Automation index
How much of a Food technologist’s job can AI already do?
An industrial recipe comes earlier. Coming out safe and tasting like food do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Food technologist
22%
AI can already do 22% of this job.
#397 / 633
View rankingFood technologist
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 |
|---|---|---|---|
Recipe, process and shelf life.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Plant, the batch and the one that will not set.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
HACCP, labelling and not lying.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Taste, marketing and not disguising.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
The supplier, the ingredient and not swapping blind.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
Safe, and tasting like food.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 22% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- industrial recipes
- shelf life
- HACCP
- labelling
- taste
- plant batches
- ingredients
- food process
Sources for this job· O*NET 19-1012.00· ESCO/ISCO 2145
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