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
IS WORK OVER?
Textile engineer
27%
AI can already do 27% of this job.
#165 / 623
View rankingTextile 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.
| Task | Share | AI | Points |
|---|---|---|---|
Recipes, weaves and piece QC.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 48% | +8.6 |
Shop, looms and the warp break.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Supplier, cotton and the sustainability card.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 36% | +5.8 |
Finishes, shrinkage and fastness tests.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The brand, the deadline and the colour that misses.EstimateNo 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.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 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
IS WORK OVER?
ML engineer
29%
#64 / 623
View rankingML 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.
| Task | Share | AI | Points |
|---|---|---|---|
Train models, features and notebooks.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 48% | +10.6 |
Evaluate, document and compare runs.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 42% | +7.6 |
Deploy, monitor and retrain.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Talk to product about what a good error is.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 15% | +2.4 |
Dirty data, bias and model limits.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 18% | +2.5 |
On-call, incidents and rollback.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 16% | +2.2 |
| Total | 100% | 29% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Train models, features and notebooks
- Evaluate, document and compare runs
- Deploy, monitor and retrain
- Talk to product about what a good error is
- Dirty data, bias and model limits
- On-call, incidents and rollback
- machine learning engineer
- ingeniero ml
- mlops
Sources for this job· O*NET 15-1221.00· ESCO/ISCO 2511