Policy Administration Professionals

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 7 task statements that define Policy Administration Professionals (ISCO-08 2422) score an average of 0.42 on a 0–1 exposure scale — more exposed than about 79% 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 Minimal 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

“Researching social, economic and industrial trends, and client expectations of programmes and services provided;”

Scores 0.50 on the 2025 scale. The task of researching social, economic, and industrial trends, as well as understanding client expectations of programs and services, involves significant data analysis, pattern recognition, and the ability to comprehend complex, nuanced scenarios. Generative AI can significantly aid in gathering data, identifying trends, and even drafting preliminary reports. This similar situational context is evident in tasks like "Analyzing algorithms and data processing programs" (automation score of 0.77) and "Monitoring the online advertising market" (automation score of 0.685). These involve systematic data analysis abilities that AI excels at. However, the necessity for human interpretation, context analysis, and nuanced understanding limits full automation capabilities, similar to tasks like "Preparing social assistance plans and reports" (automation score of 0.46) and "Conducting ongoing monitoring of service implementation" (automation score of 0.45). Considering Poland's high digital literacy and access to tech resources, the task is highly automatable in terms of data synthesis but still requires human oversight for strategic context and insights, thus justifying an adjusted score of 0.55.

Moving fastest, 2023 → 2025

“Conducting threat and risk assessments and developing responses;”

Model capability on this task changed by +0.14 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 2422, 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.

• Business Operations Specialists, All Other
• Business Continuity Planners
• Sustainability Specialists
• Online Merchants
• Security Management Specialists

In context

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

Policy Administration Professionals sit at the 79th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Policy Administration Professionals rank in the 79th 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 0.03 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Researching social, economic and industrial trends, and client expectations of programmes and services provided;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Policy Administration Professionals". https://singulariki.com/gradient/2422-policy-administration-professionals.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.