Automation index
How much of a Notary’s job can AI already do?
A standard deed drafts faster. Identifying the parties, warning them, and authenticating does not fit a model.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Notary
22%
AI can already do 22% of this job.
#356 / 633
View rankingNotary
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 |
|---|---|---|---|
Prepare drafts and deeds from templates.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 42% | +9.2 |
Check liens, titles and registry data.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 35% | +5.6 |
Advise parties on the act’s consequences.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Identify, authorise and authenticate in person.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 8% | +1.4 |
Protocolise, file at the registry and keep the record.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
Resolve conflicts between parties at signing.EstimateNo public source provides a precise figure. This is our estimate. | 12% | 10% | +1.2 |
| Total | 100% | 22% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- advise clients
- draft contracts
- litigate
- interpret law
- Prepare drafts and deeds from templates
- Check liens, titles and registry data
- Advise parties on the act’s consequences
- notario
- notaria
- notary
Sources for this job· O*NET 23-1011.00· ESCO/ISCO 2619
IS WORK OVER?
ML engineer
29%
#64 / 633
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