Automation index
How much of a Land registrar’s job can AI already do?
An index search and a first entry speed up. Qualifying a title and owning the folio do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Land registrar
28%
AI can already do 28% of this job.
#116 / 623
View rankingLand registrar
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 |
|---|---|---|---|
Review deeds, simple notes and certificates.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 48% | +8.6 |
Cross indexes, encumbrances and graphic bases.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Make entries and process documents.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 22% | +4 |
Deal with presenters, notaries and the administration.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Decide appeals and qualification queries.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Keep the folio, the archive and the public record.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 28% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Make entries and process documents
- Decide appeals and qualification queries
- registrador
- registro de la propiedad
- land registrar
Sources for this job· O*NET 23-2093.00· 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