Automation index
How much of a Court processor’s job can AI already do?
A register and a scanned filing come earlier. Not losing the claim in the pile do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Court processor
29%
AI can already do 29% of this job.
#75 / 623
View rankingCourt processor
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 |
|---|---|---|---|
Register, scanning and the case number.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 52% | +9.4 |
Allocation, copies and service on the parties.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Letters, rogatory requests and what another court wants.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The counter, the queue and the paper that is not there.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
Archive, boxes and the old file.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Not losing the filing, and balancing the register.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
| Total | 100% | 29% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- register filings
- scan claims
- allocate cases
- court letters
- letters of request
- court counter
- box archive
- serve the parties
Sources for this job· O*NET 23-2011.00· ESCO/ISCO 3411
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