Trade Brokers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 6 task statements that define Trade Brokers (ISCO-08 3324) score an average of 0.44 on a 0–1 exposure scale — more exposed than about 82% 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 6 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Monitoring and analysing market trends and other factors affecting the supply and demand for commodities and shipping services.”

Scores 0.61 on the 2025 scale. The task of monitoring and analyzing market trends and other factors affecting the supply and demand for commodities and shipping services involves substantial data analysis, pattern recognition, and reporting—all tasks where Generative AI tools excel. Similar tasks, such as "Analyzing the efficiency of forwarding services" (score 0.655) and "Collecting information about goods on global exchanges" (score 0.645), demonstrate AI's capability to handle large datasets and identify trends with relative ease. However, this task also necessitates human oversight to interpret nuanced market shifts, regulatory compliance, and client-specific analysis. Tasks related to logistics and market analysis, such as "Planning domestic and international transportation" and "Analyzing market demand for services," also highlight the need for human judgment in complex decision-making and strategic forecasting. Given the AI's ability to significantly support data-driven components of this task in a high-income country like Poland, yet recognizing the need for human expertise in strategic evaluations and responses to market dynamics, a score of 0.55 reflects a balanced automation potential.

Moving fastest, 2023 → 2025

“Monitoring and analysing market trends and other factors affecting the supply and demand for commodities and shipping services.”

Model capability on this task changed by +0.02 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 3324, 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.

• Securities, Commodities, and Financial Services Sales Agents
• First-Line Supervisors of Non-Retail Sales Workers
• Cargo and Freight Agents
• Freight Forwarders

In context

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

Trade Brokers sit at the 82nd percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Trade Brokers rank in the 82nd 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 fell by 0.12 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Monitoring and analysing market trends and other factors affecting the supply and demand for commodities and shipping services.". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Trade Brokers". https://singulariki.com/gradient/3324-trade-brokers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.