Automation index
How much of a Printer technician’s job can AI already do?
A jam log comes earlier. Leaving the office printing without inking the floor do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Printer technician
23%
AI can already do 23% of this job.
#314 / 633
View rankingPrinter technician
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 |
|---|---|---|---|
Log, count and the maintenance kit.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Jam, fuser and toner on the floor.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
Network, queues and the wrong driver.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Contract, visits and the machine that is done.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
The office at peak and not stopping invoicing.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
That it prints, and not swapping the fuser blind.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 23% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- jams
- fuser
- maintenance kits
- print queues
- toner
- service contracts
- drivers
- copy counts
Sources for this job· O*NET 49-2011.00· ESCO/ISCO 7421
IS WORK OVER?
ML engineer
29%
#64 / 633
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