Automation index
How much of a Paid media specialist’s job can AI already do?
A media plan comes earlier. The pixel firing without burning the brand do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Paid media specialist
28%
AI can already do 28% of this job.
#123 / 623
View rankingPaid media specialist
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 |
|---|---|---|---|
Plan, platforms and the pixel.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 52% | +9.4 |
Creatives, frequency and audience cuts.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
The client, going live and the campaign that drifts.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Reports, attribution and the last-click fight.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Brand safety and inventory you cannot touch.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
That the pixel fires, and not burning extra CPM.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 28% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- media plan
- pixel
- frequency
- brand safety
- attribution
- creatives
- audiences
- digital inventory
Sources for this job· O*NET 13-1161.00· ESCO/ISCO 2431
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