Automation index
How much of a Funeral director’s job can AI already do?
A form and an obituary come earlier. Collecting someone and looking after the family do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Funeral director
22%
AI can already do 22% of this job.
#330 / 633
View rankingFuneral director
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 |
|---|---|---|---|
Paperwork, chapel, burial and cremation.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Collection, thanatopraxy and care of the body.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 12% | +2.2 |
The family, the wake, and what is and is not possible.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
Notice, flowers, hearse and the minute-by-minute of the rite.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Cemetery, times and suppliers.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Dignity, legal times and owning the service.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
| Total | 100% | 22% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- arrange funerals
- file death paperwork
- collect the deceased
- support the family
- thanatopraxy
- run the wake
- arrange cremation
- notices
Sources for this job· O*NET 39-4031.00· ESCO/ISCO 5163
IS WORK OVER?
ML engineer
29%
#64 / 633
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