What the Index Measures
The Opportunity Data AI Exposure Index measures how much an occupation's core work tasks overlap with current AI capabilities. It balances one exposure factor against two protective factors:
- Digital work intensity increases exposure. Computer-based, data-heavy work is the frontier of AI automation.
- Human contact intensity decreases exposure. Empathy, direct care, and constant interpersonal interaction are difficult for AI to replicate.
- Physical task intensity decreases exposure. Hands-on skill, physical presence, and responsibility for others' safety currently require a human body on site. Advances in AI-powered robotics may erode this protection over longer horizons, but the dimension remains a strong differentiator for near-term workforce planning (5-10 years).
A score of 0 means fully protected. A score of 1 means fully exposed. The index does not predict job loss. It measures task-level overlap with AI capabilities. Whether exposed occupations are augmented, restructured, or displaced depends on adoption speed, regulation, and organizational decisions the index cannot capture.
The Nine O*NET Variables
All variables are drawn from O*NET 23.1 (U.S. Department of Labor). O*NET surveys incumbent workers and occupation analysts to produce standardized descriptors for 966 occupations. Scores are self-reported survey responses, not modeled or imputed.
| Variable |
O*NET Element |
Scale |
Dimension |
Direction |
| Interacting With Computers |
4.A.3.b.1 |
IM (1-5) |
Digital |
Higher = more exposed |
| Analyzing Data or Information |
4.A.2.a.4 |
IM (1-5) |
Digital |
Higher = more exposed |
| Processing Information |
4.A.2.a.2 |
IM (1-5) |
Digital |
Higher = more exposed |
| Assisting and Caring for Others |
4.A.4.a.5 |
IM (1-5) |
Contact |
Higher = more protected |
| Performing for or Working Directly with the Public |
4.A.4.a.8 |
IM (1-5) |
Contact |
Higher = more protected |
| Contact With Others |
4.C.1.a.2.l |
CX (1-5) |
Contact |
Higher = more protected |
| Spend Time Using Hands |
4.C.2.d.1.g |
CX (1-5) |
Physical |
Higher = more protected |
| Spend Time Sitting |
4.C.2.d.1.a |
CX (1-5) |
Physical |
Inverted: more sitting = less protected |
| Responsible for Others' Health and Safety |
4.C.1.c.1 |
CX (1-5) |
Physical |
Higher = more protected |
Why "Responsible for Others' Health and Safety" is in the Physical dimension: This variable captures the requirement for physical presence. If someone's safety depends on you being there (a nurse, a firefighter, a pilot, a lifeguard), the work cannot be performed remotely or delegated to software. It complements the hands-on and posture variables by adding a safety-stakes signal that pure manual-work measures miss.
Three Dimensions
Digital Work Intensity
How computer- and data-intensive is the core work? Average of three normalized variables: Interacting With Computers, Analyzing Data or Information, Processing Information.
Physical Task Intensity
Is the work grounded in physical presence, manual skill, or safety responsibility? Average of three normalized variables: Spend Time Using Hands, Spend Time Sitting (inverted), Responsible for Others' Health and Safety.
Each dimension ranges from 0 to 1 after normalization. Digital intensity drives exposure up. Human contact and physical task intensity drive it down.
Construction: From Raw Scores to Composite Index
1
Extract and normalize. For each of the 966 O*NET occupations, extract the raw score (1-5) for all 9 variables. Min-max normalize each variable to [0, 1] across all 966 occupations. Invert "Spend Time Sitting" so that higher values mean less sitting. Occupations missing any variable are excluded, leaving 966 scored occupations.
2
Compute dimension scores. Each dimension is the simple average of its constituent normalized variables.
Digital = mean( computers, analyzing, processing )
Contact = mean( caring, public, contact_with_others )
Physical = mean( hands, 1 − sitting, responsible )
3
Compute the composite. One exposure factor minus two protective factors, rescaled to [0, 1].
AI Exposure Index = ( Digital − Contact − Physical + 2 ) / 3
The +2 offset ensures the result stays in [0, 1]. All three dimensions receive equal weight (1:1:1).
| Tier | Score Range | Occupations | U.S. Workers |
|---|
| AI-protected |
Below 0.45 | 356 | 61.7 million |
| Moderate |
0.45 - 0.60 | 230 | 35.3 million |
| AI-exposed |
0.60 - 0.70 | 134 | 19.2 million |
| Highly exposed |
Above 0.70 | 52 | 6.4 million |
Occupation-Level Index
772 occupations covering 122.6 million workers
The occupation index scores each Standard Occupational Classification (SOC) code directly from O*NET variables and pairs it with national employment estimates from the BLS Occupational Employment and Wage Statistics (May 2024). Of the 966 scored O*NET occupations, 772 map to unique 6-digit BLS codes. The remaining 194 are O*NET-specific subdivisions (e.g., "Data Warehousing Specialists") that map to broader BLS categories.
Each occupation receives scores on all three dimensions plus the composite index, alongside its BLS employment count. This is the foundation layer: every program-level score traces back to these occupation scores.
Academic Program-Level Index
1,786 academic programs across the full federal CIP taxonomy
The program index maps each Classification of Instructional Programs (CIP) code to occupations via the NCES CIP2020-SOC2018 Crosswalk. For each program, the linked occupations' AI Exposure scores are averaged using BLS national employment as weights. Programs whose linked occupations all have zero BLS employment receive an unweighted average.
Employment weighting
Employment weighting ensures that a program's score reflects the occupations graduates actually enter, not just the full list of possible jobs. Larger occupations contribute proportionally more to the program score.
Example: CIP 51.3801 (Registered Nursing) links to SOC codes for Registered Nurses (3.2M employed), Nurse Practitioners (355K), Nurse Anesthetists (45K), and others. The program's score is the employment-weighted average of those occupations' individual scores, so the 3.2 million Registered Nurses dominate the program's composite.
Data Sources
| Source | Version | What We Use |
|---|
O*NET Database
U.S. Department of Labor |
23.1 |
9 work characteristic variables (5 Work Activities, 4 Work Context) for 966 occupations. Self-reported survey data from incumbent workers. |
BLS OES
Bureau of Labor Statistics |
May 2024 |
National employment estimates for 831 detailed occupations. Used as weights for program-level aggregation and displayed alongside occupation scores. |
NCES CIP-SOC Crosswalk
National Center for Education Statistics |
2020 CIP / 2018 SOC |
Maps academic programs (CIP codes) to occupations (SOC codes). Used to aggregate occupation-level scores to the program level. |
Limitations and Scope
What the index does
- Measures how much an occupation's core work tasks overlap with current AI capabilities, based on standardized federal survey data.
- Provides a consistent, reproducible score for every occupation in O*NET and every academic program in the federal CIP taxonomy.
- Identifies three distinct dimensions of AI exposure, allowing users to see why a score is high or low.
What it does not do
- Predict job loss. High exposure means task overlap with AI, not inevitable displacement. Many exposed occupations will be augmented rather than replaced.
- Account for adoption speed. Regulatory, organizational, and economic factors determine how quickly AI is deployed in a given field. The index measures technical susceptibility only.
- Reflect individual variation. A single score per SOC code. A software engineer at a startup and one at a defense contractor have different work contexts but the same score.
- Track AI progress in real time. O*NET 23.1 reflects survey conditions roughly 2014-2022. AI capabilities are evolving faster than survey update cycles.
Known data limitations
- Self-reported survey data. O*NET scores reflect incumbent worker perceptions, not objective measurement of task content.
- Employment is a point estimate. BLS OES May 2024 is a single cross-section. Occupations growing or declining rapidly may be over- or under-represented in the weighting.
- O*NET-to-BLS mismatch. 194 O*NET occupations lack BLS employment data because O*NET uses finer subdivisions. These occupations are scored but show zero employment.
- Crosswalk is many-to-many. A single CIP code can link to dozens of SOC codes, and vice versa. The employment-weighted average is a reasonable summary but masks within-program variation.
- Equal dimension weighting. The 1:1:1 weighting across Digital, Contact, and Physical is a modeling choice, not an empirical finding. Different weights would produce different rankings.
Reproducibility
All source data, construction scripts, and intermediate outputs are available in the GitHub repository.
| File | Description |
|---|
onet_raw/db_23_1_text/Work Activities.txt | Source: O*NET Work Activities (5 variables) |
onet_raw/db_23_1_text/Work Context.txt | Source: O*NET Work Context (4 variables) |
national_M2024_dl.xlsx | Source: BLS OES May 2024 national employment |
CIP2020_SOC2018_Crosswalk.xlsx | Source: NCES CIP-SOC crosswalk |
dii_v2_by_soc.csv | Intermediate: 966 occupations with raw + normalized scores |
soc_dii_v2_employment.csv | Output: 772 occupations with dimensions, composite, employment |
all_cip_dii_v2.csv | Output: 1,786 programs with dimensions and composite |
dii_v2_variables.csv | Reference: 9-row variable table with element IDs and dimensions |