General Office Clerks

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 8 task statements that define General Office Clerks (ISCO-08 4110) score an average of 0.59 on a 0–1 exposure scale — more exposed than about 97% 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 4 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

“Transcribing information onto computers, and proofreading and correcting copy.”

Scores 0.71 on the 2025 scale. The task of transcribing information onto computers, and proofreading and correcting copy is highly aligned with the core capabilities of Generative AI technologies. These tasks involve data entry, text processing, and error correction—areas where Generative AI excels. Many semantically similar tasks, such as "Conducting successive corrections until the text is free from composition errors," received an adjusted automation score of 0.675, reflecting this automation potential. The contexts provided indicate that Generative AI can handle routine proofreading and error detection while suggesting corrections effectively without human intervention. Given the availability of advanced tools and internet access in a high-income country like Poland, the environment supports AI integration. While human oversight might still be necessary for complex contextual understanding and nuanced language errors, the overall potential for automation of this task remains high, justifying the score.

Moving fastest, 2023 → 2025

“Sorting, opening and sending mail;”

Model capability on this task changed by +0.19 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 4110, 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.

• Office Clerks, General
• Procurement Clerks

In context

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

General Office Clerks sit at the 97th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, General Office Clerks rank in the 97th 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 0.09 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Transcribing information onto computers, and proofreading and correcting copy.". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "General Office Clerks". https://singulariki.com/gradient/4110-general-office-clerks.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.