[This infographic article was produced with the assistance of Claude, Gemini Nano Banana, and Perplexity, supervised by Jia Hui Tee.]

Generative AI (GenAI) will most strongly reshape jobs in cross-border trade where people create, process, translate, and communicate information, rather than where they physically inspect, handle, or move goods.

Similarly, GenAI’s impact on cross-border trade-related occupations is likely to be concentrated in export-enabling tasks, rather than in the physical movement of goods itself.

Drawing on a large-scale, real-world usage-based “AI applicability score” for occupations developed by Microsoft researchers, we adapted the study’s ranking to identify 10 trade-related roles where productivity gains are most plausible based on observed task-level fit.

We found that sales representatives of services and customer service representatives are among the most exposed occupations, as the nature of their work involves structured communication and information-intensive tasks that AI excels at. GenAI can perform many of the subtasks of sales representatives, from drafting client-specific pitch decks and emails to translating and localizing marketing materials for different markets. AI chatbots can also handle a large volume of customer queries, assisting with customer complaints and problem-solving.

Computer numerically controlled (CNC) tool programmers, despite working with physical machinery, are also exposed to AI tools that can read design files, write code, and optimize parameters for standardized production processes. The World Trade Organization’s World Trade Report 2025 similarly observed that AI tools have been increasingly adopted to support trading activities such as automating customs clearance, reducing language barriers, strengthening market intelligence, and helping firms navigate complex regulatory frameworks.

However, exposure to AI varies by occupation and task composition. Many trade-related roles combine digital and physical elements, and not all AI interaction is equal or effective in completing a user’s task. Some tasks exhibit high AI complementarity, where automation enhances worker productivity and decision-making; others show low complementarity, where AI may substitute human input. Applying this perspective to the top 10 trade-related occupations can provide a more nuanced understanding of how GenAI can transform such roles, distinguishing between roles that can be empowered by AI and those that may face disruption.

What “exposed to GenAI” means

The Microsoft study measured “AI applicability” by mapping 200,000 anonymized Microsoft Copilot conversations against O*NET Resource Center data, then aggregating those activities into an occupation-level score.

Higher scores indicate a greater role for GenAI in supporting, performing, and/or completing the job’s tasks.¹

The study focused on four indicators:

Coverage: How frequently tasks associated with a job appear in Copilot conversations
Completion: The share of conversations where an LLM-based evaluator judged that Copilot completed the user’s task successfully
Scope: The share of conversations where Copilot demonstrated at least “moderate” capability in task performance measured via a six-point ordinal scale (none, minimal, limited, moderate, significant, and complete)
Overall AI applicability score: A measure of how suitable AI is for supporting or performing tasks in a given role. A high applicability score shows that a large share of the tasks within a job role can be assisted or successfully completed by GenAI.

This study produced a ranked list of 40 occupations with the highest AI applicability scores.

What “cross-border trade-related jobs” mean

We adapted the study’s ranking to match a task-based definition of “cross-border trade-related jobs”. Jobs count as trade-related when their day-to-day tasks directly contribute to producing, selling, delivering, or coordinating goods and services for non-resident customers, or enabling those cross-border transactions. Examples include market development, customer handling, information processing, and compliance-facing commercial administration.²

In practical terms, this definition broadly covers:³

Export-enabling services
These occupations perform tasks that help firms reach foreign markets and execute cross-border sales and delivery, including sales, marketing and market intelligence, customer-facing support, and business/professional services.
Trade-embodied production tasks
These occupations (including farmers, craft workers, and machine operators) carry out production and assembly tasks that physically create goods for export.

Based on this definition, we identified 10 trade-related occupations as a subset of Microsoft Research’s list of 40 as being most exposed to GenAI.

Figure 1: Top 10 trade-related occupations most exposed to GenAI

Rank	Job title	Coverage	Completion	Scope	AI applicability score	Trade-related tasks
1	Sales Representatives of Services	0.842	0.902	0.573	0.449	Export-enabling services: sales worker
2	Computer Numerically Controlled Tool Programmers	0.899	0.869	0.528	0.419	Trade-embodied production: skilled craft workers programing precision manufacturing equipment
3	Customer Service Representatives	0.72	0.901	0.591	0.408	Export-enabling services: clerical/customer-interface tasks
4	Telemarketers	0.656	0.893	0.603	0.404	Export-enabling services: outbound sales/lead generation
5	Data Scientists	0.765	0.863	0.506	0.357	Export-enabling services: professional analytics
6	Web Developers	0.733	0.858	0.506	0.353	Export-enabling services: digitally deliverable services
7	Advertising Sales Agents	0.66.	0.901	0.526	0.353	Export-enabling services: sales tasks for demand generation
8	Management Analysts	0.676	0.896	0.54	0.353	Export-enabling services: professional advisory
9	Market Research Analysts and Marketing Specialists	0.707	0.897	0.517	0.35	Export-enabling services: market intelligence
10	Brokerage Clerks	0.742	0.892	0.574	0.35	Export-enabling services: transaction-processing

Source: Data from Microsoft Research, Working with AI: Measuring the Applicability of Generative AI to Occupations. Rankings by author analysis.