Automation index
How much of a Administrative agent’s job can AI already do?
A form and an appointment come earlier. A file that actually goes through, and the client not coming back, do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Administrative agent
25%
AI can already do 25% of this job.
#226 / 623
View rankingAdministrative agent
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 |
|---|---|---|---|
Forms, appointments and filings.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 48% | +8.6 |
Files, deadlines and requests.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Serve the client and explain procedures.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Tax, traffic, immigration and social security.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Archive, copies and follow-up.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 16% | +2.9 |
Not missing a deadline that costs a car.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
| Total | 100% | 25% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Forms, appointments and filings
- Files, deadlines and requests
- Serve the client and explain procedures
- Archive, copies and follow-up
- Not missing a deadline that costs a car
- gestor administrativo
- gestoria
- administrative agent
Sources for this job· O*NET 43-6011.00· 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