IS WORK OVER?

Automation index

How much of a Textile engineer’s job can AI already do?

A dye recipe comes earlier. Getting the colour the same over ten thousand metres do not.

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

Task comparison

Textile engineer

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.

TaskShareAIPoints
Recipes, weaves and piece QC.
Estimate

No public source provides a precise figure. This is our estimate.

18%48%+8.6
Shop, looms and the warp break.
Estimate

No public source provides a precise figure. This is our estimate.

16%22%+3.5
Supplier, cotton and the sustainability card.
Estimate

No public source provides a precise figure. This is our estimate.

16%36%+5.8
Finishes, shrinkage and fastness tests.
Estimate

No public source provides a precise figure. This is our estimate.

16%28%+4.5
The brand, the deadline and the colour that misses.
Estimate

No public source provides a precise figure. This is our estimate.

18%14%+2.5
Ten thousand metres the same, and not hiding the bad yardage.
Estimate

No public source provides a precise figure. This is our estimate.

16%12%+1.9
Total100%27%

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

Skills and activities

  • dye recipes
  • weaves
  • looms
  • colour fastness
  • shrinkage
  • piece card
  • warp
  • textile finishes

Sources for this job· O*NET 17-2199.00· ESCO/ISCO 2141

Data architect

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.

TaskShareAIPoints
Models, lineage and the catalogue.
Estimate

No public source provides a precise figure. This is our estimate.

18%52%+9.4
Pipelines, quality and the data contract.
Estimate

No public source provides a precise figure. This is our estimate.

16%40%+6.4
The analyst who finds nothing and the mess.
Estimate

No public source provides a precise figure. This is our estimate.

16%22%+3.5
Privacy, retention and the field you cannot keep.
Estimate

No public source provides a precise figure. This is our estimate.

16%28%+4.5
The business, shadow IT and not building another lake.
Estimate

No public source provides a precise figure. This is our estimate.

18%14%+2.5
That it gets used, and not letting raw data rot.
Estimate

No public source provides a precise figure. This is our estimate.

16%12%+1.9
Total100%28%

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

Skills and activities

  • model data
  • lineage
  • data catalogue
  • data contracts
  • pipeline quality
  • retention
  • data lakes
  • privacy

Sources for this job· O*NET 15-1243.00· ESCO/ISCO 2511

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