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AI Automation Index
Will AI Replace First-Line Supervisors of Entertainment and Recreation Workers, Except Gambling Services?
Task-level automation exposure for First-Line Supervisors of Entertainment and Recreation Workers, Except Gambling Services: 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
$48,560 / year
BLS OEWS, May 2025 — this exact occupation
Employment Level
High
103,190 people hold this job — size, not growth
Projected 2025–35
+5.3%
employment change
10,600 openings a year · BLS line item
Pay by state
BLS OEWS publishes 53 state estimates for this occupation. Each is its own survey estimate, not the national median adjusted for local prices.
$27,990 – $62,900
Highest medians
Washington $62,900
District of Columbia $62,090
Delaware $59,790
Maine $59,050
Colorado $58,640
Lowest medians
Puerto Rico $27,990
Guam $28,850
West Virginia $36,230
Arkansas $36,830
New Mexico $37,220
State figures come from the 39-1014 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 0%
Share of weighted tasks in hands-on or machine-operating work activities.
Judgment & influence 47%
Share in analysis, decisions, teaching, directing, advising, or caring for others.
Routine administration 4%
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 3/100
Low
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) 30%
6 tasks
Medium exposure (≥ 35%) 47%
9 tasks
Low exposure (below 35%) 23%
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 →Constituent Tasks
#23159 · Core · 68% automatable
Assign work schedules, following work requirements, to ensure quality and timely delivery of service.
decision support — Assignments drafted; accountability and negotiation human
Assign duties or work schedules to employees.
#23175 · Core · 50% automatable
Train workers in proper operational procedures and functions and explain company policies.
information retrieval — AI explains policies; human handles live questions
Explain regulations, policies, or procedures. Train service staff.
#23162 · Core · 50% automatable
Furnish customers with information on events or activities.
information retrieval — Event info retrieval assisted; patron interaction human
Provide attraction or event information to patrons.
#23160 · Core · 54% automatable
Collaborate with staff members to plan or develop programs of events or schedules of activities.
text generation — Service plans drafted; operational ownership human
Develop plans for programs or services. Confer with organizational members to accomplish work activities.
#23172 · Core · 45% automatable
Resolve customer complaints regarding worker performance or services rendered.
interpersonal — Complaint resolution needs authority and rapport
Resolve customer complaints or problems.
#23168 · Core · 34% automatable
Plan, direct, or supervise recreational and entertainment activities led by staff, such as sports, aquatics, games, or performing arts.
interpersonal — Event organizing aided; logistics and presence human
Organize recreational activities or events. Manage operations of artistic or entertainment departments or organizations.
#23161 · Core · 28% automatable
Direct or coordinate the activities of entertainment and recreation related workers.
interpersonal — Directing service workers requires live human presence
Supervise service workers.
#23170 · Core · 45% automatable
Recruit and hire staff members.
decision support — Broad HR work needs human judgment and accountability
Perform human resources activities.
#23163 · Core · 62% automatable
Inform workers about interests or special needs of specific groups.
information retrieval — AI explains policies; human handles live questions
Explain regulations, policies, or procedures.
#23169 · Core · 45% automatable
Provide staff with assistance in performing difficult or complicated duties.
interpersonal — AI can coach content; mentorship relationship is human
Support the professional development of others.
#23171 · Core · 75% automatable
Requisition supplies and equipment necessary for workers to facilitate recreational or entertainment activities, such as safety harnesses, flash lights, or first aid kits.
decision support — Ordering via systems automatable; human approves spend
Order materials, supplies, or equipment.
#23164 · Core · 20% automatable
Inspect work areas or operating equipment to ensure conformance to established standards in areas such as cleanliness or maintenance.
visual inspection — Facility inspection is inherently physical
Inspect facilities. Inspect equipment to ensure proper functioning.
#23165 · Core · 65% automatable
Meet with managers or other supervisors to stay informed of changes affecting workers or operations.
information retrieval — AI synthesizes business info; contextual grasp is human
Maintain knowledge of business operations.
#23173 · Core · 28% automatable
Serve as a point of contact between managerial staff and leaders of recreational or entertainment activities.
interpersonal — Managing arts departments is creative leadership and relationships
Manage operations of artistic or entertainment departments or organizations.
#23174 · Core · 55% automatable
Take disciplinary action to address performance problems.
decision support — Performance review aided; accountability and context human
Evaluate employee performance.
#23158 · Core · 45% automatable
Apply customer feedback to service improvement efforts.
interpersonal — Complaint resolution needs authority and rapport
Resolve customer complaints or problems.
#23166 · Core · 55% automatable
Observe and evaluate workers' appearance and performance to ensure quality service and compliance with specifications.
decision support — Performance review aided; accountability and context human
Evaluate employee performance.
#23157 · Core · 80% automatable
Analyze and record personnel or operational data and write related activity reports.
text generation — Prepare operational reports from data
Prepare operational reports or records.
#23167 · Core · 70% automatable
Participate in continuing education to stay abreast of industry trends and developments.
information retrieval — AI supports study; certification requires human
Maintain professional knowledge or certifications.
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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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