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.
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).
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.
Update Schedule and Versioning
The index is rebuilt on a fixed cycle keyed to the release calendars of its three federal inputs. Each rebuild updates the Data Sources table above and the vintage notes on every index page. Data refreshes and methodology changes are versioned separately, as described below.
The state portfolio analysis additionally uses NCES IPEDS completions, which are released annually; state scores are updated when a new award year becomes final. BLS Employment Projections, released each year in late August, are not an input to the index: projections estimate expected employment growth, while the index measures task overlap with current AI capability. The two are designed to be read side by side, not substituted for each other.
Data refreshes versus index versions
A data refresh recomputes all scores on newer vintages of the same nine variables, using the same construction. Refreshes keep the methodology version (currently v2) and update only the vintage notes. A new index version is published when the construction itself changes: variables added or removed, dimension weights revised, or the aggregation rule altered. If O*NET or another federal source begins publishing occupational measures built specifically around AI exposure, those measures would enter through a new version released alongside the current one, with a change note documenting what moved and why. Prior versions and prior vintages are archived and available on request, so any published figure can be traced to the exact release it was computed from.
Status as of September 2026: the current index is built on O*NET 23.1, which uses the 2010 occupation taxonomy. The newest upstream releases are O*NET 31.0 (August 2026) and OEWS May 2025. Every O*NET release since 25.1 (November 2020) uses the 2019 taxonomy, which merged, split, and renumbered occupations (the two 2010 software developer codes, for example, became one Software Developers code). The next rebuild therefore crosses a taxonomy boundary, so it will be published as a new index version with a documented bridge, per the policy above, not as a plain data refresh.
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 was published in November 2018, with descriptor ratings collected on a rolling cycle over the preceding decade. AI capabilities have changed substantially since those surveys were fielded, and are evolving faster than any survey update cycle.
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.
Opportunity Data · AI Exposure Index · Methodology
Released April 2026 · Updated September 2026