Automation index
How much of a Architect’s job can AI already do?
A model can already draft plans, renders and a first spec. Stamping, client fights and liability stay with the architect.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Architect
28%
AI can already do 28% of this job.
#81 / 623
View rankingArchitect
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 |
|---|---|---|---|
Draft plans, sections and a first BIM model from the brief.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 48% | +10.6 |
Produce renders, views and façade options.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 40% | +7.2 |
Write quantities, specs and the project report.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Meetings with client, developer and planning office.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Site direction and inspection visits.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
Stamp, register and own legal responsibility.EstimateNo public source provides a precise figure. This is our estimate. | 12% | 8% | +1 |
| Total | 100% | 28% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- building plans
- renders
- sign a project
- Produce renders, views and façade options
- Site direction and inspection visits
- arquitect
- architect
- proyecto de edificio
Sources for this job· O*NET 17-1011.00· ESCO/ISCO 2161
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