Automation index
How much of a Veterinary technician’s job can AI already do?
A chart and stock come earlier. Holding, placing a line and not getting bitten do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Veterinary technician
18%
AI can already do 18% of this job.
#594 / 643
View rankingVeterinary technician
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 |
|---|---|---|---|
Charts, bookings and drug stock.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Hold, place lines and assist in theatre.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 10% | +2.2 |
Labs, X-rays and samples.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Talk to owners and explain discharge.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Cages, cleaning and hospitalisation.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 16% | +2.6 |
Bites, zoonoses and not leaving an animal.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 14% | +2 |
| Total | 100% | 18% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Charts, bookings and drug stock
- Hold, place lines and assist in theatre
- Labs, X-rays and samples
- Talk to owners and explain discharge
- Cages, cleaning and hospitalisation
- Bites, zoonoses and not leaving an animal
- tecnico veterinario
- auxiliar veterinario
- vet tech
Sources for this job· O*NET 29-2056.00· ESCO/ISCO 3240
IS WORK OVER?
ML engineer
29%
#64 / 643
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