Inquiry Clerks

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 5 task statements that define Inquiry Clerks (ISCO-08 4225) score an average of 0.57 on a 0–1 exposure scale — more exposed than about 95% 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 5 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Responding to inquiries about problems and providing advice, information and assistance;”

Scores 0.67 on the 2025 scale. The task "Responding to inquiries about problems and providing advice, information and assistance" is highly automatable due to the repetitive and structured nature of responding to user inquiries, which aligns closely with the strengths of Generative AI. AI models like ChatGPT are proficient in processing language inputs and generating coherent responses, making them well-suited for tasks that involve providing factual information and advice. This task is similar to "responding to user inquiries" in the semantically related tasks, which received adjusted automation scores of 0.69, reflecting a high potential for AI automation in handling routine and repetitive inquiries. While human oversight may still be necessary for handling complex or nuanced queries, the capacity for AI to streamline the response process, especially in a high-income country with good access to technology like Poland, is substantial. Consequently, the adjusted score of 0.69 reflects AI's potential to perform this task efficiently while still acknowledging the need for human involvement in more complex scenarios.

Moving fastest, 2023 → 2025

“Issuing relevant forms, information kits and brochures to interested parties.”

Model capability on this task changed by −0.04 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 4225, 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.

• Receptionists and Information Clerks
• Correspondence Clerks

In context

Part of the 4 - Clerical support workers major group. Return to the full gradient to see how the whole group sits.

Inquiry Clerks sit at the 95th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Inquiry Clerks rank in the 95th 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 fell by 0.15 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Responding to inquiries about problems and providing advice, information and assistance;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Inquiry Clerks". https://singulariki.com/gradient/4225-inquiry-clerks.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.