Automation index
How much of a Printing press operator’s job can AI already do?
A preflight and a colour profile come earlier. Calibrating the press and not wasting the run do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Printing press operator
27%
AI can already do 27% of this job.
#157 / 623
View rankingPrinting press operator
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 |
|---|---|---|---|
Preflight, imposition and colour profiles.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 48% | +7.7 |
Calibrate, ink and start the press.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
Watch register, density and defects on the run.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 22% | +4 |
Job change, plates and wash-up.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Finishing, guillotine and delivery.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Stock, times and liability for the run.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
| Total | 100% | 27% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- run offset
- calibrate colour
- preflight files
- watch register
- ink the press
- avoid wasting the run
- imposition
- change plates
Sources for this job· O*NET 51-5112.00· ESCO/ISCO 7322
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