Automation index
How much of a Product manager’s job can AI already do?
Today an AI can already knock out copy, outlines, reports, and a lot of the prep. Deciding what matters, and what moves the business, is another story.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Product manager
31%
AI can already do 31% of this job.
#37 / 623
View rankingProduct 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 |
|---|---|---|---|
Formulate, direct, or coordinate marketing activities or policies to promote products or services, working with advertising or promotion managers.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 28% | +6.2 |
Identify, develop, or evaluate marketing strategy, based on knowledge of establishment objectives, market characteristics, and cost and markup factors.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Direct the hiring, training, or performance evaluations of marketing or sales staff and oversee their daily activities.EstimateNo public source provides a precise figure. This is our estimate. | 12% | 28% | +3.4 |
Evaluate the financial aspects of product development, such as budgets, expenditures, research and development appropriations, or return-on-investment and profit-loss projections.EstimateNo public source provides a precise figure. This is our estimate. | 7% | 48% | +3.4 |
Consult with product development personnel on product specifications, such as design, color, or packaging.EstimateNo public source provides a precise figure. This is our estimate. | 6% | 28% | +1.7 |
Compile lists describing product or service offerings.EstimateNo public source provides a precise figure. This is our estimate. | 6% | 28% | +1.7 |
Use sales forecasting or strategic planning to ensure the sale and profitability of products, lines, or services, analyzing business developments and monitoring market trends.EstimateNo public source provides a precise figure. This is our estimate. | 6% | 48% | +2.9 |
Develop pricing strategies, balancing firm objectives and customer satisfaction.EstimateNo public source provides a precise figure. This is our estimate. | 5% | 28% | +1.4 |
Other less frequent tasks (weight estimated from frequency).EstimateNo public source provides a precise figure. This is our estimate. | 20% | 28% | +5.6 |
| Total | 100% | 31% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- characteristics of products
- channel marketing
- advertising techniques
- content marketing strategy
- product comprehension
- company policies
- plan marketing strategy
- assign codes to product items
- perform data analysis
- maintain relationship with suppliers
Sources for this job· O*NET 11-2021.00· 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