Automation index
How much of a Section supervisor’s job can AI already do?
A section planogram comes earlier. The shelf being there and the shift running do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Section supervisor
22%
AI can already do 22% of this job.
#331 / 633
View rankingSection supervisor
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 |
|---|---|---|---|
Section, planogram and the order.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Shift, the gap and the one who is late.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
Customer, the complaint and the one who cannot find it.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Shrink, dates and not hiding them.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
The store manager, the peak and not leaving the shelf empty.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
The shelf, and the shift standing.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
- section
- planogram
- shrink
- dates
- store shifts
- complaints
- section orders
- gaps
Sources for this job· O*NET 41-1011.00· ESCO/ISCO 5222
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