Key Findings
The clearest patterns in the ranking.
Desk-based digital work dominates the top.
Computer Programmers lead at 45%, followed by Customer Service Representatives at 42% and Data Entry Keyers at 40%. These jobs revolve around structured digital tasks AI can already help perform.
Documentation-heavy work looks especially exposed.
Medical Records Specialists sit at 40%, while Medical Transcriptionists reach 38%. Both roles rely heavily on organizing, processing, and documenting information in formats AI handles well.
Communications and research jobs also rank high.
Market Research Analysts and Marketing Specialists show 39%, while Public Relations Specialists sit at 27%. These roles still require judgment, but they include writing, summarizing, and information synthesis that overlap with AI tools.
Healthcare splits into two distinct groups.
Medical Records Specialists rank at 40%, while Registered Nurses are near 4%. Administrative healthcare work appears far more exposed than hands-on clinical care.
The safest jobs tend to be physical and in-person.
Many of the lowest-ranked occupations sit at 0%, including Bartenders, Dishwashers, Electricians, and Firefighters. These roles depend on real-world movement, manual skill, and human presence that AI cannot directly replace.
Low implied odds do not mean no AI impact.
Even the safest jobs may still use AI tools in parts of the workflow. This ranking measures the implied odds of AI replacing the job overall, not whether AI can assist with smaller tasks inside it.
Top 10 Highest Odds
Jobs with the highest implied odds of AI replacing them.
Top 10 Lowest Odds
Jobs with the lowest implied odds of AI replacing them.
Full Ranking
All occupations ranked by implied odds.
| Rank | Job | Implied Odds | Exposure Score | Risk Level |
|---|
How We Built This Tool What comes from the source dataset, and what Action Network added. +
This tool starts with occupation-level AI exposure data, which estimates how much of each job overlaps with tasks AI can already perform or assist with.
Action Network then transformed that source data into this odds-based ranking.
- The source dataset provides the exposure score for each occupation.
- Action Network uses that score as the base signal of AI task overlap.
- Action Network converts it into a probability-style metric for easier comparison across jobs.
- Action Network ranks occupations from highest to lowest using that implied odds figure.
Sources The underlying data and modeling layer behind the page. +
Occupation-level AI exposure dataset.
O*NET occupation codes and titles used to structure the jobs across the dataset.
Implied odds conversion, rankings, editorial findings, and page design.