First-Line Supervisors of Entertainment and Recreation Workers, Except Gambling Services
39-1014.00Median wage $48,560/yr103,190 employed (US)Rank #475 of 923 scored · top 51% by substitution
Directly supervise and coordinate activities of entertainment and recreation related workers.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution28
Exposure23
Augmentation57
Substitution — the headline: capability discounted by cost, barriers and adoption.
Exposure — technical capability alone, regardless of whether anyone deploys it.
Augmentation — how much AI assists without replacing. High here + moderate substitution = a changing job, not a disappearing one.
Tasks on the substitution scale
19 rated tasks, binned by substitution score.
Position among all scored occupations
Distribution of 923 occupation scores; the marker is this occupation.
Tasks with substitution ≥ 70
0%
Run 1.0.0-draft.1 · computed 2026-08-05 · rater panel: claude-sonnet-5, claude-haiku-4-5-20251001 · intervals span rater disagreement.
Why this score
The five weighted dimensions of the composite, averaged across this occupation's tasks (importance-weighted, panel mean). Exact weights and formulas: /api/v1/methodology.
Task automatabilityw 35%22
panel mean rating 1.9/5 → substitution pressure 22/100
Technical feasibility todayw 20%24
panel mean rating 2.0/5 → substitution pressure 24/100
Cost vs. human wagew 15%29
panel mean rating 2.2/5 → substitution pressure 29/100
Adoption barriersw 20%inverted — strong barriers lower the score42
panel mean rating 3.3/5 (barrier strength) → substitution pressure 42/100
Sector adoption velocityw 10%23
panel mean rating 1.9/5 → substitution pressure 23/100
Task breakdown (19 tasks)
Substitution pressure per task, weighted by O*NET importance in the composite. Expand a task for the full rater audit trail — every rating, every model, every rationale.
Furnish customers with information on events or activities.
64CI 47–81 · exposure 55 · augmentation 75 · click for rater detail
Furnish customers with information on events or activities.
64| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Entertainment and recreation venues have adopted self-serve kiosks and basic chatbots to handle event inquiries, but adoption remains patchy and supervisors remain primary channels for complex or high-value customer interactions in most settings. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Recreation and entertainment venues are often smaller, less digitized operations with slower AI adoption compared to finance or professional services, though some larger chains use chatbots. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI assistants (event databases, automated FAQ systems, integration with booking systems) significantly help supervisors provide faster, more accurate information retrieval and scheduling assistance, allowing them to focus on relationship-building and problem-solving rather than routine lookups. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools like chat assistants, FAQ bots, and scheduling systems can significantly speed up and improve accuracy of supervisors relaying event information to customers. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can retrieve and present basic event information, the task involves dynamic customer interaction requiring contextual understanding of customer needs, preferences, and the ability to handle follow-up questions or complex requests. Current systems struggle with nuanced, context-dependent customer guidance at supervisory quality standards. |
| Task automatability | claude-sonnet-5 | 4/5 | Providing information on events/activities is largely a text/knowledge retrieval task that chatbots and virtual assistants can handle with high time savings, especially with structured data feeds.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | There are minimal regulatory or licensing barriers to automating information provision; however, customer preference for human interaction and supervisor accountability for event details and last-minute changes create modest friction against full substitution. |
| Adoption barriers | claude-sonnet-5 | 1/5 | No licensing, liability, or regulatory requirement mandates a human provide this basic informational service. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Deployed chatbots and information kiosks are significantly cheaper per interaction than paying a supervisor to answer routine event queries; however, ongoing maintenance and oversight prevents a full 5x cost advantage over lower-wage customer service staff. |
| Cost vs. human wage | claude-sonnet-5 | 5/5 | Automated information systems (chatbots, kiosks, websites) cost a fraction of a cent per query versus a staffed employee's hourly wage for the same repetitive informational task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Chatbots and AI information systems deploy in limited production (e.g., automated event FAQs, venue websites), but they typically handle routine queries only and often require human escalation for non-standard requests or relationship-based interactions that supervisors provide. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | Chatbots, IVR systems, and website assistants already handle event/activity info queries reliably in many venues (theme parks, gyms, recreation centers) though edge cases and complex questions still need human backup. |
Analyze and record personnel or operational data and write related activity reports.
54CI 39–70 · exposure 62 · augmentation 88 · click for rater detail
Analyze and record personnel or operational data and write related activity reports.
54| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Entertainment and recreation is a fragmented, often small-business sector with low IT sophistication; while larger corporate chains may pilot analytics, most venues lack the data infrastructure or compliance readiness for AI-driven personnel reporting, resulting in slow, shallow adoption. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Recreation and entertainment services are a lower-digitization sector with slower AI tool adoption compared to finance or professional services, though basic reporting software is common. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can significantly assist supervisors by auto-populating operational data summaries, suggesting report structures, highlighting anomalies in attendance or safety metrics, and generating draft language, allowing the supervisor to focus on judgment and personnel interpretation while retaining oversight. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI tools can strongly assist in compiling data, spotting trends, and drafting narrative reports, letting supervisors focus on judgment calls while dramatically speeding up write-up time. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | Data analysis and report writing on structured operational metrics can be partially automated with current AI systems (data extraction, basic summarization, formatting), but judgment about personnel performance nuance and context-specific interpretation typically requires human review, limiting end-to-end time savings to roughly 40-50% depending on data standardization. |
| Task automatability | claude-sonnet-5 | 4/5 | Analyzing structured operational/personnel data and drafting reports is largely text/data synthesis work that current LLMs and BI tools handle well, though some data gathering and validation still requires human input.4 |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Personnel records are legally protected (HR/labor law), many venues face union or employee-consent requirements for automated performance tracking, and liability for incorrect personnel documentation creates strong legal and organizational friction against full automation without human sign-off. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates a human write these reports; main friction is organizational habit and need for a supervisor's sign-off on accuracy. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | While AI inference for data processing is cheap, the integration cost for reliable personnel data handling, compliance oversight, and human review of judgment-sensitive outputs (performance records) makes the total cost per task roughly comparable to or slightly cheaper than a supervisor's hourly wage for this component of work. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated data aggregation and report drafting via software/AI tools costs a small fraction of a supervisor's hourly wage once set up, though integration with recreation-specific systems adds some cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Commercial tools exist for data logging, basic analytics, and automated report generation (e.g., BI platforms, template-based systems), but production systems often require manual data cleaning, human verification of personnel assessments, and customization to venue-specific needs, resulting in material error rates and narrow applicability. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | Products like BI dashboards, spreadsheet AI add-ins, and LLM report generators are widely deployed in production for exactly this kind of reporting task across many industries. |
Assign work schedules, following work requirements, to ensure quality and timely delivery of service.
41CI 30–52 · exposure 38 · augmentation 75 · click for rater detail
Assign work schedules, following work requirements, to ensure quality and timely delivery of service.
41| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Entertainment and recreation sectors are typically smaller, more distributed, and slower to digitize than finance or tech; scheduling automation has modest traction outside corporate chains. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Recreation and entertainment services are a moderately low-digitization sector with slower AI adoption compared to finance or professional services, though scheduling tools are common. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI-powered scheduling assistants can generate options, flag conflicts, and propose optimizations that supervisors review and adjust, substantially raising their speed and coverage without removing human judgment on fairness and service quality. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI scheduling assistants can significantly speed up draft schedule creation and constraint checking, with the supervisor reviewing and finalizing decisions. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Scheduling individual workers against multiple constraints (availability, skill-fit, service demand) requires judgment calls and exception handling that current AI struggles with reliably at scale. While AI can generate candidate schedules, validating quality and handling real-time conflicts still demands human oversight. |
| Task automatability | claude-sonnet-5 | 3/5 | Scheduling logic can be handled by AI/algorithmic tools given labor rules and demand forecasts, but adapting to last-minute staff issues, facility-specific quirks, and worker preferences still requires human judgment for full end-to-end automation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | There is organizational and employee preference for human scheduling decisions (fairness, flexibility concerns), and union contracts or labor agreements may require human discretion in shift assignment. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement for scheduling, but union rules, labor law compliance, and manager accountability create some friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Scheduling tools are available but don't eliminate the supervisor role; the cost of the tool plus required human oversight and modification often approaches or exceeds the cost of human scheduling alone. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | Scheduling software subscriptions are cheap relative to a supervisor's wage for this sub-task, but integration, exception handling, and oversight reduce the savings to roughly comparable levels for smaller operations. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Scheduling software exists but typically requires manual tweaking and human sign-off; no deployed system fully replaces supervisors in assigning schedules for entertainment/recreation teams with the quality and service guarantees needed. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Workforce scheduling software with AI-assisted optimization is widely deployed (retail, hospitality), but entertainment/recreation contexts with variable events and skill requirements often still need manual adjustment. |
Apply customer feedback to service improvement efforts.
40CI 30–50 · exposure 30 · augmentation 75 · click for rater detail
Apply customer feedback to service improvement efforts.
40| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Entertainment and recreation sectors are middling digitizers; larger chains and resorts use feedback tools and analytics, but many smaller venues still rely on informal feedback loops. Adoption of AI-driven feedback analysis is spreading but not yet standard practice across the industry. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Entertainment and recreation services are a lower-digitization sector with slower AI adoption compared to information or finance industries, though feedback tools are gradually spreading. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI tools that surface patterns in customer feedback, flag high-impact issues, and suggest improvement themes significantly boost supervisor productivity and decision quality. The human remains essential but works much faster and more data-driven with AI assistance. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools can efficiently aggregate, categorize, and highlight trends in customer feedback, significantly speeding up the analysis phase that supervisors then act upon. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI can summarize and categorize customer feedback at scale, but applying insights to actual service improvement requires judgment about feasibility, priority, and implementation strategy that remains primarily human-driven. Automation covers perhaps 20–30% of the end-to-end task. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can help summarize and analyze customer feedback, but translating insights into actual service changes requires managerial judgment, staff coordination, and operational decisions that AI cannot execute end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | No legal requirement that a human must apply feedback, but organizational culture, risk aversion around service changes, and the need for supervisor sign-off create meaningful adoption friction. No hard licensing barrier, but internal governance slows substitution. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but organizational structure typically vests this responsibility in a supervisor role with accountability for staff and customer relations, creating moderate structural friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI feedback analysis and categorization cost is now very low (cloud NLP services), while the human supervisory work that remains (prioritization, change design, rollout) still commands a salary. The AI-augmented process is substantially cheaper than manual feedback review alone. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While feedback analysis software is cheap, the managerial component (deciding and enacting changes) still requires human labor, keeping overall costs comparable to a human supervisor doing this work. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | NLP tools and sentiment-analysis platforms reliably extract feedback themes, but no deployed system fully automates the decision-making step of *which* improvements to implement and how. Products assist but don't replace the supervisory judgment. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Sentiment analysis and feedback-summarization tools are deployed in many businesses, but the full loop of interpreting feedback and implementing service improvements is not handled reliably by any current product. |
Requisition supplies and equipment necessary for workers to facilitate recreational or entertainment activities, such as safety harnesses, flash lights, or first aid kits.
39CI 25–52 · exposure 38 · augmentation 63 · click for rater detail
Requisition supplies and equipment necessary for workers to facilitate recreational or entertainment activities, such as safety harnesses, flash lights, or first aid kits.
39| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Recreation and entertainment are fragmented, often smaller-scale operations with low digitization levels. While some large chains may use automated procurement systems, adoption of AI for supervisory supply requisitioning remains limited and experimental in this sector. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Recreation and entertainment services are a low-digitization, often small-organization sector with limited AI adoption for back-office logistics tasks like this. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI could assist supervisors by suggesting needed supplies based on activity schedules or checklists, flagging low inventory, or auto-populating requisition forms. However, the supervisory judgment required for appropriate equipment selection and vendor choices means assistance is partial rather than transformative. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-assisted inventory tracking, reorder alerts, and demand forecasting can meaningfully streamline the supervisor's requisition process while they retain final decision authority. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI could help identify what supplies are needed and generate requisition lists, the task requires understanding operational context, inventory levels, and worker needs that typically demand human judgment. Most organizations still require human sign-off on orders and vendor selection, making full end-to-end automation unlikely to save 50% time at equal quality. |
| Task automatability | claude-sonnet-5 | 3/5 | Determining supply needs based on activity plans, inventory checks, and vendor ordering can largely be automated via inventory management and procurement software, but final judgment on safety-critical equipment still requires human oversight. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Requisitioning authority and financial accountability typically rest with licensed supervisors or managers who must legally sign off on orders and equipment purchases. Many organizations have formal policies requiring human authorization, and liability for incorrect safety equipment selection (e.g., harnesses) creates strong legal barriers. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement blocks automation of ordering, though safety equipment sign-off may involve organizational accountability and liability concerns that create some friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The cost of setting up, integrating, and monitoring an AI system for supply requisitioning would likely exceed the wages of a supervisor performing this task manually, especially given the small amount of time this task consumes relative to overall supervisory duties. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | Automated procurement software reduces labor time modestly, but the task also involves physical inspection and judgment calls that still require paid staff time, keeping costs roughly comparable to human-only handling. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed products reliably perform this supervisory supply requisitioning task autonomously today. While procurement and inventory systems exist, they require human input and decision-making. AI integration into existing organizational procurement workflows is nascent and typically narrow in scope. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Off-the-shelf inventory/procurement systems and e-commerce ordering tools reliably handle reordering, but integrating them with activity-specific needs assessment for recreation supervisors is not a mature, widely deployed product category. |
Recruit and hire staff members.
31CI 25–37 · exposure 30 · augmentation 63 · click for rater detail
Recruit and hire staff members.
31| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Mid-market and larger organizations have deployed AI screening tools in recruitment pipelines, but adoption is uneven and typically augmentative rather than replacement-oriented. Small recreation venues and entertainment venues show slower adoption due to size and informal hiring practices. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Recreation and entertainment services are a low-digitization, often small-business sector where formal AI-based hiring tools are rarely adopted for frontline supervisory hiring. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI-powered recruiting tools meaningfully assist supervisors by quickly filtering applications, flagging qualified candidates, and reducing time on administrative screening tasks. This allows managers to focus on interviews and relationship-building while maintaining decision authority. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help draft job ads, screen initial applications, and schedule interviews, meaningfully assisting supervisors while they retain final hiring judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can automate resume screening and initial candidate matching, recruiting and hiring requires human judgment on cultural fit, interpersonal skills, and decision-making authority. Current AI tools handle only early-stage portions; the full pipeline from posting through final hiring decision cannot meet the 50% time-saving threshold without substantial human oversight. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can screen resumes and draft job postings, but the full recruit-and-hire cycle involves interviewing, judgment calls, and relationship-building that current systems cannot fully replace at equal quality. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Hiring is legally bound to human accountability (employment law, discrimination compliance, offer authority). Managers and HR often face liability for hiring decisions, and organizational culture typically requires humans to meet and select candidates, creating both regulatory and structural barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement to hire, but employment law, anti-discrimination liability, and organizational preference for human judgment in hiring decisions create moderate friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI recruiting tools typically cost hundreds to thousands monthly per role, while supervisor time investment is substantial. Full replacement is not occurring; most organizations use AI as a filtering layer, not a cost substitute, so the all-in cost comparison remains unfavorable. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI screening tools are cheap, but the overall hiring process still requires human interviewers and decision-makers, so total cost savings are modest for this occupation's scale. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Products like Workable, LinkedIn Recruiter, and applicant tracking systems with AI screening exist in production, but they focus on candidate sourcing and filtering. Hiring itself—final selection and offer decisions—remains legally and organizationally the province of human managers; no mature system performs end-to-end hiring autonomously. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | ATS and AI screening tools are deployed widely for resume filtering, but actual hiring decisions and interviews still rely on human supervisors, especially in small entertainment/recreation venues. |
Collaborate with staff members to plan or develop programs of events or schedules of activities.
30CI 25–35 · exposure 25 · augmentation 63 · click for rater detail
Collaborate with staff members to plan or develop programs of events or schedules of activities.
30| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Entertainment and recreation is a mid-digitization sector with many smaller, non-tech-forward operators; adoption of AI for supervisory collaboration remains low—most rely on email, spreadsheets, and ad-hoc meetings rather than intelligent planning automation. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Recreation and entertainment sectors are generally slower adopters of AI compared to information or finance industries, with limited production use for collaborative planning tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can usefully assist by generating schedule drafts, flagging conflicts, and surfacing resource constraints, allowing supervisors to focus on stakeholder buy-in and creative decisions; however, the augmentation is limited to data-organization and optimization rather than transforming the core collaborative aspect. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools can meaningfully assist by generating draft schedules, suggesting activities, and analyzing past program data, improving efficiency while humans retain collaborative decision-making. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI can assist with scheduling optimization and event logistics (e.g., calendar conflicts, resource allocation), but the core task requires collaborative judgment, creative input from multiple stakeholders, and understanding nuanced human preferences that current systems cannot reliably synthesize end-to-end into production plans. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can help draft schedules or brainstorm activity ideas, but the core task requires interpersonal collaboration, negotiation with staff, and situational judgment that current AI cannot fully replace end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Supervisory roles involve accountability for team morale, staff input legitimacy, and organizational culture—stakeholders strongly prefer human judgment in collaborative planning; liability and organizational friction around automation of team-coordination tasks create meaningful adoption friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but organizational and interpersonal norms around staff collaboration and leadership create moderate friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI scheduling and planning tools are available but require significant manual oversight and human judgment to validate outputs; combined cost rivals or exceeds that of a supervisor doing the work directly, especially considering integration and error correction. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While AI scheduling tools are cheap to run, the human collaborative planning and staff coordination still requires paid supervisor time, so cost savings are limited to partial task components. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably performs collaborative program planning with staff; scheduling tools exist but do not capture the interactive, consensus-building aspect of the task or translate it into coherent event/activity programs. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Scheduling and planning software with AI features exist, but no deployed product autonomously 'collaborates with staff' to co-develop event programs; humans remain central to the process. |
Inform workers about interests or special needs of specific groups.
30CI 23–37 · exposure 25 · augmentation 50 · click for rater detail
Inform workers about interests or special needs of specific groups.
30| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Entertainment and recreation sectors have lower overall digitization and AI adoption than information-intensive industries, and supervisor-level decision-making in these sectors remains heavily human-centered with limited automation track records in production. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Entertainment and recreation supervision is a low-digitization, physical, in-person sector with minimal AI agent deployment for interpersonal team management tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can usefully assist by gathering, organizing, and summarizing information about group interests or accessibility needs from diverse sources, helping supervisors prepare and structure their communications more efficiently while the supervisor retains the essential role of interpreting and delivering context-aware guidance. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI tools can help supervisors prepare notes, checklists, or communication points about specific group needs, offering moderate assistance while the human still delivers and adapts the information. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can aggregate and summarize information about group interests or needs from data sources, the task fundamentally requires real-time awareness of context-specific group dynamics and the ability to tailor communication to workers in situ, which demands human judgment and ongoing relationship management that current AI cannot reliably handle end-to-end. |
| Task automatability | claude-sonnet-5 | 2/5 | This task involves interpersonal communication, situational judgment, and dynamic team briefing that current AI cannot fully replace, though drafting communications could be assisted.some tasks like generating notes could be automated but the core supervisory act cannot. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Supervisors have direct human-contact and accountability requirements; they are responsible for understanding and communicating nuanced group needs to their teams, and organizational culture emphasizes human leadership in this interpersonal function, creating substantial friction against full substitution. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No strict licensing requirement, but organizational reliance on a trusted supervisor with situational awareness and accountability creates moderate friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI systems for gathering and organizing information about group needs are inexpensive to operate relative to the supervisor time saved in information collection and drafting, though human oversight and judgment remain necessary, keeping the ratio favorable overall. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Human supervisors already perform this as part of broader duties at low marginal cost, and AI would add integration overhead without clearly displacing the interpersonal communication component. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed products reliably perform this task in production environments. AI systems can draft generic information or pull from databases, but supervisors must synthesize real-time, situational knowledge about specific groups and deliver it authentically to staff, which deployed systems do not do with reliable accuracy. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | No deployed product autonomously manages real-time supervisor-to-worker briefings about specific group needs in recreation settings; at best AI could help draft informational memos. |
Inspect work areas or operating equipment to ensure conformance to established standards in areas such as cleanliness or maintenance.
28CI 25–30 · exposure 25 · augmentation 50 · click for rater detail
Inspect work areas or operating equipment to ensure conformance to established standards in areas such as cleanliness or maintenance.
28| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Entertainment and recreation is a fragmented, labor-intensive sector with relatively low digital maturity and slow AI adoption in supervisory functions. Most venues rely on traditional staff-based inspection protocols rather than automated monitoring. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Entertainment and recreation venues are generally low-digitization, physical-service sectors with slow AI adoption for facilities management compared to information or finance sectors. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-powered cameras or checklists could assist supervisors by flagging areas needing attention or documenting conditions, raising efficiency of inspections. However, the human supervisor remains essential for judgment, communication, and enforcement. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-powered checklists, IoT sensors, and image-based defect detection can assist supervisors in flagging issues faster, improving efficiency of inspections while the human still performs final verification. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI vision systems can detect some cleanliness issues or equipment maintenance problems, supervisory inspection requires contextual judgment about standards that vary by venue, season, and operational state. Current vision AI cannot reliably interpret nuanced conformance to 'established standards' without extensive human training data and setup per location, nor replace the supervisory decision-making on enforcement. |
| Task automatability | claude-sonnet-5 | 2/5 | Physical inspection of work areas and equipment requires on-site presence, sensory judgment, and mobility that current AI systems cannot fully replicate without extensive sensor infrastructure.dated:This task involves substantial physical, on-site components that off-the-shelf AI cannot perform end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Supervisory inspections typically require human presence and judgment to enforce standards, communicate with workers, and make contextual decisions about remediation. Liability for missed safety or cleanliness issues creates organizational friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement blocks automation, but liability for safety/maintenance failures, need for human judgment on ambiguous conditions, and customer/staff expectations of a human overseer create moderate friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Vision system hardware, ongoing model training, and human oversight for compliance validation remain costly relative to a first-line supervisor's periodic walk-throughs. Integration into existing venue management systems adds further expense. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Deploying camera/sensor systems plus AI analysis requires significant capital investment and integration costs that often exceed the cost of a supervisor's routine walk-through, especially at smaller venues. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Deployed computer vision can flag obvious debris or damage in controlled environments, but production systems struggle with the contextual interpretation of cleanliness and maintenance standards across diverse entertainment venues. No mature product reliably performs end-to-end supervisory inspection at scale across the sector. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some computer-vision-based monitoring products exist for specific narrow checks (e.g., cleanliness detection via cameras) but they are not broadly deployed for comprehensive facility/equipment inspection in entertainment/recreation settings. |
Train workers in proper operational procedures and functions and explain company policies.
28CI 25–30 · exposure 25 · augmentation 50 · click for rater detail
Train workers in proper operational procedures and functions and explain company policies.
28| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Entertainment and recreation venues are typically small, dispersed operators with low IT sophistication and legacy practices; adoption of formal AI training systems in this sector lags far behind tech and finance firms. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Recreation and entertainment services are a lower-digitization sector with limited AI agent deployment in frontline supervisory training compared to office/professional services. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist supervisors by drafting training scripts, generating visual aids, preparing policy summaries, or providing real-time lookup of procedures, meaningfully raising supervisor productivity while they remain the primary trainer. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help supervisors draft training materials, quizzes, and policy summaries, improving efficiency, while the actual training delivery remains human-led. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can generate training materials and procedural documentation, the task requires real-time interaction, assessment of worker understanding, and adaptive explanation—functions that demand human judgment and responsiveness that current systems cannot reliably replicate end-to-end at 50% time savings. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can generate training materials and explain policies via chatbots, but hands-on demonstration, live coaching, and enforcement of procedures in recreation/entertainment settings require physical presence and adaptive supervision.rated below the 50% end-to-end threshold. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Significant barriers exist: supervisors bear legal and operational responsibility for worker safety and compliance; improper training on procedures can create liability; regulatory requirements in some jurisdictions mandate human-led safety training; and workers generally expect human instruction for critical operational guidance. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement typically, but liability for safety procedures, need for hands-on supervision, and organizational preference for human trainers create moderate friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Building and maintaining a custom AI training system for a specific organization's procedures and policies typically costs more than the supervisor time saved, especially for small to mid-sized entertainment venues where training occurs irregularly and in small cohorts. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Content creation costs drop with AI, but the core task still requires a human trainer's time for demonstration and oversight, keeping overall costs comparable to human-led training. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | AI-powered training systems exist as supplements (e.g., chatbots for Q&A, video generation), but no mature product reliably handles the full scope of live training delivery, policy explanation, and worker assessment without significant human oversight and correction. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | E-learning platforms and AI-generated training content exist, but no deployed product reliably trains staff on operational/physical procedures and policy explanation without a human supervisor present. |
Participate in continuing education to stay abreast of industry trends and developments.
23CI 16–30 · exposure 17 · augmentation 63 · click for rater detail
Participate in continuing education to stay abreast of industry trends and developments.
23| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Entertainment and recreation is a mixed-digitization sector with variable adoption. While some large firms use learning platforms, small venues and independent operators lag significantly. Adoption of AI for professional development remains pilot-stage. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Entertainment and recreation supervisory roles are not high-digitization, fast-AI-adopting sectors; uptake of AI for professional development tracking is slow and uneven. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by summarizing industry news, recommending courses, and curating relevant trends, meaningfully reducing time spent searching and filtering. However, the supervisor retains final judgment on educational value and completion responsibility. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully augment this task by aggregating industry news, generating summaries of trends, and recommending relevant courses or certifications, saving significant research time. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Continuing education and staying informed of industry trends requires human judgment about relevance, critical evaluation of sources, and integration with personal professional development—tasks that current AI cannot perform end-to-end with meaningful time savings at equal quality. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can help surface and summarize industry trends but the task requires the human to actually engage, learn, and participate in continuing education activities, which cannot be fully offloaded.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Professional development and licensure requirements in entertainment and recreation sectors often mandate human participation and completion of recognized programs. Supervisors themselves must engage meaningfully, creating a strong human-contact and authorization requirement. |
| Adoption barriers | claude-sonnet-5 | 3/5 | Some industries require documented continuing education credits or certifications tied to a specific licensed individual, creating moderate procedural and organizational barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI can reduce the cost of information gathering and summarization, but supervisors must invest significant time evaluating, selecting, and engaging with educational content. The all-in cost remains comparable to or higher than simple human browsing and reading. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI tools for information curation are cheap, but the core requirement is human engagement and credentialing, so cost comparisons for full task substitution are not favorable. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While AI can help surface and summarize industry news and trends, no deployed product reliably performs the full task of deciding what education to pursue and actually completing it for a human supervisor. AI tools may assist with information gathering but not the participation itself. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Products like AI-curated newsletters, summarization tools, and learning platforms exist but no deployed system independently 'participates' in continuing education on a supervisor's behalf. |
Resolve customer complaints regarding worker performance or services rendered.
23CI 16–30 · exposure 17 · augmentation 63 · click for rater detail
Resolve customer complaints regarding worker performance or services rendered.
23| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Entertainment and recreation sectors remain fragmented, labor-intensive, and relatively low-digitization. While larger operators may pilot AI triage tools, production adoption of complaint resolution automation is minimal and progresses slowly in this sector. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Entertainment and recreation is a service sector with lower digitization and more physical/in-person interaction, showing slower AI adoption compared to information or finance sectors. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist supervisors by summarizing complaints, suggesting precedents, flagging key issues, and drafting responses. These augmentations improve efficiency and consistency, though the supervisor retains full decision authority and must evaluate each case individually. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully assist supervisors by summarizing complaints, suggesting resolution scripts, tracking patterns in worker performance issues, and drafting communications, significantly speeding up the process while the supervisor retains final judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Resolving customer complaints requires understanding context, human judgment about interpersonal dynamics, and nuanced decision-making about fairness and service recovery. Current AI systems lack the situational judgment and accountability-bearing capacity to autonomously handle complaint resolution at equal quality. |
| Task automatability | claude-sonnet-5 | 2/5 | This task requires interpersonal de-escalation, judgment calls on remedies (refunds, discipline, policy exceptions), and often in-person presence, which current AI cannot fully replicate end-to-end despite being able to draft responses or triage tickets. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Supervisory authority and accountability for complaint resolution typically rest with a human manager who must sign off on outcomes. Customer expectations and organizational policy usually require human judgment and accountability, creating high friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement, but organizational policy, liability concerns (e.g., disciplinary action, refunds affecting revenue), and customer expectation of human accountability create moderate friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI-assisted triage and draft generation cost little, but the human supervisor must still investigate, evaluate, and make the final decision. The human remains essential, limiting cost savings to perhaps 20-30% of task time, insufficient for strong cost advantage. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI can cheaply draft or triage complaint responses, but final resolution still requires a human supervisor's judgment, oversight, and often direct interaction, keeping overall cost savings modest. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While AI can triage complaints, extract information, and suggest responses, no deployed system reliably resolves actual customer complaints end-to-end. Products may assist with drafting responses but cannot independently own the outcome or accountability. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Chatbots and case-management AI exist for routing and drafting complaint responses in some industries, but no deployed product autonomously resolves worker-performance complaints in entertainment/recreation settings reliably today. |
Observe and evaluate workers' appearance and performance to ensure quality service and compliance with specifications.
18CI 7–29 · exposure 13 · augmentation 38 · click for rater detail
Observe and evaluate workers' appearance and performance to ensure quality service and compliance with specifications.
18| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Entertainment and recreation sectors tend to be fragmented, often small to medium operations with lower digitization and slower technology adoption. While large venues use some security cameras, systematic AI-driven performance evaluation remains uncommon compared to finance or information services. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Recreation and entertainment services are generally slower-adopting, physical, service-oriented sectors with limited AI integration into direct supervisory observation tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist supervisors by flagging appearance violations or attendance anomalies via video analysis, creating performance logs, and highlighting out-of-specification moments for human review. This reduces manual observation burden, but the supervisor's judgment remains essential for contextual evaluation and employee coaching. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help with peripheral tasks like compiling performance logs or flagging complaints, but offers minimal assistance to the core act of in-person observation and judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Current AI can detect some appearance and performance metrics (e.g., uniform compliance via computer vision, some behavioral patterns via video), but holistic evaluation of service quality and specification compliance requires contextual judgment that AI systems struggle with today. Significant gaps remain in understanding nuanced performance indicators and customer interaction quality. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires in-person physical observation of workers' appearance, demeanor, and real-time performance in recreational/entertainment settings, which current AI cannot autonomously execute end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Significant barriers exist: worker privacy concerns and potential legal restrictions on surveillance, union opposition in unionized entertainment venues, customer perception that human supervision is part of service quality, and liability risk if automated systems miss compliance failures. Supervisory judgment also carries accountability that organizations are reluctant to delegate fully to AI. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Organizational accountability, liability for service quality/safety, and the inherently human nature of judging appearance and interpersonal performance create strong practical barriers to automation, though not formal licensing requirements. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 3/5 | Camera systems and basic computer vision are moderately priced, but meaningful oversight integration and the need for human review of flagged incidents keep total cost roughly comparable to employing a supervisor, especially when accounting for false positives and missed subtle performance issues. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this in-person evaluative task, so cost comparison favors the human supervisor by default. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While video monitoring and basic computer vision systems exist in production, they typically flag only obvious deviations (missing uniform items, absence from station). Reliable, end-to-end evaluation of service quality and performance against complex specifications remains largely in pilot or research stages rather than mature deployment. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs live supervisory observation and evaluation of frontline entertainment/recreation staff; this remains a human managerial function. |
Direct or coordinate the activities of entertainment and recreation related workers.
18CI 5–30 · exposure 13 · augmentation 50 · click for rater detail
Direct or coordinate the activities of entertainment and recreation related workers.
18| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Entertainment and recreation sectors are typically smaller, more fragmented, and less digitized than finance or tech. Adoption of AI coordination tools remains in pilot phase, with most venues still relying on traditional human-led management structures. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Recreation and entertainment sectors are largely physical, low-digitization environments with minimal AI agent adoption for frontline supervisory roles. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-powered scheduling, worker-performance dashboards, and automated task allocation can meaningfully assist supervisors in organizing workflows and tracking metrics, allowing them to focus on mentorship and problem-solving rather than administrative overhead. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI scheduling tools, communication platforms, and task-tracking software can help supervisors coordinate shifts and staff assignments, offering moderate productivity gains without replacing the supervisory judgment involved. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Directing and coordinating workers requires real-time human judgment, interpersonal communication, and dynamic decision-making in response to complex social situations. While AI can assist with scheduling and task assignment, end-to-end supervision—handling conflicts, mentoring, and adaptive resource allocation—remains heavily dependent on human presence and accountability. |
| Task automatability | claude-sonnet-5 | 1/5 | Directing and coordinating staff in real-time recreational/entertainment settings requires physical presence, situational judgment, and interpersonal leadership that current AI cannot replicate end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Supervisory roles typically carry legal accountability for worker safety, labor compliance, and duty of care. Many jurisdictions and organizational policies require a licensed or designated human supervisor to be responsible for worker activities and to sign off on schedules and incident reports. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No formal licensing requirement typically exists, but organizational structure, liability for staff safety, and the need for on-site human authority create moderate friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Supervisory oversight tools (scheduling, monitoring software) are relatively inexpensive, but the marginal cost savings from partial automation do not offset the full loaded wage of a supervisor; human oversight remains necessary and cost-prohibitive to replace fully. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this supervisory function, so cost comparison favors the human by default. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No production systems today reliably perform supervisory coordination end-to-end. AI tools exist for scheduling and task tracking, but deployed products do not autonomously handle the judgment calls, personnel management, and situational responsiveness that supervision demands. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product manages live supervision and coordination of recreation/entertainment staff; this remains firmly in the human management domain. |
Plan, direct, or supervise recreational and entertainment activities led by staff, such as sports, aquatics, games, or performing arts.
15CI 5–25 · exposure 13 · augmentation 38 · click for rater detail
Plan, direct, or supervise recreational and entertainment activities led by staff, such as sports, aquatics, games, or performing arts.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Recreation and entertainment sectors show slow AI adoption overall; these are often small, locally-operated businesses with limited digitization, low tech budgets, and strong cultural preference for human leadership and in-person oversight of activities. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Recreation and entertainment services are a low-digitization, physically grounded sector with minimal AI agent deployment for on-site supervisory work. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI could meaningfully assist with scheduling optimization, staff roster management, activity planning templates, and performance analytics, allowing supervisors to focus on real-time management and staff development. However, augmentation is partial rather than transformative. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can help with scheduling, staff rosters, or activity planning templates, but offers little assistance for the core in-person directing and supervising activity. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Planning and directing recreational activities requires real-time human oversight, staff coordination, participant safety management, and dynamic decision-making that current AI cannot reliably perform end-to-end. While AI could assist with scheduling or content recommendations, the core supervisory and directive functions remain dependent on human judgment and on-site presence. |
| Task automatability | claude-sonnet-5 | 1/5 | Planning, directing, and live supervision of staff running physical activities requires real-time presence, judgment, and interpersonal leadership that current AI cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Significant barriers exist: liability and duty-of-care requirements for participant safety, organizational expectations that supervisors be physically present, and potential regulatory requirements in youth-serving or aquatics contexts. Many facilities and jurisdictions require credentialed human supervision for legal and insurance reasons. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Safety oversight (e.g., aquatics, sports) often requires certified, present staff, and liability/insurance concerns create strong barriers to removing human supervisors. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The cost of AI systems capable of real-time supervision, monitoring, and coordination would be substantial relative to the operational salary of first-line supervisors, especially considering the need for human oversight of any automation to ensure safety and quality. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this supervisory function, so no meaningful cost comparison favors AI; a human must be present and paid regardless. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed products reliably perform full supervisory direction of live recreational activities. AI tools exist for scheduling and planning, but actual supervision—managing staff performance, ensuring participant safety, making real-time adjustments—requires human presence and accountability that current systems cannot replace. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product supervises entertainment/recreation staff or directs live activities; this remains firmly outside current AI product scope. |
Provide staff with assistance in performing difficult or complicated duties.
15CI 5–25 · exposure 8 · augmentation 50 · click for rater detail
Provide staff with assistance in performing difficult or complicated duties.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Entertainment and recreation is a relatively low-digitization sector with small, distributed teams and high employee turnover. Adoption of AI agents for supervisory functions remains rare; most organizations continue relying on human floor supervisors rather than experimenting with automated guidance systems. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Recreation and entertainment supervisory work is a low-digitization, physically-present sector with minimal AI agent adoption for direct staff assistance. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI could usefully augment a supervisor by providing reference material, checklists, or past-case knowledge retrieval, but the task fundamentally requires human judgment, relationship-building, and accountability. Moderate augmentation potential exists through decision-support tools without transforming the core supervisory role. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI tools (checklists, troubleshooting guides, chat-based knowledge assistants) can help supervisors quickly find answers or procedures to relay to staff, offering moderate assistance. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | This task requires real-time judgment, contextual understanding of staff capabilities, and adaptive problem-solving in dynamic entertainment settings. While AI could generate generic guidance documents, it cannot reliably assess individual staff competencies, provide situational coaching, or handle the unpredictable nature of 'difficult or complicated duties' without human oversight. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires real-time, situational, hands-on problem-solving with staff and physical/interpersonal context that AI cannot execute end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Supervisory duties carry implicit liability and safety responsibility: a supervisor must be present, accountable, and capable of judgment. Regulatory and organizational norms expect a licensed/trained human to hold accountability for staff support, making legal substitution difficult even if AI could perform technical aspects. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement per se, but organizational structure, liability for staff safety, and expectation of hands-on human leadership create moderate friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | An AI assistant might handle some documentation or basic troubleshooting at low cost, but the core value—experienced judgment and real-time guidance calibrated to staff and situation—remains expensive to replicate. Full automation would require human oversight anyway, negating cost advantage. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this supervisory assistance role, so cost comparison favors the human by default. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product reliably performs live supervisory assistance with the contextual judgment and accountability this role demands. Chatbots can offer scripted help, but they cannot substitute for a supervisor who understands their specific team's skill gaps, organizational constraints, and the nuanced demands of recreation/entertainment work. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product supervises or physically/managerially assists recreation staff through complex duties in real time; this remains a human supervisory function. |
Serve as a point of contact between managerial staff and leaders of recreational or entertainment activities.
15CI 5–25 · exposure 8 · augmentation 38 · click for rater detail
Serve as a point of contact between managerial staff and leaders of recreational or entertainment activities.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Recreation and entertainment organizations, particularly those with these front-line supervisory roles, operate in labor-intensive, relationship-dependent sectors with low digital maturity. Adoption of AI in core supervisory and liaison functions remains negligible in these industries. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Recreation and entertainment services are a lower-digitization sector with limited AI adoption for interpersonal supervisory liaison functions compared to fast-adopting professional services. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could provide minor assistance in scheduling, message drafting, or data aggregation for reports, but the core communicative and judgment elements of being a liaison cannot be meaningfully augmented by current systems without the human supervisor doing most of the real work anyway. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI tools (scheduling apps, messaging summarization, communication drafting) can assist supervisors in tracking communications and drafting updates, improving efficiency without replacing the human liaison role. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task is fundamentally about interpersonal liaison and relationship management between hierarchical levels, requiring real-time judgment, contextual negotiation, and human trust-building. Current AI systems cannot reliably substitute for the human credibility and discretionary decision-making required to mediate between management and activity leaders. |
| Task automatability | claude-sonnet-5 | 2/5 | This task involves relaying information, negotiating priorities, and coordinating between two human groups, which requires interpersonal judgment and situational awareness that current AI cannot fully replicate end-to-end.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong organizational and structural barriers exist: the role requires accountability, on-the-spot judgment in disputes, and trust from both managers and staff. Any attempt to automate would face significant friction from the need for a human decision-maker to remain accountable for final judgments and employee relations. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement exists, but organizational trust, accountability for decisions, and the need for a human authority figure in staff relations create moderate friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of implementing an AI intermediary, including integration, ongoing oversight, and correction of misunderstandings, would far exceed the wage cost of a single supervisor, since trust and accountability cannot be outsourced at lower cost than human employment in this context. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While AI could cheaply handle scheduling or messaging subcomponents, the full coordination and trust-building role still requires human oversight, keeping all-in costs comparable to or higher than a human supervisor for this function. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI system currently performs this bridging role independently; it requires understanding organizational dynamics, managing conflicting priorities, and maintaining trusted relationships—capabilities that exist only in research or narrow roleplay scenarios, not in production systems managing real teams. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product acts as an autonomous liaison between management and activity leaders in entertainment/recreation settings; this remains a human relationship-management role. |
Meet with managers or other supervisors to stay informed of changes affecting workers or operations.
12CI 7–16 · exposure 0 · augmentation 50 · click for rater detail
Meet with managers or other supervisors to stay informed of changes affecting workers or operations.
12| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Supervisory meeting practices in entertainment and recreation remain highly traditional and human-centric. While some organizations use meeting-assist tools (transcription, note-taking), there is minimal adoption of AI to *replace* the supervisor's attendance or participation in these meetings. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Recreation and entertainment supervisory roles are in a physically-oriented, lower-digitization sector where AI adoption for interpersonal coordination tasks remains minimal. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist supervisors by transcribing meetings, summarizing key changes, flagging action items, or generating briefing notes for their team—thus raising meeting efficiency and information dissemination. However, the core task (attending and engaging in dialogue) remains human-performed. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI tools like meeting transcription, summarization, and scheduling assistants can meaningfully support preparation and follow-up, though the core exchange of information remains human-driven. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task fundamentally requires real-time interpersonal communication and contextual awareness of organizational changes. Current AI cannot autonomously participate in manager meetings, process nuanced organizational context, or make decisions about which information affects which workers—this requires human judgment and presence. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires in-person or synchronous interpersonal coordination, relationship management, and situational judgment that AI cannot substitute for; it is fundamentally a human coordination activity, not an information-processing task alone. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong barriers exist because this task requires direct human presence and synchronous communication with peers and managers. Organizational culture, the need for real-time problem-solving discussion, and accountability for operational decisions all create friction against full automation or substitution. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement exists, but organizational norms and the inherent need for real-time human interaction and accountability create moderate friction against any automation of the meeting itself. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI tools like transcription and summarization can reduce some meeting preparation/documentation work, but a human supervisor must still attend and engage. The cost savings are marginal compared to the supervisor's loaded wage, as the human remains the irreplaceable participant. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this interpersonal task, so no meaningful cost comparison favors AI; the human must attend and engage regardless. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI system can reliably attend, participate in, or substitute for supervisory meetings with managers. While AI can summarize documents or draft notes from transcripts, it cannot perform the actual meeting and information exchange that defines this task. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product conducts managerial coordination meetings on a supervisor's behalf; AI can only support note-taking or summarization around the meeting, not perform the meeting itself. |
Take disciplinary action to address performance problems.
4CI 0–7 · exposure 0 · augmentation 38 · click for rater detail
Take disciplinary action to address performance problems.
4| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Entertainment and recreation sectors show low overall digitization and adoption of advanced automation. Disciplinary processes remain firmly within human management purview across these industries, with minimal AI integration. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Recreation and entertainment supervision is a low-digitization, service-oriented sector with limited AI adoption for people-management tasks like discipline. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist by drafting documentation templates or summarizing performance records, but the core task—exercising judgment, conducting the conversation, and making the decision—remains inherently supervisory and offers limited room for meaningful AI assistance. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help draft performance improvement plans, documentation, and language for difficult conversations, moderately aiding supervisors while they retain full responsibility for delivery and decision-making. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Taking disciplinary action requires contextual judgment about employee performance, organizational policy interpretation, and interpersonal calibration. Current AI systems cannot reliably conduct the investigation, weigh mitigating factors, or execute the sensitive human interaction that disciplinary conversations demand. |
| Task automatability | claude-sonnet-5 | 1/5 | Disciplinary action requires nuanced human judgment, contextual understanding of employee circumstances, and legally sensitive interpersonal communication that current AI cannot execute end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Disciplinary action is a core supervisory and managerial function that carries legal liability, employment law compliance requirements, and documented record-keeping obligations. A human supervisor must legally authorize and execute disciplinary decisions. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Disciplinary actions carry legal, HR compliance, and liability implications (wrongful termination, labor law) that require a human supervisor to authorize and deliver, creating strong organizational and legal barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI cannot perform this task end-to-end, so direct cost comparison is not meaningful. Any attempt to automate would require substantial human oversight, making it more expensive than the supervisor performing the task directly. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this task alone, so cost comparison favors the human, whose judgment and authority are required regardless of AI tools used for support. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs disciplinary action autonomously. This task requires legal compliance, documented evidence review, and documented delivery—all of which require human supervisory authority and accountability that cannot be delegated to AI systems today. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product independently conducts disciplinary actions with employees; at most AI assists with drafting documentation, but the actual managerial act remains human-performed. |
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How to read this
A high substitution score does not mean this job disappears — it means a large share of its current tasks face replacement pressure, so the mix of tasks is likely to change. High augmentation alongside substitution typically means the occupation reorganizes around the protected tasks. Wide confidence intervals mean the rater panel disagreed: treat those scores as open questions, not verdicts.
What would change this score
New model capabilities (automatability, feasibility), falling inference costs (cost ratio), regulation and licensing shifts (barriers), and measured sector adoption (velocity) all re-enter at every index release. Each release is recomputed, versioned and kept queryable — scores are claims with a date on them, not permanent labels.