Automation index
How much of a Automation engineer’s job can AI already do?
A PLC and a SCADA come earlier. Leaving the plant in auto without it tripping do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Automation engineer
27%
AI can already do 27% of this job.
#159 / 623
View rankingAutomation 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 |
|---|---|---|---|
PLC, SCADA and the interlock.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 48% | +8.6 |
Panel, loops and the lying sensor.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
FAT, SAT and commissioning.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 36% | +5.8 |
Recipes, batches and the product change.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The shift, the alarm and not bypassing the interlock.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
That it does not trip, and documenting the program.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
- program PLCs
- SCADA
- interlocks
- control loops
- FAT SAT
- commissioning
- plant alarms
- automation panels
Sources for this job· O*NET 17-3024.00· ESCO/ISCO 2149
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