Automation index
How much of a Warehouse supervisor’s job can AI already do?
A dock dashboard comes earlier. Getting the shift out without mixing the goods do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Warehouse supervisor
24%
AI can already do 24% of this job.
#270 / 633
View rankingWarehouse 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 |
|---|---|---|---|
Docks, waves and the shift plan.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
The team, the peak and the one who cannot keep up.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
Stock, shrink and the mystery gap.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Safety, forklifts and the near miss.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The late haulier and not mixing the docks.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
Get the shift out, and not mixing the goods.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
| Total | 100% | 24% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- plan docks
- waves
- warehouse shift
- shrink
- forklifts
- near misses
- outbound peaks
- mixed goods
Sources for this job· O*NET 53-1048.00· ESCO/ISCO 1324
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