IS WORK OVER?

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

Category 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.

TaskShareAIPoints
Assortment, prices and the shelf.
Estimate

No public source provides a precise figure. This is our estimate.

18%48%+8.6
Supplier, the negotiation and the one that will not list.
Estimate

No public source provides a precise figure. This is our estimate.

16%40%+6.4
Promo, the calendar and the one that clashes.
Estimate

No public source provides a precise figure. This is our estimate.

16%22%+3.5
Data, margin and not killing the category.
Estimate

No public source provides a precise figure. This is our estimate.

16%28%+4.5
Own-label, the clash and not hiding it.
Estimate

No public source provides a precise figure. This is our estimate.

18%14%+2.5
That the category wins.
Estimate

No public source provides a precise figure. This is our estimate.

16%12%+1.9
Total100%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

Last-mile 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.

TaskShareAIPoints
Zones, capacity and route cuts.
Estimate

No public source provides a precise figure. This is our estimate.

16%44%+7
KPIs, first attempt and incidents.
Estimate

No public source provides a precise figure. This is our estimate.

16%36%+5.8
The courier, the lobby and the one who will not open.
Estimate

No public source provides a precise figure. This is our estimate.

18%16%+2.9
Peaks, rain and hiring too many or too few.
Estimate

No public source provides a precise figure. This is our estimate.

16%28%+4.5
The angry customer and not dressing delivered.
Estimate

No public source provides a precise figure. This is our estimate.

18%18%+3.2
Cover the peak, and not leaving the neighbourhood orphaned.
Estimate

No public source provides a precise figure. This is our estimate.

16%14%+2.2
Total100%26%

Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low

Skills and activities

  • delivery zones
  • first attempt
  • delivery KPIs
  • last-mile peaks
  • lobbies
  • delivery incidents
  • courier capacity
  • cut routes

Sources for this job· O*NET 11-3071.00· ESCO/ISCO 1324

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