Athletes and Sports Players

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 8 task statements that define Athletes and Sports Players (ISCO-08 3421) score an average of 0.23 on a 0–1 exposure scale — more exposed than about 41% 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 Not exposed 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

“Undertaking sports promotional activities and media interviews;”

Scores 0.40 on the 2025 scale. The task of undertaking sports promotional activities and media interviews involves a mix of strategic communication, creativity, and human interaction, areas where Generative AI can assist but not fully automate. In the context provided, similar tasks include "Conducting promotional activities for the agency's offer," which had an adjusted score of 0.4. Generative AI can support in generating promotional content, analyzing market trends, and even managing some aspects of communication through AI-driven communication tools like chatbots. However, like the tasks of organizing promotional events (adjusted scores of 0.365 and 0.38) and eliminating athletes from competition for violations (score of 0.24), human oversight remains crucial for tailored communication, strategic planning, and building interpersonal relationships, which are essential aspects of promotional activities and interviews. Given Poland's high digital infrastructure, AI's support potential is notable but not complete, reflecting a reliance on human judgment and creativity. The adjusted score of 0.355 balances AI capabilities with the significant human elements required in sports promotions and media engagement tasks.

Moving fastest, 2023 → 2025

“Undertaking sports promotional activities and media interviews;”

Model capability on this task changed by +0.20 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 3421, 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.

• Athletes and Sports Competitors

In context

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

Athletes and Sports Players sit at the 41st percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Athletes and Sports Players rank in the 41st 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 0.03 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Undertaking sports promotional activities and media interviews;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Athletes and Sports Players". https://singulariki.com/gradient/3421-athletes-and-sports-players.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.