Automation index
How much of a Court case manager’s job can AI already do?
A filing nudge comes earlier. Not letting the deadline die, and the judge having the order, do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Court case manager
29%
AI can already do 29% of this job.
#61 / 623
View rankingCourt case manager
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 |
|---|---|---|---|
Drive the case, copies and the status.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 52% | +9.4 |
Steps, notices and gazetting.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Listings, diaries and the court that overflows.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The lawyer who rings, the citizen and the counter.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
File, certified copy and what the senior asks.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
The deadline, and not letting the case die.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
- drive proceedings
- serve notices
- listings
- gazetting
- certified copies
- court archive
- procedural deadlines
- serve the counter
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