Automation index
How much of a Property manager’s job can AI already do?
Minutes and a levy come earlier. A meeting that does not end in shouting does not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Property manager
24%
AI can already do 24% of this job.
#255 / 623
View rankingProperty manager
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 |
|---|---|---|---|
Minutes, levies, accounts and contracts.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 48% | +8.6 |
Meetings, neighbours and porters.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
Vendors, works and incidents.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Rules, insurance and arrears.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Building visits and maintenance.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 16% | +2.6 |
Not leaving a building without heat in January.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
| Total | 100% | 24% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Minutes, levies, accounts and contracts
- Meetings, neighbours and porters
- Vendors, works and incidents
- Rules, insurance and arrears
- Building visits and maintenance
- administrador de fincas
- property manager
Sources for this job· O*NET 11-9141.00· ESCO/ISCO 3334
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