with legal counsel on claims requiring litigation.
- Examine claims investigated by insurance adjusters, further investigating questionable claims to determine whether to authorize payments.
- Maintain claim files, such as records of settled claims and an inventory of claims requiring detailed analysis.
- Refer questionable claims to investigator or claims adjuster for investigation or settlement.
- Collect evidence to support contested claims in court.
- Contact or interview claimants, doctors, medical specialists, or employers to get additional information.
- Present cases and participate in their discussion at claim committee meetings.
- Report overpayments, underpayments, and other irregularities.
- Attend mediations or trials.
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.