Automation index
How much of a CCTV technician’s job can AI already do?
A camera plan comes earlier. Seeing the face, not the wall opposite, do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
CCTV technician
23%
AI can already do 23% of this job.
#316 / 633
View rankingCCTV 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 |
|---|---|---|---|
Plan, FOV and the recorder.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Fit, focus and the backlight that blinds.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
Network, PoE and the disk that fills.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Data-protection law, signs and the client who wants the whole street.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Residents, the porter and the illegal angle.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
See the face, and not recording extra.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 23% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- focus cameras
- FOV
- PoE
- recorders
- data-protection signs
- lawful angles
- recording disks
- camera plan
Sources for this job· O*NET 49-2098.00· ESCO/ISCO 7412
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