1.1. Insect Production Scientist as an Occupation

An insect production scientist designs, manages, and optimizes mass-rearing systems to generate scalable biological outputs for agricultural, nutritional, and industrial applications. This occupation connects biological sciences with industrial engineering by transforming insect life cycles, nutritional requirements, and environmental constraints into efficient and sustainable production processes. To ensure these systems operate effectively, the scientist must understand how biological questions, environmental data, rearing methods, quality measures, and regulatory requirements collectively determine the viability and usefulness of the production output.

Typical responsibilities of an insect production scientist include:

• insect colony maintenance and breeding optimization;
• diet formulation and nutritional analysis;
• environmental control and habitat monitoring;
• health management and disease prevention;
• data collection, phenotyping, and genetic tracking;
• production scaling and process automation;
• quality assurance and regulatory compliance; and
• harvesting, processing, and downstream application research.

These responsibilities are fulfilled through distinct tasks and subtasks, each requiring one or more actions with specific skill requirements. Human skills and agentic skills both contribute to these responsibilities, but their levels and forms of involvement differ according to the responsibility, task, action, data, biological context, technical environment, and expected outcome.

1.2. Human Skills and Agentic Skills in Insect Production

Insect production increasingly integrates both human skills and agentic skills.

Human skills are reusable capabilities possessed and developed by insect production scientists. They include biological understanding, ecological reasoning, experimental design, nutritional science, problem formulation, critical thinking, contextual interpretation, welfare assessment, and professional judgment.

Agentic skills are reusable capabilities available to AI agents through their underlying models, contextual information, instructions, constraints, tools, and computational resources. In insect production, these skills include environmental data analysis, predictive modeling, image recognition, anomaly detection, literature retrieval, formulation calculation, simulation generation, report generation, and automation logic support.

Both skill types contribute to the same occupational responsibilities, but their involvement varies by action. Understanding a loosely defined biological anomaly relies primarily on human knowledge of insect physiology and ecological stressors; an AI agent then uses agentic skills to retrieve environmental logs and calculate correlations that support the scientist’s interpretation. In diet formulation, the scientist first determines appropriate nutritional targets, and an AI agent subsequently searches literature databases, calculates macronutrient balances, and proposes alternative feedstocks. For production scaling, an AI agent simulates mechanical handling parameters, while the scientist interprets the output to ensure it preserves biological integrity and animal welfare. Across these actions, human and agentic skills are activated sequentially or iteratively based on task requirements, with the human professional maintaining oversight and final judgment.

The relationship between these skills is not a fixed division where certain responsibilities permanently belong to humans and others to AI. Instead, human and agentic involvement shifts across tasks and between different actions within the same task.

1.1. Logistics Analyst as an Occupation

A logistics analyst analyzes and optimizes supply chain and logistics operations to reduce costs, improve efficiency, and ensure reliable delivery performance. This occupation supports the movement of goods across suppliers, manufacturing nodes, warehousing facilities, and end customers by analyzing transportation routes, inventory levels, distribution networks, and operational workflows that support scalability, resilience, and cost-effectiveness. To ensure logistics strategies support long-term organizational objectives, the analyst must understand how demand variability, carrier capacity, warehouse constraints, fuel costs, geopolitical factors, and service-level agreements collectively shape the supply chain landscape.

Typical responsibilities of a logistics analyst include:

• supply chain network and route optimization;
• inventory and warehouse operations analysis;
• transportation and freight management;
• demand forecasting and capacity planning;
• supplier and vendor performance evaluation;
• logistics cost analysis and financial reporting;
• risk management and supply chain resilience; and
• process improvement and logistics system integration.

These responsibilities are fulfilled through distinct tasks and subtasks, each requiring one or more actions with specific skill requirements. Human skills and agentic skills both contribute to these responsibilities, but their levels and forms of involvement differ according to the responsibility, task, action, network scope, operational context, system complexity, technical environment, and expected outcome.

1.2. Human Skills and Agentic Skills in Logistics Analysis

Logistics analysis increasingly integrates both human skills and agentic skills.

Human skills are reusable capabilities possessed and developed by logistics analysts. They include systems thinking, operational reasoning, spatial analysis, supplier negotiation, risk assessment, process design, cost-benefit evaluation, stakeholder communication, facilitation, contextual interpretation, and professional judgment.

Agentic skills are reusable capabilities available to AI agents through their underlying models, contextual information, instructions, constraints, tools, and computational resources. In logistics analysis, these skills include information retrieval, route calculation, inventory data extraction, freight rate comparison, demand pattern recognition, anomaly detection, schedule generation, cost aggregation, scenario simulation, and tool use.

Both skill types contribute to the same occupational responsibilities, but their involvement varies by action. Optimizing a distribution network relies primarily on human understanding of service-level agreements and operational constraints; an AI agent then uses agentic skills to calculate routing alternatives and extract historical transit times. In inventory analysis, the analyst first establishes the service-level targets, and an AI agent subsequently classifies stock-keeping units (SKUs), maps warehouse capacities, and identifies stockout risks. For freight auditing, an AI agent compares invoiced rates against contracted rates, while the analyst interprets systemic discrepancies, evaluates carrier relationships, and determines the appropriate operational response. Across these actions, human and agentic skills are activated sequentially or iteratively based on task requirements, with the human professional maintaining oversight and final judgment.

The relationship between these skills is not a fixed division where certain responsibilities permanently belong to humans and others to AI. Instead, human and agentic involvement shifts across tasks and between different actions within the same task.

1.1. Presentation Designer as an Occupation

A presentation designer transforms complex information, strategies, and data into compelling visual narratives, slide decks, and multimedia presentations. This occupation connects raw organizational knowledge with visual communication by structuring arguments, designing layouts, and applying typographic and graphical elements to support persuasion, education, and decision-making. To ensure these presentations achieve their intended impact, the designer must understand how audience psychology, narrative flow, brand guidelines, data visualization principles, and accessibility standards collectively determine the effectiveness of the final output.

Typical responsibilities of a presentation designer include:

• audience analysis and narrative strategy;
• content structuring and storyboarding;
• visual design and layout generation;
• data visualization and information design;
• animation and multimedia integration;
• template and brand asset management;
• quality assurance and accessibility compliance; and
• stakeholder review and iteration management.

These responsibilities are fulfilled through distinct tasks and subtasks, each requiring one or more actions with specific skill requirements. Human skills and agentic skills both contribute to these responsibilities, but their levels and forms of involvement differ according to the responsibility, task, action, content type, brand context, technical environment, and expected outcome.

1.2. Human Skills and Agentic Skills in Presentation Design

Presentation design increasingly integrates both human skills and agentic skills.

Human skills are reusable capabilities possessed and developed by presentation designers. They include narrative strategy, audience empathy, visual hierarchy, spatial reasoning, typographic judgment, brand interpretation, color theory, data storytelling, stakeholder communication, and aesthetic evaluation.

Agentic skills are reusable capabilities available to AI agents through their underlying models, contextual information, instructions, constraints, tools, and computational resources. In presentation design, these skills include text summarization, layout generation, image sourcing, asset resizing, color palette extraction, chart generation, accessibility scanning, template application, text formatting, and file conversion.

Both skill types contribute to the same occupational responsibilities, but their involvement varies by action. Understanding a client’s core message relies primarily on human empathy and strategic reasoning; an AI agent then uses agentic skills to summarize background research and extract key themes that support the designer’s narrative. In visual design, the designer first establishes the brand guidelines and visual hierarchy, and an AI agent subsequently generates initial layout variations and sources relevant imagery. For final delivery, an AI agent resizes and exports assets for multiple formats, while the designer reviews the final outputs to ensure brand consistency and emotional impact. Across these actions, human and agentic skills are activated sequentially or iteratively based on task requirements, with the human professional maintaining oversight and final judgment.

The relationship between these skills is not a fixed division where certain responsibilities permanently belong to humans and others to AI. Instead, human and agentic involvement shifts across tasks and between different actions within the same task.

1.1. Search Marketing Strategist as an Occupation

A search marketing strategist develops, implements, and optimizes search engine marketing (SEM) and search engine optimization (SEO) strategies to increase online visibility, drive targeted traffic, and maximize return on investment. This occupation connects organizational objectives with how people search by defining campaign structures, optimizing digital assets, and analyzing search behavior and performance across paid and organic channels. To ensure search strategies support long-term organizational objectives, the strategist must understand how search engine systems, user intent, competitive conditions, technical site infrastructure, and budget constraints collectively affect search visibility, campaign performance, and business outcomes.

Typical responsibilities of a search marketing strategist include:

• search strategy and campaign architecture;
• keyword research and semantic mapping;
• technical SEO and site audit management;
• content optimization and search intent alignment;
• paid search (PPC) bidding and budget allocation;
• competitor analysis and market positioning;
• performance analytics and attribution modeling; and
• algorithm adaptation and search trend forecasting.

These responsibilities are fulfilled through distinct tasks and subtasks, each requiring one or more actions with specific skill requirements. Human skills and agentic skills both contribute to these responsibilities, but their levels and forms of involvement differ according to the responsibility, task, action, campaign scope, market context, algorithm complexity, technical environment, and expected outcome.

1.2. Human Skills and Agentic Skills in Search Marketing

Search marketing increasingly integrates both human skills and agentic skills.

Human skills are reusable capabilities possessed and developed by search marketing strategists. They include strategic reasoning, search intent interpretation, audience understanding, quantitative reasoning, analytical interpretation, experimentation, technical reasoning, brand judgment, stakeholder communication, contextual reasoning, and professional judgment.

Agentic skills are reusable capabilities available to AI agents through their underlying models, contextual information, instructions, constraints, tools, and computational resources. In search marketing, these skills include data extraction, keyword clustering, bid calculation, site crawling, anomaly detection, trend summarization, report generation, A/B test simulation, code snippet generation, and tool use.

Both skill types contribute to the same occupational responsibilities, but their involvement varies by action. Defining a search campaign’s strategic direction relies primarily on human understanding of brand positioning and consumer behavior; an AI agent then uses agentic skills to retrieve historical search volume data and organize competitive benchmarks. In technical SEO, the strategist first defines the crawl parameters and business priorities, and an AI agent subsequently crawls the site infrastructure, identifies indexing errors, and maps redirect chains. For performance reporting, an AI agent aggregates cross-channel attribution data, while the strategist interprets the business impact, evaluates assisted conversions, and determines the appropriate budget reallocation. Across these actions, human and agentic skills are activated sequentially or iteratively based on task requirements, with the human professional maintaining oversight and final judgment.

The relationship between these skills is not a fixed division where certain responsibilities permanently belong to humans and others to AI. Instead, human and agentic involvement shifts across tasks and between different actions within the same task.

1.1. UX/UI Designer as an Occupation

A UX/UI designer designs and optimizes digital interfaces and user experiences by conducting user research, creating wireframes and prototypes, designing visual elements, and conducting usability testing. This occupation connects user needs with technical implementation by defining interaction models, information architectures, visual languages, and usability standards that support intuitiveness, accessibility, engagement, and operational effectiveness. To ensure design decisions support long-term user and organizational objectives, the designer must understand how user psychology, business goals, technical constraints, accessibility standards, platform guidelines, and behavioral data collectively shape the digital product landscape.

Typical responsibilities of a UX/UI designer include:

• user research and persona development;
• information architecture and user flow design;
• wireframing and interactive prototyping;
• visual design and design system management;
• usability testing and heuristic evaluation;
• accessibility and inclusive design compliance;
• developer handoff and implementation quality assurance; and
• product strategy and experience metrics analysis.

These responsibilities are fulfilled through distinct tasks and subtasks, each requiring one or more actions with specific skill requirements. Human skills and agentic skills both contribute to these responsibilities, but their levels and forms of involvement differ according to the responsibility, task, action, design scope, organizational context, system complexity, technical environment, and expected outcome.

1.2. Human Skills and Agentic Skills in UX/UI Design

UX/UI design increasingly integrates both human skills and agentic skills.

Human skills are reusable capabilities possessed and developed by UX/UI designers. They include user empathy, user research, interaction design, visual reasoning, systems thinking, accessibility reasoning, strategic alignment, contextual interpretation, facilitation, negotiation, and professional judgment.

Agentic skills are reusable capabilities available to AI agents through their underlying models, contextual information, instructions, constraints, tools, and computational resources. In UX/UI design, these skills include information retrieval, behavioral data analysis, heatmap generation, wireframe generation, component library scanning, contrast checking, code extraction, usability metric aggregation, scenario simulation, and tool use.

Both skill types contribute to the same occupational responsibilities, but their involvement varies by action. Defining the strategic direction of a digital product relies primarily on human understanding of user psychology and business objectives; an AI agent then uses agentic skills to retrieve, organize, and compare relevant behavioral data and market benchmarks. In interaction design, the designer first establishes the core user flows, and an AI agent subsequently generates wireframe variations and maps edge-case states. For design system maintenance, an AI agent scans existing component libraries for visual inconsistencies, while the designer interprets the deviations, evaluates brand alignment, and determines the appropriate structural response. Across these actions, human and agentic skills are activated sequentially or iteratively based on task requirements, with the human professional maintaining oversight and final judgment.

The relationship between these skills is not a fixed division where certain responsibilities permanently belong to humans and others to AI. Instead, human and agentic involvement shifts across tasks and between different actions within the same task.

1.1. Budget Analyst as an Occupation

A budget analyst is a professional who develops, analyzes, reviews, monitors, and communicates budgets to support the effective allocation and control of financial resources. The occupation connects financial analysis with organizational planning, resource allocation, expenditure control, program requirements, management decisions, and applicable financial policies and procedures.

Budget analysis extends beyond preparing numerical budgets. A budget analyst must understand how organizational objectives, program requirements, historical expenditures, revenue and funding constraints, accounting information, financial assumptions, management priorities, and regulatory requirements affect the planning and use of financial resources.

Typical responsibilities of a budget analyst include:

• budget planning, preparation, and forecasting;
• budget and financial analysis;
• budget review, accuracy verification, and compliance;
• expenditure monitoring and budgetary control;
• resource allocation and funding recommendations;
• budget adjustments and financial coordination;
• cost-benefit analysis and operational efficiency; and
• budget reporting, communication, and advisory support.

These responsibilities are fulfilled through different tasks and subtasks, and each task may require multiple actions with different skill requirements. Human skills and agentic skills can both contribute to performing these responsibilities, but their levels and forms of involvement may differ according to the characteristics of the responsibility, financial information, organizational context, applicable requirements, consequences of decisions, and expected outcome.

1.2. Human Skills and Agentic Skills in Budget Analysis

Budget analysis increasingly involves both human skills and agentic skills.

Human skills are reusable capabilities possessed and developed by budget analysts. They may include financial reasoning, accounting knowledge, quantitative analysis, budget planning, organizational understanding, regulatory interpretation, communication, negotiation, contextual interpretation, problem solving, and professional judgment.

Agentic skills are reusable capabilities available to AI agents through their underlying models, contextual information, instructions, constraints, tools, and computational resources. In budget analysis, these skills may include information retrieval, financial data extraction, classification, calculation, summarization, comparison, variance analysis, trend analysis, scenario generation, document generation, consistency checking, report preparation, and tool use.

Both skill types can contribute to the same occupational responsibilities, but their involvement levels vary. Budget calculations and repeated variance analyses may involve high agentic participation because the underlying actions are systematic and data-intensive. Interpretation of an unusual spending pattern may require greater human involvement because its significance depends on program activities and organizational context. Funding recommendations may combine extensive agentic analysis with strong human judgment because the analyst must consider not only numerical results but also priorities, constraints, risks, and consequences.

The useful relationship is therefore not a fixed division in which certain responsibilities permanently belong to humans and others belong to AI. Human and agentic involvement can change from one task to another and even between different actions within the same task.

1.1. Business Execution Consultant as an Occupation

A business execution consultant is a professional who helps organizations translate business objectives, strategies, and improvement opportunities into coordinated initiatives, operational changes, requirements, and measurable outcomes. The occupation connects strategic planning with business analysis, process improvement, implementation, risk management, performance measurement, stakeholder coordination, and organizational change.

Business execution consulting extends beyond planning projects or documenting business requirements. A business execution consultant must understand how organizational objectives, business processes, operating procedures, technology, stakeholder needs, risks, policies, performance measures, dependencies, and implementation constraints interact. The consultant analyzes business challenges, develops executable approaches, coordinates activities across organizational functions, monitors progress, and helps ensure that intended business outcomes are realized.

Typical responsibilities of a business execution consultant include:

• strategic planning and business initiative execution;

• business problem analysis and requirements definition;

• process improvement and root-cause analysis;

• implementation, change, and project lifecycle coordination;

• risk, compliance, governance, and execution control;

• business performance measurement and outcome realization; and

• stakeholder coordination, communication, and user enablement.

These responsibilities are fulfilled through different tasks and subtasks, and each task may require multiple actions with different skill requirements. Human skills and agentic skills can both contribute to performing these responsibilities, but their levels and forms of involvement may differ according to the complexity of the initiative, task ambiguity, stakeholder diversity, information quality, risk exposure, technical dependencies, organizational consequences, and expected outcome.

1.2. Human Skills and Agentic Skills in Business Execution Consulting

Business execution consulting increasingly involves both human skills and agentic skills.

Human skills are reusable capabilities possessed and developed by business execution consultants. They may include strategic reasoning, business analysis, requirements elicitation, process analysis, root-cause reasoning, project coordination, risk judgment, stakeholder management, facilitation, negotiation, organizational understanding, communication, problem solving, and professional judgment.

Agentic skills are reusable capabilities available to AI agents through their underlying models, contextual information, instructions, constraints, tools, and computational resources. In business execution consulting, these skills may include information retrieval, document extraction, summarization, requirements drafting, process comparison, data analysis, metric calculation, root-cause support, scenario analysis, risk identification, status reporting, issue tracking, document generation, and tool use.

Both skill types can contribute to the same occupational responsibilities, but their involvement levels vary. Strategic planning may depend strongly on human understanding of organizational priorities while using agentic skills to organize evidence and compare alternatives. Requirements work may involve substantial agentic drafting and consistency checking while relying on human skills for elicitation and interpretation. Performance monitoring may rely heavily on agentic analysis of metrics while requiring human judgment to determine whether results indicate a real business problem or require corrective action.

The useful relationship is therefore not a fixed division in which certain responsibilities permanently belong to humans and others belong to AI. Human and agentic involvement can change from one task to another and even between different actions within the same task.

1.1. Card Access Specialist as an Occupation

A card access specialist is a professional who administers physical access credentials and related access-control processes to help ensure that authorized individuals receive appropriate, accurate, and secure access to organizational facilities and controlled areas. The occupation connects identity verification, badge and credential production, physical access provisioning, operational security procedures, record management, equipment use, and coordination with employees, contractors, security teams, facilities personnel, and other organizational functions.

Card access work extends beyond printing identification cards. A card access specialist must understand how identity information, authorization, access levels, credential status, physical security procedures, access-control systems, badge production processes, confidentiality requirements, and operational service expectations interact throughout the credential lifecycle.

Typical responsibilities of a card access specialist include:

• access request review and identity and authorization validation;
• credential production and personalization;
• physical access provisioning and credential lifecycle administration;
• credential quality control, security compliance, and information protection;
• credential distribution and access-service operations;
• access records, inventory, and operational documentation; and
• access issue resolution, escalation, and stakeholder coordination.

These responsibilities are fulfilled through different tasks and subtasks, and each task may require multiple actions with different skill requirements. Human skills and agentic skills can both contribute to performing these responsibilities, but their levels and forms of involvement may differ according to security requirements, request complexity, data quality, access consequences, procedural clarity, available systems, workload volume, and expected outcome.

1.2. Human Skills and Agentic Skills in Card Access Operations

Card access operations increasingly involve both human skills and agentic skills.

Human skills are reusable capabilities possessed and developed by card access specialists. They may include security awareness, access-control knowledge, attention to detail, identity verification, procedural judgment, equipment operation, exception handling, confidentiality, organizational skills, communication, problem solving, and professional judgment.

Agentic skills are reusable capabilities available to AI agents through their underlying models, contextual information, instructions, constraints, tools, and computational resources. In card access operations, these skills may include information extraction, request classification, record comparison, completeness checking, policy checking, workflow routing, data validation, discrepancy detection, queue prioritization, inventory tracking, document generation, reporting, and tool use.

Both skill types can contribute to the same occupational responsibilities, but their involvement levels vary. High-volume request review may involve substantial agentic support because completeness, formatting, and basic consistency can be checked systematically. Decisions involving unusual authorization conditions or conflicting information may require stronger human involvement. Credential production may combine automated data validation and workflow control with human inspection and physical handling of secure credentials.

The useful relationship is therefore not a fixed division in which certain responsibilities permanently belong to humans and others belong to AI. Human and agentic involvement can change from one task to another and even between different actions within the same task.

1.1. Management Analysts as an Occupation

A management analyst is a professional who studies organizational problems, processes, structures, information, and performance to identify opportunities for improvement and recommend changes that can help organizations operate more effectively and efficiently. The occupation connects organizational analysis with problem solving, data analysis, process improvement, systems design, management advisory work, organizational change, and performance evaluation.

Management analysis extends beyond identifying inefficiencies or preparing recommendations. A management analyst must understand how organizational objectives, work processes, personnel, information flows, financial conditions, operating procedures, technology, management practices, and organizational constraints interact. The analyst examines current conditions, identifies problems or opportunities, develops possible solutions, communicates recommendations, and helps determine whether implemented changes produce the intended results.

Typical responsibilities of management analysts include:

• organizational studies and current-state assessment;
• business, financial, and organizational performance analysis;
• process, system, and organizational improvement design;
• management recommendations and strategic advisory support;
• implementation, change coordination, and effectiveness evaluation; and
• procedures, documentation, records, and knowledge transfer.

These responsibilities are fulfilled through different tasks and subtasks, and each task may require multiple actions with different skill requirements. Human skills and agentic skills can both contribute to performing these responsibilities, but their levels and forms of involvement may differ according to the nature of the organizational problem, task ambiguity, information quality, stakeholder involvement, analytical complexity, organizational consequences, and expected outcome.

1.2. Human Skills and Agentic Skills in Management Analysis

Management analysis increasingly involves both human skills and agentic skills.

Human skills are reusable capabilities possessed and developed by management analysts. They may include organizational analysis, analytical reasoning, systems thinking, financial reasoning, process analysis, interviewing, observation, stakeholder communication, problem solving, facilitation, strategic reasoning, contextual interpretation, change management, and professional judgment.

Agentic skills are reusable capabilities available to AI agents through their underlying models, contextual information, instructions, constraints, tools, and computational resources. In management analysis, these skills may include information retrieval, document extraction, classification, summarization, data analysis, financial comparison, trend analysis, process mapping, workflow comparison, scenario generation, report drafting, consistency checking, recommendation support, and tool use.

Both skill types can contribute to the same occupational responsibilities, but their involvement levels vary. Organizational diagnosis may rely heavily on human interaction, observation, and contextual interpretation while using agentic skills to organize and compare evidence. Large-scale analysis of operational or financial information may involve high agentic participation while requiring human judgment to determine what identified patterns mean. Process redesign may combine agentic generation and comparison of alternatives with human understanding of organizational feasibility and stakeholder consequences.

The useful relationship is therefore not a fixed division in which certain responsibilities permanently belong to humans and others belong to AI. Human and agentic involvement can change from one task to another and even between different actions within the same task.