Automation index
How much of a Early childhood educator’s job can AI already do?
An activity sheet writes itself. Holding twenty three-year-olds, a nappy, and a parent does not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Early childhood educator
15%
AI can already do 15% of this job.
#632 / 643
View rankingEarly childhood educator
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 |
|---|---|---|---|
Plan activities, reports and rubrics.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Write to families.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 22% | +3.1 |
Care, play and hold the room.EstimateNo public source provides a precise figure. This is our estimate. | 24% | 6% | +1.4 |
Hygiene, meals and naps.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 10% | +1.6 |
Watch development and talk to the family.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
Coordinate staff and safeguarding.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 14% | +2 |
| Total | 100% | 15% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Plan activities, reports and rubrics
- Write to families
- Care, play and hold the room
- Hygiene, meals and naps
- Watch development and talk to the family
- Coordinate staff and safeguarding
- educador infantil
- maestro de infantil
- preschool
- jardin de infancia
Sources for this job· O*NET 25-2011.00· ESCO/ISCO 2342
IS WORK OVER?
ML engineer
29%
#64 / 643
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