Bookmakers, Croupiers and Related Gaming Workers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 5 task statements that define Bookmakers, Croupiers and Related Gaming Workers (ISCO-08 4212) score an average of 0.45 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 5 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Preparing and issuing lists of approximate odds;”

Scores 0.65 on the 2025 scale. The task "Preparing and issuing lists of approximate odds" involves activities that are numerically intensive, structured, and repetitive in nature. Generative AI can significantly aid in calculating and compiling lists based on probability data, which aligns closely with tasks such as "Accepting betting slips" or "Preparing reports" that involve similar structured data handling and have automation scores in the range of 0.5375 to 0.65. Unlike tasks needing nuanced judgment or physical interaction, this task is more deterministic, focusing on data processing and analysis, allowing a higher degree of automation. In Poland, where access to technology is high, the infrastructure facilitates the use of advanced AI tools, supporting a higher automation potential. Considering the capabilities of AI to automate computational and data sorting tasks, the adjusted score of 0.62 is appropriate, reflecting significant automation feasibility while acknowledging a need for human oversight to ensure accuracy and compliance with regulatory frameworks.

Moving fastest, 2023 → 2025

“Distributing cards, rolling dice or spinning a roulette wheel;”

Model capability on this task changed by +0.08 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 4212, 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.

• Gambling Dealers
• Gambling Service Workers, All Other
• Gambling Cage Workers
• Gambling and Sports Book Writers and Runners

In context

Part of the 4 - Clerical support workers major group. Return to the full gradient to see how the whole group sits.

Bookmakers, Croupiers and Related Gaming Workers sit at the 82nd percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Bookmakers, Croupiers and Related Gaming Workers 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 0.07 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Preparing and issuing lists of approximate odds;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Bookmakers, Croupiers and Related Gaming Workers". https://singulariki.com/gradient/4212-bookmakers-croupiers-and-related-gaming-workers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.