Policy and Planning Managers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 9 task statements that define Policy and Planning Managers (ISCO-08 1213) score an average of 0.36 on a 0–1 exposure scale — more exposed than about 66% 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 9 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Developing, directing, administering and participating in policy research and analysis;”

Scores 0.41 on the 2025 scale. The task "Developing, directing, administering, and participating in policy research and analysis" involves a variety of complex cognitive activities, including strategic thinking, deep subject matter expertise, and interpersonal communication, all requiring significant human judgment and decision-making. Although Generative AI can assist in data analysis, pattern recognition, drafting preliminary reports, and suggesting policy frameworks, it cannot replicate the nuanced understanding and strategic foresight necessary for effective policy development. Semantically similar tasks in the provided context, like "Initiating and developing policies in relation to the managed government administration department" and "Managing the quality of education and scientific research," reflect moderate automation potential, scoring 0.37 and 0.36, respectively, due to their reliance on human expertise and judgment. Considering the technological landscape in a high-income country like Poland, where AI can augment analytical and drafting tasks but not fully automate the higher-order strategic elements, I predict an adjusted score of 0.375. This score captures AI's supportive role while highlighting the continued necessity of human oversight and strategic insight in policy-related contexts.

Moving fastest, 2023 → 2025

“Developing, implementing and monitoring strategic plans, programmes, policies, processes, systems and procedures to achieve goals, objectives and work standards;”

Model capability on this task changed by +0.26 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 1213, 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.

• Managers, All Other
• Regulatory Affairs Managers
• Compliance Managers
• Loss Prevention Managers
• Wind Energy Operations Managers
• Wind Energy Development Managers
• Brownfield Redevelopment Specialists and Site Managers

In context

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

Policy and Planning Managers sit at the 66th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Policy and Planning Managers rank in the 66th 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 0.14 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Developing, directing, administering and participating in policy research and analysis;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Policy and Planning Managers". https://singulariki.com/gradient/1213-policy-and-planning-managers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.