Air Traffic Controllers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 8 task statements that define Air Traffic Controllers (ISCO-08 3154) score an average of 0.31 on a 0–1 exposure scale — more exposed than about 58% 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 8 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Informing flight crew and operations staff about weather conditions, operational facilities, flight plans and air traffic;”

Scores 0.50 on the 2025 scale. The task of informing flight crew and operations staff about weather conditions, operational facilities, flight plans, and air traffic involves significant information processing, data analysis, and communication, areas where Generative AI can assist effectively. Generative AI can automate data synthesis and provide timely updates and alerts about changing weather conditions or flight plans. This task shares similarities with the high-potential automation tasks of analyzing weather data and scheduling, both data-driven and structured in nature, with existing AI models providing substantial aid. However, real-time decision-making, interpreting nuanced changes, and contextual judgment call for human oversight. Compared to similar tasks, like performing meteorological forecasts or managing transport systems, this task has a substantial automation potential given the consistent, structured nature of communication and data involved. The assumption of a high-income country like Poland, where technology integration is feasible, supports a higher score, suggesting significant potential for AI's role in automating information dissemination while maintaining the essential human element for oversight and critical judgments. Thus, an adjusted score of 0.55 reflects current AI capabilities to enhance but not fully automate complex operational tasks in aviation contexts.

Moving fastest, 2023 → 2025

“Applying knowledge of principles and practices of air traffic control in order to identify and solve problems arising in the course of their work;”

Model capability on this task changed by +0.22 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 3154, 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.

• Air Traffic Controllers
• Airfield Operations Specialists

In context

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

Air Traffic Controllers sit at the 58th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Air Traffic Controllers rank in the 58th 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 0.01 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Informing flight crew and operations staff about weather conditions, operational facilities, flight plans and air traffic;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Air Traffic Controllers". https://singulariki.com/gradient/3154-air-traffic-controllers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.