Economists

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 12 task statements that define Economists (ISCO-08 2631) score an average of 0.55 on a 0–1 exposure scale — more exposed than about 93% of the 427 placed occupations. Roughly 100% of its tasks fall somewhere on the exposed part of the gradient, and the typical task lands in the Gradient 3 band.

Exposure is task overlap, not a verdict. A high score means a generative-AI model can do part of the content of these tasks — it says nothing about whether the work is automated, whether anyone uses AI for it today, or whether jobs are lost. The gradient is scored on the international ISCO-08 system; the rest of Singulariki is U.S. O*NET/SOC, bridged below by an approximate, many-to-many crosswalk.

How its tasks split across the gradient

Each of the 12 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Analysing factors that determine labour force participation, employment, wages, unemployment and other labour market outcomes;”

Scores 0.65 on the 2025 scale. The task of analyzing factors that determine labor force participation, employment, wages, unemployment, and other labor market outcomes involves extensive data analysis and interpretation. This is similar to tasks such as “Preparing and creating analyses of basic economic and production indicators” (score: 0.61) and "Analyzing the market of similar services and forecasting the demand for services" (score: 0.405), both of which require data processing and trend identification, areas where Generative AI excels. Given that Generative AI can significantly aid in the processing and initial interpretation of large datasets to identify patterns relevant to labor market outcomes, there is a high potential for automation in the data-driven aspects of this task. However, human expertise is crucial for contextual understanding, interpreting complex economic concepts, and making nuanced decisions based on incomplete data, as seen in tasks requiring qualitative analysis. The high level of access to technology in Poland further supports automation potential, allowing AI tools to be efficiently integrated into analytical tasks. Thus, considering AI's capabilities in data analysis and the need for human insight for comprehensive interpretation, an adjusted score of 0.62 is appropriate for this task.

Moving fastest, 2023 → 2025

“Examining problems related to the economic activities of individual companies;”

Model capability on this task changed by +0.20 in two years — the gradient is not static, it is filling in.

U.S. occupations this maps to

The American O*NET/SOC roles that crosswalk to ISCO-08 2631, biggest by employment first, via the published (approximate, many-to-many) IBS O*NET-SOC ↔ ISCO-08 correspondence. These are the closest U.S. matches — not an asserted one-to-one identity.

• Economists
• Environmental Economists

In context

Part of the 2 - Professionals major group. Return to the full gradient to see how the whole group sits.

Economists sit at the 93rd percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Economists rank in the 93rd percentile for GenAI task exposure — overlap with what generative AI can attempt, not a projection of displacement. (ILO / Gmyrek et al. (2025) GenAI exposure gradient)
• About 100% of this occupation's tasks fall into an exposed gradient band. (ILO / Gmyrek et al. (2025))
• Mean task exposure rose by 0.02 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Analysing factors that determine labour force participation, employment, wages, unemployment and other labour market outcomes;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Economists". https://singulariki.com/gradient/2631-economists.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.