Data Warehouse Architects

Computer & Mathematical

AI exposure

  • Data source: BLSPublished: 2026-08

    Very high· relative

    LowFour relative bandsVery high

    Group-level value

    Scale, basis and source

    Four relative bands (Low / Moderate / High / Very high)

    831 detailed occupations in the BLS Employment Projections table. Assigned per National Employment Matrix (NEM) code, so occupations sharing a NEM code carry the same band

    Source dataset (XLSX download)

  • Data source: AnthropicPublished: 2026-03

    0.579

    0.000Range of values carried here0.745
    Scale, basis and source

    Observed exposure index, 0–1 as published

    Mapped onto O*NET tasks

    Source dataset

  • Data source: ILOPublished: 2025

    0.55

    0.09Range of values carried here0.70

    Group-level value

    Scale, basis and source

    Generative AI exposure index, 0–1 as published

    ISCO-08 unit group — every occupation sharing the code gets this value

    Computed by this site, not published by the ILO: of the 1,012 occupations this site links to the ILO dataset, 12% score at or above this value.

    Source dataset

What kind of figure this source publishes

The BLS category is a relative rank, not an absolute level, and it is not a first-hand measurement: it groups an occupation's percentile ranks across several published studies into four bands. It is not an employment or wage forecast, not a probability of adoption, and it does not separate automation from augmentation.

Task-level exposure

Values in this tab are predicted labels, not observations. Eloundou et al. (2023) published two rating regimes — human raters and GPT-4 — and the β shown here is derived from the GPT-4 rater basis alone; the same task can take a different value under the other regime. The unit and the meaning differ from the observed shares (%) in the other tabs, so do not place them on the same axis.

TaskβE1 + 0.5 × E2
Test software systems or applications for software enhancements or new products.

O*NET Task ID 16116

1.0
Review designs, codes, test plans, or documentation to ensure quality.

O*NET Task ID 16117

1.0
Prepare functional or technical documentation for data warehouses.

O*NET Task ID 16119

1.0
Write new programs or modify existing programs to meet customer requirements, using current programming languages and technologies.

O*NET Task ID 16120

1.0
Select methods, techniques, or criteria for data warehousing evaluative procedures.

O*NET Task ID 16122

1.0
Perform system analysis, data analysis or programming, using a variety of computer languages and procedures.

O*NET Task ID 16123

1.0
Map data between source systems, data warehouses, and data marts.

O*NET Task ID 16124

1.0
Implement business rules via stored procedures, middleware, or other technologies.

O*NET Task ID 16125

1.0
Develop and implement data extraction procedures from other systems, such as administration, billing, or claims.

O*NET Task ID 16126

1.0
Develop or maintain standards, such as organization, structure, or nomenclature, for the design of data warehouse elements, such as data architectures, models, tools, and databases.

O*NET Task ID 16127

1.0

β = E1 + 0.5 × E2 · E1 = tasks where direct LLM access alone cuts time by at least 50%, E2 = tasks where software built on top of an LLM cuts time by at least 50%. Values take only 0 / 0.5 / 1.0.

Data sources & licenses — O*NET®, Eloundou et al. (2023): see full notices on the Credits page

Occupation information