Product Graders and Testers (except Foods and Beverages)

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 9 task statements that define Product Graders and Testers (except Foods and Beverages) (ISCO-08 7543) score an average of 0.31 on a 0–1 exposure scale — more exposed than about 58% 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 9 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Analysing test data and making computations as necessary to determine test results.”

Scores 0.56 on the 2025 scale. The task of analyzing test data and making computations to determine test results involves a combination of data analysis, pattern recognition, and potentially interpretative skills where Generative AI can assist significantly. This aligns with tasks such as "Performing load tests," which had an adjusted score of 0.325, where AI aids in data analysis but requires human attention for setting parameters and making nuanced judgments. Similarly, "Analyzing and interpreting research results" scored 0.425, reflecting the need for human expertise in interpreting complex data. Given that this task is performed in a high-income country like Poland, with widespread digital infrastructure, the involvement of AI in processing and initial analysis is notably feasible. However, human oversight is essential to ensure accurate contextual interpretation of the data, limiting full automation. Therefore, an adjusted score of 0.365 reasonably accounts for the substantial support AI can provide in automating data analysis and computation, while acknowledging the crucial role of human judgment and expertise in final decision-making.

Moving fastest, 2023 → 2025

“Recording inspection or test data, such as weights, temperatures, grades or moisture content, and quantities inspected or graded;”

Model capability on this task changed by +0.07 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 7543, 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.

• Inspectors, Testers, Sorters, Samplers, and Weighers

In context

Part of the 7 - Craft and related trades workers major group. Return to the full gradient to see how the whole group sits.

Product Graders and Testers (except Foods and Beverages) sit at the 58th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Product Graders and Testers (except Foods and Beverages) rank in the 58th 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 0.09 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Analysing test data and making computations as necessary to determine test results.". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Product Graders and Testers (except Foods and Beverages)". https://singulariki.com/gradient/7543-product-graders-and-testers-except-foods-and-beverages.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.