Automation index
How much of a Secretary’s job can AI already do?
Minutes and a diary come earlier. Knowing what is not said, who gets in, and when to interrupt do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Secretary
28%
AI can already do 28% of this job.
#118 / 623
View rankingSecretary
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 |
|---|---|---|---|
Minutes, mail and template documents.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 48% | +10.6 |
Diary, travel and expenses.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 42% | +7.6 |
Filter calls and visitors.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Anticipate the boss and the conflict.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 15% | +2.4 |
Confidentiality and internal protocol.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 16% | +2.2 |
Board, clients and the team.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 18% | +2.5 |
| Total | 100% | 28% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Minutes, mail and template documents
- Diary, travel and expenses
- Filter calls and visitors
- Anticipate the boss and the conflict
- Confidentiality and internal protocol
- Board, clients and the team
- secretario
- secretaria
- secretary
- asistente administrativo senior
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