Automation index
How much of a Process engineer’s job can AI already do?
A diagram and a balance come earlier. Stopping the plant and not overloading the reactor do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Process engineer
26%
AI can already do 26% of this job.
#205 / 623
View rankingProcess 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 |
|---|---|---|---|
P&IDs, balances and the operating procedure.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 48% | +8.6 |
Plant, trials and the real bottleneck.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Capacity, yields and the improvement report.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Start-up, shutdown and the shift that skips the SOP.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Process safety and the HAZOP that hurts.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
Not overloading the reactor, and signing the change.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 26% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- P&IDs
- mass balances
- HAZOP
- plant start-up
- improve yields
- SOP
- bottlenecks
- process safety
Sources for this job· O*NET 17-2112.00· 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