Automation index
How much of a E-learning trainer’s job can AI already do?
A SCORM course comes earlier. Someone learning without dropping at module three do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
E-learning trainer
27%
AI can already do 27% of this job.
#153 / 623
View rankingE-learning trainer
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 |
|---|---|---|---|
Script, SCORM and the platform.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 48% | +8.6 |
Tutoring, the forum and the one who vanishes.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Assess, copying and the one who does not log in.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Design, the video and not drowning in text.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The client, the deadline and not delivering a dressed-up PDF.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
That they learn, and not dropping out.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
- SCORM
- online tutoring
- forums
- platforms
- course script
- training videos
- assess online
- drop-out
Sources for this job· O*NET 25-9031.00· ESCO/ISCO 2351
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