Ranking of Industries with the Highest AI Impact
AI does not affect every industry the same way, or even every job within an industry the same way. What matters is how much of a sector's work is repeatable information handling, versus judgment, physical presence, and accountability that a model cannot take on.
Industries built on standardized documents, structured data, and routine communication, such as finance back offices, administrative support, and parts of media and marketing, tend to score higher, because those tasks are what current AI does well. Sectors anchored in physical work, safety-critical decisions, and hands-on care, such as construction, healthcare, energy, and transportation, hold up better even as AI speeds up their paperwork.
The ranking below shows each industry's average AI risk across the jobs we track. Read it as a starting point, not a verdict: within any industry, roles heavy on routine processing sit far above roles built on judgment and responsibility. Open an industry to see which specific jobs drive its score.
A higher average AI Risk score means the typical work in that industry is more exposed to automation; a lower score means more of its work still depends on people. The score reflects how exposed the tasks are, not a prediction that the jobs disappear.
Industry Ranking
| Rank | Industry | Industry Average Risk Score | Jobs Analyzed |
|---|---|---|---|
| 1 | Operations | 91.67 | 3 |
| 2 | Marketing | 68.2 | 15 |
| 3 | Media | 65.5 | 6 |
| 4 | Retail | 63 | 2 |
| 5 | Finance | 60.8 | 15 |
| 6 | Logistics | 60.67 | 6 |
| 7 | Technology | 58.65 | 23 |
| 8 | Creative | 57.44 | 9 |
| 9 | Consulting | 54.18 | 11 |
| 10 | Entertainment | 49.17 | 6 |
| 11 | Transportation | 44.3 | 10 |
| 12 | Legal | 43.63 | 8 |
| 13 | Agriculture | 42.25 | 4 |
| 14 | Manufacturing | 42.13 | 8 |
| 15 | Environment | 40.5 | 4 |
| 16 | Real Estate | 39.33 | 3 |
| 17 | Energy | 37 | 3 |
| 18 | Hospitality | 36.69 | 13 |
| 19 | Construction | 34.33 | 12 |
| 20 | Education | 33 | 12 |
| 21 | Science | 33 | 9 |
| 22 | Healthcare | 26.33 | 15 |
| 23 | Public Service | 12 | 3 |
| 24 | Government | 11.75 | 4 |
Scores run from 0 to 100 and compare occupations with each other. A score is not a probability that the job disappears. Methodology
The Range Inside Each Sector
An average hides the thing most readers came for. The two columns below show, for every sector we track, the occupation currently most exposed to automation and the one least exposed — the same industry, at opposite ends of the same week's scoring.
Frequently asked questions
Q.Which industries are most exposed to AI?
The sectors at the top of this ranking are the ones whose typical work is standardised information handling: back-office finance, administrative support, and parts of media and marketing. They are not exposed because of the industry label but because of the task profile that dominates inside them — predictable inputs, checkable outputs, and enough volume to make automation worth building.
Q.Does a high industry score mean every job in it is at risk?
No, and the spread table above is there to show why. Every sector we track contains both routine processing roles and roles built on judgement, physical presence or accountability, and the distance between them is usually larger than the distance between one industry and the next. Read the sector figure as context, then read the occupation.
Q.My field is not listed as one of these industries. Where should I look?
The sector list is a grouping of the occupations we track, not a full industry taxonomy, so specialised fields sit inside a broader sector rather than getting their own row. The reliable route is to look up the occupation itself: every one has its own page with the weekly history and the reasoning behind its position.
Q.How is an industry's figure calculated?
It is the average across every occupation we track in that sector, recomputed each week from the same framework used for individual occupations. It is deliberately unweighted — a sector is not adjusted for how many people actually work in each of its roles — so it describes the mix of work we track, not the shape of the labour market. The methodology page sets out the dimensions and the known limits.