Legislators

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 8 task statements that define Legislators (ISCO-08 1111) score an average of 0.31 on a 0–1 exposure scale — more exposed than about 59% of the 427 placed occupations. Roughly 0% of its tasks fall somewhere on the exposed part of the gradient, and the typical task lands in the Not exposed 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 8 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Determining, formulating and directing policies of national, state, regional or local governments;”

Scores 0.36 on the 2025 scale. The task of determining, formulating, and directing government policies involves high-level strategic decision-making, understanding complex socioeconomic factors, and navigating political landscapes, which are skills that Generative AI cannot fully replicate. The task shares similarities with other high-level strategic roles such as initiating and developing policies for a government department and directing a university department, which received adjusted scores of 0.37 and 0.38, respectively. These tasks highlight the need for human expertise in strategic oversight and policy formulation, areas where AI can assist by providing data analysis, trend forecasting, and initial document drafting but cannot fully automate the decision-making process. The context of performing this task in a high-income country like Poland supports the use of advanced AI tools to streamline certain aspects of data handling and analysis. Considering these factors, the adjusted score reflects AI's supportive role while acknowledging the significant human involvement necessary for the nuanced judgment and strategic leadership required in government policy-making.

Moving fastest, 2023 → 2025

“Presiding over or participating in the proceedings of legislative bodies and administrative councils of national, state, regional or local governments or legislative assemblies;”

Model capability on this task changed by +0.24 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 1111, 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.

• Legislators

In context

Part of the 1 - Managers major group. Return to the full gradient to see how the whole group sits.

Legislators sit at the 59th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Legislators rank in the 59th 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 0% of this occupation's tasks fall into an exposed gradient band. (ILO / Gmyrek et al. (2025))
• Mean task exposure rose by 0.09 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Determining, formulating and directing policies of national, state, regional or local governments;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Legislators". https://singulariki.com/gradient/1111-legislators.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.