Automation index

How much of a Window installer’s job can AI already do?

A measured opening comes earlier. Closing, insulating and not trapping the lintel do not.

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

Task comparison

IS WORK OVER?

19%

AI can already do 19% of this job.

#502 / 633

Window 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
Measure openings, subframe and the order. Estimate No public source provides a precise figure. This is our estimate.	16%	32%	+5.1
Set, plumb and the missing packer. Estimate No public source provides a precise figure. This is our estimate.	22%	8%	+1.8
Foam, seal and the sweating lintel. Estimate No public source provides a precise figure. This is our estimate.	16%	22%	+3.5
Fittings, adjust and the sash that rubs. Estimate No public source provides a precise figure. This is our estimate.	16%	28%	+4.5
The lived-in house and not leaving the room freezing. Estimate No public source provides a precise figure. This is our estimate.	16%	18%	+2.9
That it closes, and not trapping the lintel. Estimate No public source provides a precise figure. This is our estimate.	14%	12%	+1.7
Total	100%		19%

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

IS WORK OVER?

25%

#230 / 633

Biomedical engineer

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
Design, validate and document clinical devices and software. Estimate No public source provides a precise figure. This is our estimate.	18%	48%	+8.6
Tests, MDR and risk. Estimate No public source provides a precise figure. This is our estimate.	16%	32%	+5.1
Hospital, clinicians and maintenance. Estimate No public source provides a precise figure. This is our estimate.	16%	22%	+3.5
Vendors, quality and traceability. Estimate No public source provides a precise figure. This is our estimate.	16%	18%	+2.9
User training and post-market watch. Estimate No public source provides a precise figure. This is our estimate.	18%	16%	+2.9
Not putting a half-built prototype in theatre. Estimate No public source provides a precise figure. This is our estimate.	16%	14%	+2.2
Total	100%		25%

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