Automation index
How much of a Telemarketer’s job can AI already do?
A script comes earlier. Them not hanging up in ten seconds, and not lying, do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Telemarketer
27%
AI can already do 27% of this job.
#188 / 623
View rankingTelemarketer
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 |
|---|---|---|---|
Script, queue and the shift.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 48% | +8.6 |
The call, the no and the one who insults.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Data, GDPR and not pushing extra.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Target, the bonus and not dressing contacts.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The supervisor, quality and not skipping the no.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
That they do not hang up, and not lying.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 27% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- scripts
- call queues
- GDPR
- targets
- call quality
- shifts
- contacts
- the no
Sources for this job· O*NET 41-9041.00· ESCO/ISCO 4222
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