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AI Automation Index
Will AI Replace Gambling Managers?
Task-level automation exposure for Gambling Managers: 19 O*NET tasks scored against their work activities, plus the simulator.
E-E-A-T Verified
Developed & audited by Gini Loh Automation Exposure
50%
weighted task index
19 of 19 tasks scored
Median Salary
$93,220 / year
BLS OEWS, May 2025 — this exact occupation
Employment Level
Low
5,030 people hold this job — size, not growth
Projected 2025–35
+3.6%
employment change
600 openings a year · BLS line item
Pay by state
BLS OEWS publishes 28 state estimates for this occupation. Each is its own survey estimate, not the national median adjusted for local prices.
$54,640 – $106,670
Highest medians
Texas $106,670
Washington $106,450
Kentucky $105,340
New York $102,350
Michigan $101,860
Lowest medians
Puerto Rico $54,640
Nebraska $61,360
West Virginia $74,270
Iowa $76,350
Maryland $77,320
State figures come from the 11-9071 row in the May 2025 state panel. States where the estimate was suppressed or too small to publish are absent.
What the score is made of
Each task is mapped to its work activities, scored for AI automation potential, and weighted by how important those activities are to the job. These are the components.
Automation potential 50%
Importance-weighted mean of the scored tasks. This is the headline index.
Physical work 5%
Share of weighted tasks in hands-on or machine-operating work activities.
Judgment & influence 41%
Share in analysis, decisions, teaching, directing, advising, or caring for others.
Routine administration 6%
Share in documenting, recording, and administrative activities.
Rewritten by O*NET 0%
Share of weighted tasks O*NET rewrote in a recent update. Reported, not folded into the score.
Consequence of error 29/100
Moderate
O*NET's own rating of how serious an uncorrectable mistake would be here — respondents, not us. Deliberately not in the index: the score measures what AI can do with the task content, and multiplying it by stakes would be our judgment dressed as data.
Constituent Task Mix
19 tasks for this occupation, weighted by work-activity importance. Shares below are of total task weight, not task count.
High exposure (≥ 60% of the task is automatable) 29%
5 tasks
Medium exposure (≥ 35%) 51%
10 tasks
Low exposure (below 35%) 20%
4 tasks
How would you survive?
Move your week away from the high-exposure tasks and toward the ones that need judgment. The simulator recalculates the index as you shift the mix.
Simulate Upskilling →Emerging & rewritten tasks
Tasks O*NET added or rewrote for this occupation in its recent updates (05/2025 – 08/2026), with who proposed them. Additions are not in the scored task inventory — they are new work O*NET is still measuring, so they appear here and nowhere in the index. A Revision replaces a task that is scored, which is why only those move the "Rewritten by O*NET" share.
- New 08/2023 · Incumbent
Monitor the performance of the gaming floor, relocating games and installing new games as necessary.
Constituent Tasks
#7185 · Core · 45% automatable
Resolve customer complaints regarding problems, such as payout errors.
interpersonal — Complaint resolution needs authority and rapport
Resolve customer complaints or problems.
#7186 · Core · 25% automatable
Remove suspected cheaters, such as card counters or other players who may have systems that shift the odds of winning to their favor.
interpersonal — Enforcement coordination requires legal authority and presence
Enforce rules or regulations. Coordinate enforcement of laws or regulations.
#7196 · Core · 70% automatable
Track supplies of money to tables and perform any required paperwork.
data analysis — Cash flow tracking is digital and analyzable
Monitor flow of cash or other resources.
#7190 · Core · 60% automatable
Explain and interpret house rules, such as game rules or betting limits.
text generation — AI drafts policy communications; human approves
Communicate organizational policies and procedures.
#7193 · Core · 77% automatable
Prepare work schedules and station arrangements and keep attendance records.
data analysis — Maintain structured personnel records in systems
Maintain personnel records. Prepare staff schedules or work assignments.
#7191 · Core · 50% automatable
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.
decision support — Broad resource monitoring needs human judgment and physical checks
Monitor resources.
#7187 · Core · 80% automatable
Maintain familiarity with all games used at a facility, as well as strategies or tricks employed in those games.
information retrieval — AI summarizes developments; human retains expertise
Maintain knowledge of current developments in area of expertise.
#7188 · Core · 50% automatable
Train new workers or evaluate their performance.
decision support — Performance review aided; accountability and context human
Evaluate employee performance. Conduct employee training programs.
#20472 · Core · 55% automatable
Market or promote the casino to bring in business.
text generation — Promotional materials drafted; human executes outreach
Promote products, services, or programs.
#7192 · Core · 30% automatable
Interview and hire workers.
interpersonal — Live interviewing is human relational work
Interview employees, customers, or others to collect information. Hire personnel.
#7194 · Core · 25% automatable
Direct the distribution of complimentary hotel rooms, meals, or other discounts or free items given to players, based on their length of play and betting totals.
interpersonal — Guest services management is live human relationship work
Manage guest services.
#7195 · Core · 55% automatable
Establish policies on issues, such as the type of gambling offered and the odds, the extension of credit, or the serving of food and beverages.
decision support — AI drafts policies; accountability and context are human
Develop organizational policies or programs.
#7189 · Supplemental · 38% automatable
Circulate among gaming tables to ensure that operations are conducted properly, that dealers follow house rules, or that players are not cheating.
situated mobility — Live monitoring of people in physical settings
Monitor activities of individuals to ensure safety or compliance with rules.
#7197 · Supplemental · 50% automatable
Set and maintain a bank and table limit for each game.
decision support — AI models pricing; human sets monetary policy
Determine pricing or monetary policies.
#7201 · Supplemental · 85% automatable
Direct the compilation of summary sheets that show wager amounts and payoffs for races or events.
data analysis — Compiling operational data from sources is automatable
Compile operational data.
#7199 · Supplemental · 50% automatable
Review operational expenses, budget estimates, betting accounts, or collection reports for accuracy.
decision support — Audit fieldwork and attestation remain human-owned
Conduct financial or regulatory audits.
#7200 · Supplemental · 35% automatable
Record, collect, or pay off bets, issuing receipts as necessary.
physical manipulation — In-person payment handling; some digital automation
Collect payments for goods or services.
#7202 · Supplemental · 8% automatable
Notify board attendants of table vacancies so that waiting patrons can play.
physical manipulation — Embodied signaling in shared physical workspace
Signal others to coordinate work activities.
#7198 · Supplemental · 50% automatable
Monitor credit extended to players.
decision support — Broad resource monitoring needs human judgment and physical checks
Monitor resources.
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How this was scored
- 1. Tasks. O*NET task statements for this occupation, from a release whose newest record is dated 08/2026.
- 2. Work activities. Each task maps to 1–5 Detailed Work Activities; each activity belongs to one of 41 Generalized Work Activities that O*NET rates for importance (1–5) in this occupation.
- 3. Automation potential. Every work activity carries one score for how far current AI can perform it end-to-end. Tasks inherit the mean of their activities.
- 4. Weighting. Task weight = 0.35 + 0.65 × (importance − 1)/4, scaled by how often the task is performed and by 0.7 for Supplemental tasks. Importance is the task's own O*NET rating where it exists (18,369 of 18,838 tasks); the rest inherit their work activities' importance. The index is the weighted mean.
Check: across the 910 occupations where O*NET also rates how far machine speed sets the pace, this index moves opposite to it (Spearman ρ = -0.49). The more a job is paced by equipment, the less of its task content AI can address — the relationship you would expect if the scoring is working.
Sources · O*NET 31.0 (task records to 08/2026) · BLS OEWS May 2025 · BLS projections 2025-2035 · rubric v2.1 · tasks for O*NET, not a forecast
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