Automation index
How much of a Volunteer coordinator’s job can AI already do?
A spreadsheet rota comes earlier. Getting them to come, stay, and do no harm, do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Volunteer coordinator
25%
AI can already do 25% of this job.
#218 / 623
View rankingVolunteer coordinator
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 |
|---|---|---|---|
Recruitment, rotas and the volunteer register.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 44% | +7 |
Train, support and the one who arrives lost.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 16% | +2.9 |
Insurance, agreements and the volunteering act.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Recognise, keep them and the one who burns out.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The clash between volunteer and staff.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
That they add, and do not replace paid work.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
- recruit volunteers
- make rotas
- train volunteers
- volunteering law
- retain volunteers
- volunteer insurance
- staff conflict
- volunteer register
Sources for this job· O*NET 11-9151.00· ESCO/ISCO 1344
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