Retail and Wholesale Trade Managers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 7 task statements that define Retail and Wholesale Trade Managers (ISCO-08 1420) score an average of 0.44 on a 0–1 exposure scale — more exposed than about 81% 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 2 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 7 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Promoting and advertising the establishment‚Äôs goods and services;”

Scores 0.64 on the 2025 scale. The task of promoting and advertising an establishment's goods and services involves a mix of content creation, customer interaction, and strategic decision-making. Generative AI can significantly enhance automation by generating marketing content, analyzing customer data to personalize promotions, and automating routine outreach and management tasks. This is similar to tasks like "Promoting the company, its products, and services" and "Conducting promotions and marketing of postal and financial services," which scored 0.635 and 0.62 respectively. These tasks involve promotional activities, highlighting AI's capability to automate the generation and dissemination of marketing materials. While AI handles routine informational and content generation aspects efficiently, human oversight remains essential for precise strategic branding, understanding market nuances, and building customer relationships. Given these factors, and assuming execution in a technologically advanced setting like Poland, an adjusted automation score of 0.62 reflects the considerable potential for automation without discounting the crucial human element in nuanced and interpersonal aspects of promotion.

Moving fastest, 2023 → 2025

“Controlling selection, training and supervision of staff;”

Model capability on this task changed by +0.18 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 1420, 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.

• General and Operations Managers

In context

Part of the 1 - Managers major group. Return to the full gradient to see how the whole group sits.

Retail and Wholesale Trade Managers sit at the 81st percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Retail and Wholesale Trade Managers rank in the 81st 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 rose by 0.00 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Promoting and advertising the establishment‚Äôs goods and services;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Retail and Wholesale Trade Managers". https://singulariki.com/gradient/1420-retail-and-wholesale-trade-managers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.