Data Entry Clerks

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 5 task statements that define Data Entry Clerks (ISCO-08 4132) score an average of 0.70 on a 0–1 exposure scale — more exposed than about 100% 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 5 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Entering numerical data, codes and text from source material into computer-compatible storage and processing devices;”

Scores 0.74 on the 2025 scale. The task of entering numerical data, codes, and text from source material into computer-compatible storage and processing devices is highly automatable given the capabilities of Generative AI to manage structured data tasks efficiently. Similar tasks, like "Entering and processing information in textual, numerical, etc. collections" and "Coding information in accordance with the designed data processing program," received high adjusted scores of 0.7 and 0.78, indicating significant automation potential. Additionally, other tasks involving data accuracy and structured input, such as "Operating computerized passport record systems" and "Analyzing and verifying entered information and data," were scored at 0.715 and 0.705, respectively. These tasks demonstrate GenAI's capacity to handle repetitive and standardized data entry procedures with minimal human intervention. Given that these activities are taking place in Poland, where digital infrastructure supports high degrees of automation, a score of 0.725 reflects AI's capacity to significantly automate the data entry task while acknowledging the need for human oversight in ensuring data integrity and handling exceptions.

Moving fastest, 2023 → 2025

“Receiving and registering invoices, forms, records and other documents for data capture;”

Model capability on this task changed by +0.12 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 4132, 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.

• Data Entry Keyers

In context

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

Data Entry Clerks sit at the 100th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Data Entry Clerks rank in the 100th 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))
• Its most-exposed task: "Entering numerical data, codes and text from source material into computer-compatible storage and processing devices;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Data Entry Clerks". https://singulariki.com/gradient/4132-data-entry-clerks.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.