Buyers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 10 task statements that define Buyers (ISCO-08 3323) score an average of 0.39 on a 0–1 exposure scale — more exposed than about 76% 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 10 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Obtaining information about requirements and stock and developing specifications for quantity and quality to be purchased, costs, delivery dates and other contract conditions;”

Scores 0.51 on the 2025 scale. The task of obtaining information about requirements and stock, and developing specifications for quantity and quality to be purchased, involves analyzing needs and setting clear criteria for procurement, areas where Generative AI can significantly assist. Similar tasks, such as "Determining material and time standards as well as price calculation" and "Calculating and paying contributions to the Social Security Institution," have automation scores around 0.4775 to 0.675, indicating moderate potential for automation. Generative AI can help streamline data analysis, generate reports, and propose initial specifications based on predefined criteria and past data patterns. However, the task also requires human oversight for contextual understanding, adaptation to specific organizational needs, and strategic decision-making that AI cannot fully automate. Considering the task's alignment with other clerical and technical tasks where AI aids but doesn't replace human input, a score of 0.46 reflects a balance of automation potential and the need for human expertise, particularly in a technological environment such as Poland, where AI tools are accessible and widely used.

Moving fastest, 2023 → 2025

“Studying market reports, trade periodicals and sales promotion materials and visiting trade shows, showrooms, factories and product design events;”

Model capability on this task changed by +0.20 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 3323, 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.

• Buyers and Purchasing Agents, Farm Products
• Wholesale and Retail Buyers, Except Farm Products
• Purchasing Agents, Except Wholesale, Retail, and Farm Products

In context

Part of the 3 - Technicians and associate professionals major group. Return to the full gradient to see how the whole group sits.

Buyers sit at the 76th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Buyers rank in the 76th 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 0.08 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Obtaining information about requirements and stock and developing specifications for quantity and quality to be purchased, costs, delivery dates and other contract conditions;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Buyers". https://singulariki.com/gradient/3323-buyers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.