Automation index

How much of a Home-automation installer’s job can AI already do?

A scene comes earlier. The house obeying without going dark do not.

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

Task comparison

IS WORK OVER?

23%

AI can already do 23% of this job.

#301 / 633

Home-automation installer

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
Diagram, buses and scene programming. Estimate No public source provides a precise figure. This is our estimate.	16%	40%	+6.4
Wire, sensors and the switch that does not answer. Estimate No public source provides a precise figure. This is our estimate.	18%	14%	+2.5
Integrate lights, climate and the client app. Estimate No public source provides a precise figure. This is our estimate.	16%	32%	+5.1
Train, guest mode and the one who breaks it. Estimate No public source provides a precise figure. This is our estimate.	16%	22%	+3.5
The lived-in house and not leaving it dark. Estimate No public source provides a precise figure. This is our estimate.	18%	18%	+3.2
That it obeys, and not needing the cloud for a switch. Estimate No public source provides a precise figure. This is our estimate.	16%	12%	+1.9
Total	100%		23%

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