Automation index
How much of a Domestic worker’s job can AI already do?
A shopping list writes itself. Someone else’s house, a trust, and a back do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Domestic worker
15%
AI can already do 15% of this job.
#633 / 643
View rankingDomestic worker
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 |
|---|---|---|---|
Lists, online shopping and written errands.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 30% | +4.2 |
Menus and pantry organisation.EstimateNo public source provides a precise figure. This is our estimate. | 12% | 22% | +2.6 |
Clean, cook, iron and tidy.EstimateNo public source provides a precise figure. This is our estimate. | 26% | 8% | +2.1 |
Care for elders or children of the house.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 10% | +1.6 |
Trust, keys and boundaries.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
Relationship with the employing family.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
| Total | 100% | 15% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Lists, online shopping and written errands
- Menus and pantry organisation
- Clean, cook, iron and tidy
- Care for elders or children of the house
- Trust, keys and boundaries
- Relationship with the employing family
- empleada de hogar
- interno
- domestic worker
- trabajadora del hogar
Sources for this job· O*NET 39-9011.00· ESCO/ISCO 9111
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