Stock Clerks

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 5 task statements that define Stock Clerks (ISCO-08 4321) score an average of 0.37 on a 0–1 exposure scale — more exposed than about 69% 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 5 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Maintaining stock records, verifying issue of goods, estimating needs and making requisitions of new stocks;”

Scores 0.43 on the 2025 scale. The task involves maintaining stock records, verifying the issue of goods, estimating needs, and making requisitions of new stocks, which combines both physical and cognitive activities. This positions the task similarly to 'Managing inventory and maintaining it at an optimal level,' which scored 0.365 due to its reliance on both human decision-making and potential AI assistance for data handling. The task also shows parallels to 'Monitoring warehouse inventory' (score 0.35), highlighting the partial automation potential for the data-centric aspects of inventory management. While AI can assist significantly in documenting stock levels, generating reports, optimizing inventory processes, and even predicting stock needs based on historical data, tasks involving nuanced judgment, on-the-ground verification, and handling exceptions still require human oversight. Given that the task is situated in a high-income country with widespread technological infrastructure, the likelihood of AI contributing significantly to the automation of its data processing, while acknowledging the physical logistics involved, suggests an adjusted score slightly higher than comparable inventory-centric roles. Therefore, the adjusted score of 0.415 reflects its moderate level of automation potential, balancing AI support in data-driven aspects with the required human intervention in physical tasks and decision-making processes.

Moving fastest, 2023 → 2025

“Weighing goods received or produced, or for issue or dispatch and maintaining relevant records;”

Model capability on this task changed by +0.17 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 4321, 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.

• Shipping, Receiving, and Inventory Clerks
• Weighers, Measurers, Checkers, and Samplers, Recordkeeping

In context

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

Stock Clerks sit at the 69th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Stock Clerks rank in the 69th 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 rose by 0.02 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Maintaining stock records, verifying issue of goods, estimating needs and making requisitions of new stocks;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Stock Clerks". https://singulariki.com/gradient/4321-stock-clerks.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.