Automation index
How much of a Court procurator’s job can AI already do?
A procedural filing comes earlier. Appearing, serving notice and not missing a deadline do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Court procurator
25%
AI can already do 25% of this job.
#250 / 623
View rankingCourt procurator
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 |
|---|---|---|---|
Draft filings, copies and notices.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 48% | +7.7 |
File, appear and collect at the court office.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 22% | +4 |
Track deadlines, listings and powers of attorney.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 12% | +2.2 |
Coordinate case status with lawyer and client.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Attend hearings, auctions and service of process.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Records, fees and the procurator’s account.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
| Total | 100% | 25% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- advise clients
- draft contracts
- litigate
- interpret law
- Draft filings, copies and notices
- Records, fees and the procurator’s account
- procurador
- procurator
Sources for this job· O*NET 23-2011.00· ESCO/ISCO 2619
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