Sports Coaches, Instructors and Officials

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 11 task statements that define Sports Coaches, Instructors and Officials (ISCO-08 3422) score an average of 0.37 on a 0–1 exposure scale — more exposed than about 71% 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 11 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Compiling scores and other athletic records.”

Scores 0.67 on the 2025 scale. The task of compiling scores and other athletic records is highly suited for automation by Generative AI, as it primarily involves data entry, processing, and organization. Similar tasks such as "Tracking the sports results of athletes in various sports fields" and "Preparing sales registers" received adjusted scores of 0.65 and 0.68, respectively, indicating considerable potential for automation in tasks involving structured data processing and organizing information. Generative AI is particularly effective at handling repetitive data tasks and can efficiently compile, update, and maintain athletic records with minimal human intervention. Human oversight may be needed to handle exceptions or ensure data accuracy, but the core activity of compiling scores from existing data sources can be largely automated. Given Poland's well-established digital infrastructure and access to technology, AI's capabilities can be maximized in this context, justifying a high automation potential for this task.

Moving fastest, 2023 → 2025

“Developing, planning and co-ordinating competitive schedules and programmes;”

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 3422, 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.

• Self-Enrichment Teachers
• Coaches and Scouts
• Umpires, Referees, and Other Sports Officials

In context

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

Sports Coaches, Instructors and Officials sit at the 71st percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Sports Coaches, Instructors and Officials rank in the 71st 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: "Compiling scores and other athletic records.". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Sports Coaches, Instructors and Officials". https://singulariki.com/gradient/3422-sports-coaches-instructors-and-officials.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.