Automation index
How much of a Merchandiser’s job can AI already do?
A planogram comes earlier. A shelf that sells without looking empty do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Merchandiser
22%
AI can already do 22% of this job.
#353 / 633
View rankingMerchandiser
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 |
|---|---|---|---|
Planogram, facings and the shelf.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Store, replenish and the one who will not follow the plan.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
Promo, the extra and the one that falls.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Data, sell-out and not inflating facings.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
The store manager, the space and fighting for the gap.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
That the shelf sells, and not empty.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
- planograms
- facings
- the shelf
- sell-out
- promos
- replenish
- store space
- gaps
Sources for this job· O*NET 41-9011.00· ESCO/ISCO 5223
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