Supply, Distribution and Related Managers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 12 task statements that define Supply, Distribution and Related Managers (ISCO-08 1324) score an average of 0.39 on a 0–1 exposure scale — more exposed than about 74% 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 12 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Overseeing the recording of purchase, storage and distribution transactions;”

Scores 0.46 on the 2025 scale. The task of overseeing the recording of purchase, storage, and distribution transactions involves a mix of data entry, documentation, and logistical oversight. Generative AI can significantly assist with the automation of routine data handling, entry, and processing aspects, similar to maintaining records seen in tasks like "maintaining a register of contractors" which had an adjusted score of 0.69. However, this task potentially involves more complexity and oversight than simple data entry, akin to "preparing sales registers," which scored 0.65, due to the requirement for human intervention in reviewing complex transactions, ensuring compliance, and decision-making in physical logistics. Aspects of overseeing logistics resemble the "monitoring warehouse inventory" task with a score of 0.35, reflecting the need for human judgment where AI is limited. While AI can automate substantial portions of this task, the complexity of managing purchase and distribution records — involving both logistical and compliance components — prevents full automation. Considering the availability of technology in a high-income country like Poland, an adjusted score of 0.465 reflects both the high potential for data-related automation and the necessity of human oversight in logistical and transactional tasks.

Moving fastest, 2023 → 2025

“Overseeing the recording of purchase, storage and distribution transactions;”

Model capability on this task changed by +0.16 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 1324, 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.

• Transportation, Storage, and Distribution Managers
• Supply Chain Managers

In context

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

Supply, Distribution and Related Managers sit at the 74th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Supply, Distribution and Related Managers rank in the 74th 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 0.00 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Overseeing the recording of purchase, storage and distribution transactions;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Supply, Distribution and Related Managers". https://singulariki.com/gradient/1324-supply-distribution-and-related-managers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.