Automation index
How much of a Fishmonger’s job can AI already do?
A market order comes earlier. Picking the fish so it smells of sea, not fridge, do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Fishmonger
22%
AI can already do 22% of this job.
#363 / 633
View rankingFishmonger
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 |
|---|---|---|---|
Order, market and what arrives fresh.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Clean, portion and the cut the customer wants.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 12% | +2.2 |
Counter, advice and the one who cannot cook it.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Cold, ice and the piece that turns.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Christmas, the order and not running out of hake.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
Smell of sea, and not selling yesterday as today.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
- clean fish
- portion
- the market
- fish counter
- ice
- Christmas orders
- cut hake
- pick the fish
Sources for this job· O*NET 51-3022.00· ESCO/ISCO 6221
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