Automation index

How much of a Appliance technician’s job can AI already do?

An error code comes earlier. Diagnosing in someone else’s kitchen and leaving it washing do not.

This percentage does not predict how many people will lose their jobs.

Task comparison

IS WORK OVER?

22%

AI can already do 22% of this job.

#385 / 633

Appliance technician

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
Code, history and the spare. Estimate No public source provides a precise figure. This is our estimate.	16%	40%	+6.4
Strip, test and the built-in one. Estimate No public source provides a precise figure. This is our estimate.	18%	12%	+2.2
Order the part, second visit and the client who is out. Estimate No public source provides a precise figure. This is our estimate.	16%	28%	+4.5
Warranty, misuse and what it does not cover. Estimate No public source provides a precise figure. This is our estimate.	16%	22%	+3.5
The tight kitchen and not marking the unit. Estimate No public source provides a precise figure. This is our estimate.	18%	18%	+3.2
Leave it washing, and not swapping the board blind. Estimate No 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

IS WORK OVER?

26%

#211 / 633

Last-mile manager

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. 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
Total	100%		26%

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