Automation index
How much of a Cleaner’s job can AI already do?
A log and a checklist come earlier. Scrubbing a bathroom, a night shift, and a stain that will not lift do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Cleaner
16%
AI can already do 16% of this job.
#619 / 643
View rankingCleaner
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 |
|---|---|---|---|
Logs, checklists and product orders.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 32% | +4.5 |
Scrubber-driers and dosing.EstimateNo public source provides a precise figure. This is our estimate. | 12% | 22% | +2.6 |
Mop, vacuum, disinfect and restock.EstimateNo public source provides a precise figure. This is our estimate. | 26% | 10% | +2.6 |
Waste, linen and sensitive areas.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
Shifts, keys and dealing with users.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
Surprises: vomit, works, a complaint.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 16% | +2.6 |
| Total | 100% | 16% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Logs, checklists and product orders
- Scrubber-driers and dosing
- Mop, vacuum, disinfect and restock
- Waste, linen and sensitive areas
- Shifts, keys and dealing with users
- Surprises: vomit, works, a complaint
- limpiador
- limpiadora
- personal de limpieza
- cleaner
Sources for this job· O*NET 37-2011.00· ESCO/ISCO 9112
IS WORK OVER?
Last-mile manager
26%
#216 / 643
View rankingLast-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.
| Task | Share | AI | Points |
|---|---|---|---|
Zones, capacity and route cuts.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 44% | +7 |
KPIs, first attempt and incidents.EstimateNo 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.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 16% | +2.9 |
Peaks, rain and hiring too many or too few.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The angry customer and not dressing delivered.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
Cover the peak, and not leaving the neighbourhood orphaned.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
| Total | 100% | 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