Automation index
How much of a Packaging engineer’s job can AI already do?
A 3D prototype comes earlier. Surviving transport and opening on the shelf do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Packaging engineer
27%
AI can already do 27% of this job.
#161 / 623
View rankingPackaging 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 |
|---|---|---|---|
Spec, 3D and the drop test.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 48% | +8.6 |
The packing line and the one that will not fit.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Material, recyclability and unit cost.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 36% | +5.8 |
Supplier, die and pack colour.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Marketing, legal and not lying on the pack.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
That it survives the trip, and not oversizing the plastic.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
- design packs
- drop test
- packing line
- recyclability
- die-cut
- unit cost
- pack spec
- 3D prototype
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