Automation index
How much of a Legal secretary’s job can AI already do?
A stock filing comes earlier. The deadline not dying and counsel having the paper do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Legal secretary
27%
AI can already do 27% of this job.
#176 / 623
View rankingLegal secretary
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 |
|---|---|---|---|
Filings, deadlines and the court.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 48% | +8.6 |
Appointments, the client and the one who is late.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Copies, notices and the procurator.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Bill, the procurator and not losing the receipt.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Counsel, the rush and not sending the draft.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
The deadline alive, and the paper at court.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 27% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- filings
- procedural deadlines
- notices
- procurator
- office appointments
- copies
- receipts
- the court
Sources for this job· O*NET 43-6012.00· ESCO/ISCO 3343
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