Automation index
How much of a Interior designer’s job can AI already do?
A render and a moodboard come earlier. Measuring a real flat, and fighting a wall with the client, do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Interior designer
25%
AI can already do 25% of this job.
#242 / 623
View rankingInterior designer
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 |
|---|---|---|---|
Moodboards, renders and a first plan.EstimateNo public source provides a precise figure. This is our estimate. | 20% | 48% | +9.6 |
Budget, lines and orders.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 38% | +6.1 |
Measure, visit and read the real light.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 15% | +2.4 |
Works, trades and surprises.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
Client, changes of mind and close.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
Taste: what stays and what is noise.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 16% | +2.6 |
| Total | 100% | 25% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- design interfaces
- prototypes
- figma
- Moodboards, renders and a first plan
- Budget, lines and orders
- Measure, visit and read the real light
- Works, trades and surprises
- Client, changes of mind and close
- Taste: what stays and what is noise
- interiorista
Sources for this job· O*NET 27-1025.00· ESCO/ISCO 3432
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