Automation index
How much of a Motion designer’s job can AI already do?
A template and an easing come earlier. Motion that tells, and does not nauseate, do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Motion designer
25%
AI can already do 25% of this job.
#247 / 623
View rankingMotion 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 |
|---|---|---|---|
Storyboard, easing and the brand template.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 44% | +7 |
After Effects, the render and the client who changes the end.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 16% | +2.9 |
Rhythm, type and cutting to tempo.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 36% | +5.8 |
Delivery, formats and the one that is too heavy.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Art direction and not recycling the same whoosh.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
That it tells, and not nauseating with extra easing.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 25% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- motion graphics
- easing
- motion storyboard
- type in motion
- renders
- cut to tempo
- brand templates
- delivery formats
Sources for this job· O*NET 27-1014.00· ESCO/ISCO 2166
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