Automation index
How much of a Office manager’s job can AI already do?
An office inventory comes earlier. The house working without running out of paper do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Office manager
27%
AI can already do 27% of this job.
#169 / 623
View rankingOffice 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 |
|---|---|---|---|
Vendors, contracts and the inventory.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 48% | +8.6 |
Facilities, the air-con and the one who is late.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Onboarding, keys and the desk.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Budget, the extra and not overspending.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Management, the surprise and not leaving the office dark.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
That it works, and not running out of paper.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 27% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- office inventory
- facilities
- onboarding
- vendor contracts
- keys
- desks
- office budget
- air-con
Sources for this job· O*NET 11-3012.00· ESCO/ISCO 1219
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