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?
Prompt engineer
34%
#21 / 623
View rankingPrompt 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 |
|---|---|---|---|
Design prompts, tools and chains.EstimateNo public source provides a precise figure. This is our estimate. | 24% | 58% | +13.9 |
Evals, datasets and regressions.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 52% | +9.4 |
Integrate with product and safety limits.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Talk to the business about a good output.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Watch hallucinations, bias and cost.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 20% | +2.8 |
Decide what the model gets, and what it does not.EstimateNo public source provides a precise figure. This is our estimate. | 12% | 16% | +1.9 |
| Total | 100% | 34% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Design prompts, tools and chains
- Evals, datasets and regressions
- Integrate with product and safety limits
- Talk to the business about a good output
- Watch hallucinations, bias and cost
- prompt engineer
- ingeniero de prompts
- llm engineer
Sources for this job· O*NET 15-1252.00· ESCO/ISCO 2512