Will AI Replace First-Line Supervisors of Entertainment and Recreation Workers, Except Gambling Services?
First-Line Supervisors of Entertainment and Recreation Workers, Except Gambling Services face a 73.3% AI exposure score with a 51% displacement probability. Core tasks in administration and Management, management of Personnel Resources, and coordination are increasingly automatable.
This occupation scores above the national average of 48/100 by 25.3 points. The primary risk comes from AI's strong performance in management coordination, representing core functions of this role. The absence of physical presence or social interaction requirements increases overall exposure.
Which skills are most at risk?
Each skill in this occupation analyzed against current AI benchmarks. Higher scores = higher AI exposure.
The bottom line for First-Line Supervisors of Entertainment and Recreation Workers, Except Gambling Services
What's most at risk
The role's most exposed skills, specifically Administration and Management, Management of Personnel Resources, Coordination, reach up to 95/100 on AI exposure. AI systems already match or exceed human performance on τ-bench v2, directly targeting these core competencies.
Limited natural protection
This role has no strong physical presence or social interaction requirements, which are the two most reliable barriers to automation. It is predominantly knowledge-based and remote-compatible, which increases overall AI exposure. Workers should proactively build leadership, ethical judgment, and relationship-management capabilities as an active defence against displacement.
Relatively lower-risk skills
This role has no skills in the safe or augmentation category. Even the least-exposed dimensions, such as Service Orientation (36.2/100) and Customer and Personal Service (44.9/100), carry meaningful AI risk. Prioritise building leadership, ethical judgment, and complex stakeholder management: dimensions where AI consistently underperforms across all current benchmarks.
How this compares
At 73.3/100, First-Line Supervisors of Entertainment and Recreation Workers, Except Gambling Services rank above the national average of 48/100. Among the lower-risk occupations in this cluster, safer than First-Line Supervisors of Passenger Attendants (67.5/100). The role sits among the top 30% most AI-exposed occupations.
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Based on skill overlap analysis — these occupations share core competencies with First-Line Supervisors of Entertainment and Recreation Workers, Except Gambling Services but have significantly lower automation exposure.
Common questions about First-Line Supervisors of Entertainment and Recreation Workers, Except Gambling Services and AI
Partial displacement is the most likely outcome. The 51% probability suggests roughly that share of current tasks could be automated, while the remainder stays human-led. Workers who invest in interpersonal coordination and judgment will be well positioned to manage and supervise the AI-handled portions.
It's already happening. AI tools capable of handling administration and Management and management of Personnel Resources are widely deployed in enterprise software today. The question isn't if, but how quickly the remaining positions consolidate. Employment projections for this occupational category reflect continued pressure over the next decade.
This profile has limited natural protection, making active investment especially important. Build capabilities AI consistently struggles with: complex stakeholder management, ethical judgment under uncertainty, creative problem framing, and cross-functional leadership. These aren't easily benchmarked, which is precisely why they retain durable value.
Your skills transfer well to roles like First-Line Supervisors of Farming, Fishing, and Forestry Workers (51.6/100 AI risk, 20% skill overlap), Training and Development Specialists (54.3/100 AI risk, 20% skill overlap), and First-Line Supervisors of Retail Sales Workers (54.5/100 AI risk, 20% skill overlap). PathScorer can analyse your full profile and surface even more personalised matches. Try it free here.
We analyse each occupation's O*NET skill profile, covering 35+ dimensions across knowledge areas, skills, and abilities, and benchmark each against current AI capabilities (MMLU-Pro for language comprehension, τ-bench v2 for task completion, MATH-500 for mathematical reasoning, LiveCodeBench for coding, and others). Each dimension is weighted by its O*NET importance score for the occupation. Physical presence requirements and social interaction levels from O*NET work context data are also factored in. Scores are updated weekly as new AI benchmarks are published. See the full methodology →
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