Automation index
How much of a SEO specialist’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?
SEO specialist
39%
AI can already do 39% of this job.
#9 / 623
View rankingSEO 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 |
|---|---|---|---|
Devise and evaluate methods and procedures for collecting data, such as surveys, opinion polls, or questionnaires, or arrange to obtain existing data.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 28% | +5 |
Conduct research on consumer opinions and marketing strategies, collaborating with marketing professionals, statisticians, pollsters, and other professionals.EstimateNo public source provides a precise figure. This is our estimate. | 17% | 48% | +8.2 |
Collect and analyze data on customer demographics, preferences, needs, and buying habits to identify potential markets and factors affecting product demand.EstimateNo public source provides a precise figure. This is our estimate. | 11% | 48% | +5.3 |
Monitor industry statistics and follow trends in trade literature.EstimateNo public source provides a precise figure. This is our estimate. | 11% | 28% | +3.1 |
| Prepare reports of findings, illustrating data graphically and translating complex findings into written text. | 10% | 72% | +7.2 |
Measure and assess customer and employee satisfaction.EstimateNo public source provides a precise figure. This is our estimate. | 7% | 28% | +2 |
Measure the effectiveness of marketing, advertising, and communications programs and strategies.EstimateNo public source provides a precise figure. This is our estimate. | 6% | 28% | +1.7 |
Gather data on competitors and analyze their prices, sales, and method of marketing and distribution.EstimateNo public source provides a precise figure. This is our estimate. | 6% | 48% | +2.9 |
Other less frequent tasks (weight estimated from frequency).EstimateNo public source provides a precise figure. This is our estimate. | 14% | 28% | +3.9 |
| Total | 100% | 39% |
Modelled workweek: 100% · Public evidence: 10% · Estimate: 90% · Confidence: Low
Skills and activities
- online ads campaign techniques
- web strategy assessment
- content marketing strategy
- e-commerce systems
- company policies
- perform online data analysis
- implement marketing strategies
- creatively use digital technologies
- manage budgets
- execute email marketing
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