Automation index
How much of a Category manager’s job can AI already do?
An assortment comes earlier. The category winning without fighting own-label do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Category manager
27%
AI can already do 27% of this job.
#138 / 623
View rankingCategory manager
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 |
|---|---|---|---|
Assortment, prices and the shelf.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 48% | +8.6 |
Supplier, the negotiation and the one that will not list.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Promo, the calendar and the one that clashes.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Data, margin and not killing the category.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Own-label, the clash and not hiding it.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
That the category wins.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
- assortment
- supplier negotiation
- category margin
- promos
- own-label
- the shelf
- calendar
- listings
Sources for this job· O*NET 11-2022.00· ESCO/ISCO 1221
IS WORK OVER?
Hydraulic engineer
27%
#164 / 623
View rankingHydraulic 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 |
|---|---|---|---|
Hydrology, flows and the floodplain model.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 48% | +8.6 |
Field, section and what LiDAR misses.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Drainage works, pumps and the outfall.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 36% | +5.8 |
Water permits and the river authority.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The flood, the village and the yes to the channel.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
That it does not flood, and not trusting the model alone.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
- model floodplains
- flows
- drainage
- channel works
- water permits
- pumps
- channel sections
- river authority
Sources for this job· O*NET 17-2051.00· ESCO/ISCO 2142