Automation index
How much of a Community health worker’s job can AI already do?
A workshop and a leaflet come earlier. Reaching the person who never walks into the clinic do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Community health worker
24%
AI can already do 24% of this job.
#256 / 623
View rankingCommunity health 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 |
|---|---|---|---|
Health map, workshops and the calendar.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Street, doorway and the neighbour who does not come.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
Vaccines, screening and referring to the clinic.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Data, report and the health table.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The rumour, the distrust and not moralising.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
That they come, and not inflating workshop numbers.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
| Total | 100% | 24% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- health workshops
- health map
- door-to-door
- refer to clinic
- screening
- health table
- community vaccination
- health distrust
Sources for this job· O*NET 21-1094.00· ESCO/ISCO 3253
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