Automation index
How much of a Sales promoter’s job can AI already do?
A promo briefing comes earlier. Stopping the customer without lying about the product do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Sales promoter
22%
AI can already do 22% of this job.
#371 / 633
View rankingSales promoter
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 |
|---|---|---|---|
Briefing, stand and the kit.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Shelf, the one who walks by and the one who stops.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
Tasting, allergies and not pushing.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Data, contacts and not inflating.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
The supervisor, the space and not covering the shelf.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
Stop the customer, and not lying.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 22% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- stands
- tasting
- promo briefing
- contacts
- allergies
- the shelf
- promo kit
- store supervisor
Sources for this job· O*NET 41-9011.00· ESCO/ISCO 5242
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