Automation index
How much of a Growth manager’s job can AI already do?
An A/B test and a funnel report come earlier. Picking this week’s bet and fighting for budget do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Growth manager
31%
AI can already do 31% of this job.
#35 / 623
View rankingGrowth manager
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 |
|---|---|---|---|
Analytics, funnels and growth dashboards.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 52% | +11.4 |
Copy, creatives and test variants.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 48% | +8.6 |
Design experiments and read causality.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Align product, marketing and sales.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 15% | +2.4 |
Ads budget and internal negotiation.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 18% | +2.5 |
Decide what dies and what scales.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 16% | +2.2 |
| Total | 100% | 31% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Analytics, funnels and growth dashboards
- Copy, creatives and test variants
- Design experiments and read causality
- Align product, marketing and sales
- Ads budget and internal negotiation
- Decide what dies and what scales
- growth manager
- growth hacker
- responsable de growth
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