Automation index
How much of a Fundraiser’s job can AI already do?
A letter and a CRM come earlier. Asking for the money in person and getting it again do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Fundraiser
26%
AI can already do 26% of this job.
#194 / 623
View rankingFundraiser
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 |
|---|---|---|---|
Campaigns, CRM and the donor letter.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 48% | +7.7 |
The meeting, the no, and asking again.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
Events, legacies and the member base.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 36% | +5.8 |
Transparency, the annual report and what you can say.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The major donor, the board and the conflict of interest.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
That they give again, and not spending more than comes in.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 26% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- raise funds
- donor campaigns
- member CRM
- ask for legacies
- run events
- transparency report
- major donors
- keep members
Sources for this job· O*NET 13-1131.00· ESCO/ISCO 2432
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