Traditional Chiefs and Heads of Villages

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 7 task statements that define Traditional Chiefs and Heads of Villages (ISCO-08 1113) score an average of 0.23 on a 0–1 exposure scale — more exposed than about 42% 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 7 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Informing the community or village about government rules and regulations.”

Scores 0.36 on the 2025 scale. The task of informing a community or village about government rules and regulations involves conveying regulatory information, which generative AI can significantly assist with by providing automated updates, summarizing complex information, and generating structured communication templates. However, the task also requires a level of human judgment and interaction to effectively address specific community needs, local context, and to handle nuanced inquiries. This is similar to the task of "notifying security staff and external services," which scored 0.51 and involves structured communication but still needs human oversight for context-sensitive interpretation. Likewise, "complying with the law in every aspect of work" scored 0.33, reflecting the need for human judgment in legal contexts that AI cannot fully replace. Therefore, aligning closer to these tasks, the adjusted score recognizes the role of AI in information dissemination while highlighting the pivotal human element in understanding and interpretation in a high-income country like Poland, where technology aids but does not replace human roles.

Moving fastest, 2023 → 2025

“Performing ceremonial duties in connection with births, marriages, deaths, harvests and other important occasions;”

Model capability on this task changed by +0.01 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 1113, 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.

• Chief Executives
• Chief Sustainability Officers
• Legislators

In context

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

Traditional Chiefs and Heads of Villages sit at the 42nd percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Traditional Chiefs and Heads of Villages rank in the 42nd 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 fell by 0.10 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Informing the community or village about government rules and regulations.". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Traditional Chiefs and Heads of Villages". https://singulariki.com/gradient/1113-traditional-chiefs-and-heads-of-villages.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.