# Gambling Managers

> Plan, direct, or coordinate gambling operations in a casino. May formulate house rules.

- **SOC code:** 11-9071.00
- **Canonical URL:** https://singulariki.com/roles/role-11-9071-00
- **Also known as:** Casino Manager, Casino Shift Manager, Slot Manager, Table Games Manager, Casino Operations Manager, Gaming Manager, Pit Manager, Shift Manager
- **Frame:** "AI exposure" means task overlap (how codifiable the work is), not jobs lost or a forecast. Every figure below is traced to a named public dataset.

## What this work is

**Core tasks** (O*NET):
- Resolve customer complaints regarding problems, such as payout errors.
- Remove suspected cheaters, such as card counters or other players who may have systems that shift the odds of winning to their favor.
- Circulate among gaming tables to ensure that operations are conducted properly, that dealers follow house rules, or that players are not cheating.
- Track supplies of money to tables and perform any required paperwork.
- Set and maintain a bank and table limit for each game.
- Explain and interpret house rules, such as game rules or betting limits.
- Prepare work schedules and station arrangements and keep attendance records.
- Monitor staffing levels to ensure that games and tables are adequately staffed for each shift, arranging for staff rotations and breaks and locating substitute employees as necessary.
- Direct the compilation of summary sheets that show wager amounts and payoffs for races or events.
- Maintain familiarity with all games used at a facility, as well as strategies or tricks employed in those games.
- Train new workers or evaluate their performance.
- Review operational expenses, budget estimates, betting accounts, or collection reports for accuracy.

**Emerging tasks** (O*NET):
- Monitor the performance of the gaming floor, relocating games and installing new games as necessary.

## Skills, tools, capabilities

**Knowledge, skills & abilities** (O*NET, highest importance first):
- Customer and Personal Service _(knowledge)_
- English Language _(knowledge)_
- Administration and Management _(knowledge)_
- Mathematics _(knowledge)_
- Personnel and Human Resources _(knowledge)_
- Critical Thinking _(essential_skill)_
- Management of Personnel Resources _(transferable_skill)_
- Oral Expression _(ability)_
- Speaking _(essential_skill)_
- Monitoring _(essential_skill)_
- Oral Comprehension _(ability)_
- Inductive Reasoning _(ability)_

**Skills in demand:**
- English Language _(Common Skill)_
- Mathematics _(Common Skill)_
- Critical Thinking _(Common Skill)_
- Inductive Reasoning _(Common Skill)_
- Time Management _(Common Skill)_
- Speech Recognition _(Specialized Skill)_
- Social Perceptiveness _(Common Skill)_
- Deductive Reasoning _(Common Skill)_
- Complex Problem Solving _(Common Skill)_
- Active Listening _(Common Skill)_
- Writing _(Common Skill)_
- Reading Comprehension _(Common Skill)_

**Tools & technology:**
- Microsoft Excel _(hot technology, in demand)_
- Microsoft Office software _(hot technology, in demand)_
- Microsoft Outlook _(hot technology, in demand)_
- Microsoft PowerPoint _(hot technology)_
- Microsoft Word _(hot technology)_
- Employee scheduling software
- Human resources management system HRMS
- Web browser software

## AI exposure & outlook

- **AI task-overlap index:** 67th percentile (High) across all occupations — composite of current-era exposure studies (ai-exposure-index-v1).
- **Overall AI exposure (Felten et al.):** 68th percentile (High) — source: felten_aioe.
- **LLM task exposure, γ (OpenAI / Eloundou):** 68th percentile (High) — source: eloundou_gamma.
- **AI assistant applicability (Microsoft):** 64th percentile (Moderate) — source: microsoft_applicability.
- **Frey–Osborne (2013, historical computerization estimate):** 28th percentile — kept separate from current-era studies.
- **Remote-capable (Dingel–Neiman):** no — task structure, not who actually works remote.
- **Projected employment (BLS 2024–34):** 1.2% growth (About average); 0.6k annual openings; 5.1k → 5.2k jobs.
- **Pay & employment (BLS OEWS, May 2024):** median $85,580; 4,620 employed.

## How people actually use AI here

Anthropic Economic Index — measured AI conversations mapped to this occupation's tasks:

- **Autonomy median:** 4.0 (higher = AI acts more independently).

**Tasks most handed to AI here:**
- Explain and interpret house rules, such as game rules or betting limits. _(0.3% of measured AI use)_

**Example prompts (honest phrasings of the tasks above — starting points, not endorsed instructions):**
- Help me explain and interpret house rules, such as game rules or betting limits.

## Sources

- **O*NET** (30.3) — U.S. Department of Labor / National Center for O*NET Development. https://www.onetcenter.org/database.html
- **BLS Occupational Employment and Wage Statistics (OEWS)** (May 2024) — U.S. Bureau of Labor Statistics. https://www.bls.gov/oes/
- **BLS Employment Projections** (2024–2034) — U.S. Bureau of Labor Statistics. https://www.bls.gov/emp/
- **Anthropic Economic Index** (v4 (2026-01-15) + v2 (2025-03-27)) — Anthropic. https://www.anthropic.com/economic-index
- **Microsoft “Working with AI”** (working-with-ai) — Microsoft Research. https://www.microsoft.com/en-us/research/
- **“GPTs are GPTs” (Eloundou et al.)** (arXiv 2303.10130) — OpenAI / academic. https://arxiv.org/abs/2303.10130
- **AI Occupational Exposure (AIOE)** (Felten, Raj & Seamans) — academic. https://github.com/AIOE-Data/AIOE
- **Frey & Osborne (2013)** (frey-osborne-automation) — academic. https://www.oxfordmartin.ox.ac.uk/publications/the-future-of-employment/
- **Dingel & Neiman (2020)** (dingel-neiman-workathome) — academic. https://github.com/jdingel/DingelNeiman-workathome

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_Generated from Singulariki's joined dataset; data snapshot 2026-06-02T21:00:32.945303+00:00. https://singulariki.com/roles/role-11-9071-00_