Automation index

How much of a Furniture assembler’s job can AI already do?

A manual comes earlier. A wardrobe that does not wobble and fits up the stairs 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.

#523 / 643

Furniture assembler

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
Manual, fittings and the parts count. Estimate No public source provides a precise figure. This is our estimate.	16%	32%	+5.1
Assemble, plumb and the wrong screw. Estimate No public source provides a precise figure. This is our estimate.	22%	8%	+1.8
Up the stairs without marking the wall. Estimate No public source provides a precise figure. This is our estimate.	16%	22%	+3.5
Adjust doors, drawers and the client watching. Estimate No public source provides a precise figure. This is our estimate.	16%	28%	+4.5
The small flat and the piece that will not fit whole. Estimate No public source provides a precise figure. This is our estimate.	16%	18%	+2.9
That it does not wobble, and not leaving a fitting out. 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?

26%

#216 / 643

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