Survey and Market Research Interviewers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 5 task statements that define Survey and Market Research Interviewers (ISCO-08 4227) score an average of 0.55 on a 0–1 exposure scale — more exposed than about 92% 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

“Recording responses on paper or entering responses directly into a computer database through computer-assisted interviewing systems;”

Scores 0.69 on the 2025 scale. The task of recording responses on paper or entering responses directly into a computer database has a significant potential for automation, especially in a high-income country like Poland where technology infrastructure is robust. Considering semantically similar tasks, such as data entry and processing (score 0.7) and removing documentation from systems (score 0.675), this task involves structured and repetitive actions that AI can perform efficiently, such as transcribing handwritten responses or inputting data directly into digital formats. Similar to tasks like "Removing documentation" or "Entering information into computer applications," Generative AI can automate much of the routine data handling, leveraging capabilities like optical character recognition (OCR) and natural language processing to streamline these processes. However, human oversight is necessary for accuracy, especially if responses require clarification or involve complex classifications. Given these factors and comparing with similar tasks, an adjusted score of 0.63 reflects the significant but not complete automation potential, acknowledging the need for human involvement in nuances and exceptions.

Moving fastest, 2023 → 2025

“Providing feedback to survey sponsors concerning problems in obtaining valid data.”

Model capability on this task changed by −0.05 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 4227, 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.

• Interviewers, Except Eligibility and Loan

In context

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

Survey and Market Research Interviewers sit at the 92nd percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Survey and Market Research Interviewers rank in the 92nd 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 0.16 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Recording responses on paper or entering responses directly into a computer database through computer-assisted interviewing systems;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Survey and Market Research Interviewers". https://singulariki.com/gradient/4227-survey-and-market-research-interviewers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.