Automation index
How much of a Pest control technician’s job can AI already do?
A protocol and a sheet come earlier. Treating without poisoning the people who live there do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Pest control technician
25%
AI can already do 25% of this job.
#254 / 623
View rankingPest control 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 |
|---|---|---|---|
Inspection, species and the treatment plan.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Bait, apply, seal and look again.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
Sheets, schedule and the certificate for the client.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Explain risks, times and what is not a miracle.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 22% | +4 |
Rules, biocides and waste.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
That the pest drops and nobody else pays.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
| Total | 100% | 25% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- pest control
- inspect infestations
- bait
- apply biocides
- seal entry points
- treatment certificate
- identify species
- not poison anyone
Sources for this job· O*NET 37-2021.00· ESCO/ISCO 7544
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