Automation index
How much of a Insurance claims adjuster’s job can AI already do?
A claim form and some photos come earlier. Valuing the damage on site and closing the claim do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Insurance claims adjuster
29%
AI can already do 29% of this job.
#68 / 623
View rankingInsurance claims adjuster
Where the time goes
Task weights are our estimates based on O*NET frequency data, not time shares published by O*NET.
The time saving for each task is estimated separately.
| Task | Share | AI | Points |
|---|---|---|---|
Read policy, claim form and cover.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
On-site inspection, photos and facts.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
Value damage, parts and write the report.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 48% | +8.6 |
Negotiate with garage, injured party and insurer.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Fraud, recoveries and clash with another adjuster.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Close the file and own the valuation.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
| Total | 100% | 28% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- adjust claims
- assess damage
- inspect on site
- value repairs
- spot fraud
- negotiate payouts
- write survey reports
- close claims
Sources for this job· O*NET 13-1031.00· ESCO/ISCO 3315
IS WORK OVER?
ML engineer
29%
#64 / 623
View rankingML engineer
Where the time goes
Task weights are our estimates based on O*NET frequency data, not time shares published by O*NET.
The time saving for each task is estimated separately.
| Task | Share | AI | Points |
|---|---|---|---|
Train models, features and notebooks.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 48% | +10.6 |
Evaluate, document and compare runs.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 42% | +7.6 |
Deploy, monitor and retrain.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Talk to product about what a good error is.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 15% | +2.4 |
Dirty data, bias and model limits.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 18% | +2.5 |
On-call, incidents and rollback.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 16% | +2.2 |
| Total | 100% | 29% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Train models, features and notebooks
- Evaluate, document and compare runs
- Deploy, monitor and retrain
- Talk to product about what a good error is
- Dirty data, bias and model limits
- On-call, incidents and rollback
- machine learning engineer
- ingeniero ml
- mlops
Sources for this job· O*NET 15-1221.00· ESCO/ISCO 2511