AI is everywhere. Whether it is embedded in the tools we use, inspiring the conversations we have, or changing the way we work, it’s woven into the fiber of almost all of our online interactions. But with all the buzz, understanding the language of AI can feel overwhelming. This glossary strips away the complex jargon and explains essential AI terms in simple, relatable ways. Whether you’re leading a team, exploring new tech, or just curious about AI, you’ll find the clarity you need here.

Inside this glossary:

AI in HR Foundations
AI Capabilities & Techniques
Talent Acquisition AI
Talent Management & Workforce Analytics
AI Safety, Ethics & Compliance
AI Infrastructure & Systems
Moving Forward with AI

AI in HR Foundations

These foundational terms define the core building blocks of artificial intelligence in human resources. Before exploring specialized applications, learning these concepts is essential for understanding how AI thinks, learns, and transforms talent strategies. From basic artificial intelligence to advanced language models, these nine terms form the vocabulary every HR leader needs to navigate AI-powered talent solutions with confidence.

Artificial Intelligence (AI)

Artificial intelligence is technology that enables machines to perform tasks typically requiring human intelligence, such as recognizing patterns, understanding language, and solving problems.

What it does for candidates/employees: Creates smoother experiences through faster responses and personalized interactions at scale.
What it does for talent acquisition and talent management teams: Reduces repetitive work while generating insights that support better decision-making.

Examples in action:

A candidate engages with conversational AI to find suitable jobs instead of navigating traditional career sites manually.
An employee receives relevant opportunities automatically — gigs, learning, and career paths that are matched to their experience level and skills.

Machine Learning (ML)

Machine learning is a subset of AI that enables systems to improve performance over time through experience and continuous feedback. Rather than following explicit programming, machine learning algorithms analyze data to identify patterns and make predictions automatically.

What it does for candidates/employees: Improves matching between people and jobs, connecting talent to opportunities with greater speed and accuracy.
What it does for teams: Strengthens predictions about hiring, performance, and retention while delivering actionable insights for decision-making.

Examples in action:

A recruiter receives relevant candidate recommendations, and their feedback helps the system learn to surface better matches over time.
A hiring manager identifies team skill development needs, and this information flows back to recruiting requirements for faster talent acquisition.

Deep Learning

Deep learning is an advanced form of machine learning using multi-layered neural networks to recognize complex patterns in data. By processing information similarly to the human brain, deep learning enables AI systems to extract features from raw inputs without manual engineering.

What it does for candidates/employees: Powers smart recommendations and conversational tools that understand context and nuance.

What it does for teams: Enhances analysis of resumes, surveys, and employee feedback for more accurate insights.

Examples in action:

A healthcare system recognizes that "coordinated patient care teams" and "led multidisciplinary rounds" represent similar leadership experience despite different wording.
An engagement survey analysis identifies that phrases like "challenging workload" correlate with retention risk, helping HR prioritize interventions.

Explainable AI

Explainable AI, or X AI, accelerates talent processes by leveraging context, learning from patterns, and recommending actions with clear, understandable logic behind each decision.

What it does for candidates/employees: Simplifies the process of finding meaningful work through transparent recommendations.
What it does for teams: Provides interactions that improve processes and guide decisions with clear reasoning.

Examples in action:

A candidate receives clear reasoning for job recommendations: "This data analyst role matches your Python skills, healthcare experience, and remote work preferences."
A hiring manager gets explainable logic for candidate rankings: "Top candidate scored highest due to relevant project management experience and stakeholder communication skills."

AI Insights

AI insights are actionable recommendations generated by artificial intelligence after analyzing complex datasets. By detecting patterns and correlations difficult for humans to identify, AI transforms raw data into practical guidance for decision-making.

What it does for candidates/employees: Provides clear direction for career growth and personalized feedback based on comprehensive analysis.
What it does for teams: Transforms raw workforce data into practical, evidence-based actions and strategic recommendations.

Examples in action:

An HR team processes 500+ performance reviews and receives specific recommendations like "15 high-performers in engineering show disengagement signs."
A manager receives insights showing team productivity increases during weeks with fewer meetings and identifies advancement opportunities for team members.

Natural Language Processing (NLP)

Natural language processing (NLP) enables machines to understand, interpret, and respond to human language in both text and speech. NLP powers chatbots, resume parsing, and translation tools, making AI interactions feel natural and intuitive.

What it does for candidates/employees: Provides instant answers to questions and moves applications through systems more quickly.
What it does for teams: Reduces manual effort while improving accuracy in candidate matching and communication.

Examples in action:

A candidate receives a phone screen from a voice AI agent, interacting in natural language while enabling teams to gather information efficiently.
An HR business partner uses an NLP-powered chatbot to handle employee questions about benefits and policies with instant, personalized responses.

Generative AI (GenAI)

Generative AI creates new content such as text, images, or audio by learning patterns from existing data. Unlike traditional AI that primarily analyzes information, generative AI produces original outputs tailored to specific needs and prompts.

What it does for candidates/employees: Generates timely, personalized content and communications that feel relevant and engaging.
What it does for teams: Creates job descriptions, outreach emails, and training materials in significantly less time.

Examples in action:

A recruiter inputs requirements for 15 new positions and generates tailored job descriptions in minutes instead of hours.
An employee receives personalized meeting summaries and development plans with custom learning paths based on their goals and skill gaps.

Large Language Models (LLMs)

Large language models are advanced AI systems trained on massive amounts of text data, enabling them to understand context and generate human-like responses. LLMs power chatbots, writing assistants, and search tools, making AI interactions more natural and flexible.

What it does for candidates/employees: Provides realistic, conversational interactions with AI assistants that understand context and intent.
What it does for teams: Automates communication tasks and helps summarize or analyze text at scale with high accuracy.

Examples in action:

A candidate engages with a chatbot powered by an LLM trained on organizational data, finding relevant jobs and handling initial screening.
A talent team uses LLMs to analyze performance reviews and identify promotion-ready employees while flagging potential retention risks.

Applied AI

Applied AI is the practical implementation of artificial intelligence to solve specific, real-world problems and deliver measurable outcomes. Applied AI focuses on integrating AI into products and processes to create tangible value.

What it does for candidates/employees: Enhances everyday experiences through faster support and personalized interactions.
What it does for teams: Brings AI into daily workflows to make processes more efficient and intelligent.

Examples in action:

A voice agent adapts to candidates' questions during phone screens, adjusting conversation flow based on their responses and experience level.
A hiring manager with urgent needs uses an intake agent that handles cross-functional coordination and updates job descriptions in moments.

AI Capabilities & Techniques

These concepts explain how AI actually works, describing the methods machines use to learn, adapt, and provide intelligent results.

Predictive Analytics

Predictive analytics uses AI, machine learning, and statistical techniques to analyze historical and current data to forecast future outcomes. By identifying patterns and trends, it enables organizations to anticipate events and make proactive decisions.

What it does for candidates/employees: Provides proactive support through career path suggestions and development recommendations.
What it does for teams: Helps anticipate attrition, hiring needs, and future skill gaps before they become critical.

Examples in action:

An employee shows patterns suggesting advancement readiness, prompting their manager to discuss growth opportunities before external job searching begins.

A workforce planning team uses predictive analytics to forecast hiring needs six months in advance, enabling proactive talent pipeline building.

Reinforcement Learning

Reinforcement learning is a branch of machine learning where AI systems learn through trial and error, receiving feedback as rewards or corrections. Over time, the system adapts its behavior to maximize positive outcomes, discovering effective strategies for different scenarios.

What it does for candidates/employees: Creates tools that adapt and provide increasingly relevant experiences over time.
What it does for teams: Strengthens matching systems and recommendation engines through continuous learning from feedback.

Examples in action:

A candidate matching system learns from recruiter feedback that startup experience is preferred for senior marketing roles, improving future recommendations.

An internal mobility platform learns from successful career transitions to suggest better-fitting opportunities based on employee satisfaction patterns.

Agentic AI (AI Agents)

Agentic AIrefers to AI systems that operate autonomously to perform tasks, make decisions, and execute actions without requiring step-by-step human instructions. These systems are often called AI agents or digital coworkers as they can manage workflows, solve problems, and adapt to changing conditions.

What it does for candidates/employees: Keeps processes running smoothly with timely updates and reduces delays in important workflows.
What it does for teams: Takes over repetitive tasks, allowing people to focus on relationship-building and strategic thinking.

Examples in action:

A VP of Sales transitions to a new company, and a succession planning agent identifies potential successors with similar skills and leadership experience.

Leadership discovers that top-performing teams share common characteristics: regular one-on-ones, cross-team collaboration, and managers with leadership development training.

Prompt Engineering

Prompt engineering is the practice of designing clear, precise instructions to guide AI systems toward producing accurate, relevant, and useful outputs. Careful prompt crafting maximizes AI tool effectiveness, especially for generative AI applications.

What it does for candidates/employees: Results in more accurate and relevant automated communication that feels personalized and helpful.
What it does for teams: Improves the quality of AI-generated job postings, messages, and recommendations through better instruction design.

Examples in action:

A recruiting coordinator develops specific prompt templates for candidate outreach emails, resulting in higher response rates than generic messaging.
A hiring manager uses engineered prompts to generate consistent interview feedback summaries highlighting both strengths and development areas.

Personalization

Personalization uses AI to customize experiences for individuals by analyzing their skills, preferences, behaviors, and interactions. By tailoring content and recommendations, personalization makes each experience feel relevant and engaging.

What it does for candidates/employees: Provides career paths and opportunities that reflect personal goals and individual circumstances.
What it does for teams: Increases engagement through targeted content and support that resonates with specific team members.

Examples in action:

An employee sees learning opportunities matching their unique profile: advanced Python courses for data analysts, management training for specialists with leadership aspirations.
A candidate receives job recommendations based on their specific skills, location preferences, and career stage rather than generic postings.

AI Discovery

AI discovery uses artificial intelligence to uncover hidden patterns, relationships, and insights within complex datasets. By revealing trends and opportunities not immediately apparent, AI discovery helps organizations make more informed, proactive decisions.

What it does for candidates/employees: Reveals new growth opportunities and skill development paths they might not have considered.
What it does for teams: Identifies trends in talent pools, engagement patterns, and workforce needs that inform strategic planning.

Examples in action:

A talent team uses AI Discovery to access millions of profiles and identify candidates with specialized skills not found through traditional searches.
An organization discovers that high-performing teams share specific collaboration patterns, leading to targeted team development programs.

Talent Acquisition AI

Hiring is one of the first areas where organizations experience AI in action. These terms describe how AI supports sourcing, screening, and engaging candidates.

Resume Parsing

Resume parsing uses AI to automatically extract key information such as skills, education, experience, and certifications from resumes. By converting unstructured documents into structured, searchable profiles, it streamlines recruitment processes and improves candidate matching.

What it does for candidates/employees: Provides quick and consistent review of applications, reducing time between application and response.
What it does for teams: Saves hours of manual effort while creating searchable databases that improve talent discovery.

Examples in action:

A recruiter filling a Senior Data Analyst role automatically extracts and matches key requirements across hundreds of applications, instantly seeing qualified candidates ranked by fit.
A hiring manager reviewing Product Manager candidates quickly compares finalists' experience against critical requirements, enabling strategic interview discussions.

Candidate Matching

Candidate matching compares applicants' skills, experience, and qualifications with job requirements to recommend the most suitable candidates. Functioning like a recommendation engine, it uses algorithms to rank applicants and reduce bias.

What it does for candidates/employees: Improves chances of being considered for roles that truly align with their capabilities.
What it does for teams: Shortens hiring cycles by surfacing strong matches quickly and reducing time spent on unsuitable candidates.

Examples in action:

A software engineering manager receives candidates ranked by Python expertise, API development background, and database optimization skills, even without explicit keyword matching.
A candidate applying for a marketing coordinator position gets flagged as a better match for a digital marketing specialist position that just opened.

Fit Scoring

Fit scoring assigns numerical scores reflecting how closely candidates' skills, experience, and education align with specific roles. By combining multiple factors into a single measure, it helps recruiters prioritize top applicants and make data-driven hiring decisions.

What it does for candidates/employees: Highlights strengths beyond resume keywords, showcasing their full potential for specific roles.
What it does for teams: Helps prioritize candidates with the best potential fit while providing clear reasoning for rankings.

Examples in action:

A recruiter receives 200 applications for a risk analyst position and uses Fit Score to rank candidates with the strongest combination of relevant factors.
A hiring manager sees that a project management candidate scored highly for technical skills but lower for location preference, revealing capability strengths.

Intelligent Sourcing

Intelligent sourcing scans multiple platforms and databases to identify active and passive candidates, expanding search capabilities beyond traditional recruiting methods to discover hidden talent pools.

What it does for candidates/employees: Surfaces opportunities they might not have found otherwise, connecting them to relevant roles across platforms.
What it does for teams: Expands talent pipelines and discovers qualified candidates from unexpected sources and industries.

Examples in action:

A biotech startup searches across millions of profiles and identifies researchers with machine learning backgrounds who also have molecular biology expertise.
A financial services firm uses Intelligent Sourcing to find candidates with both cybersecurity certifications and banking experience from insurance and fintech companies.

Conversational AI

Conversational AI encompasses technologies like chatbots and AI assistants that understand and respond to human language in natural, conversational ways. By leveraging natural language processing it enables seamless human-AI interactions.

What it does for candidates/employees: Offers 24/7 support with quick, accurate answers to questions throughout their journey.
What it does for teams: Reduces repetitive inquiries while improving candidate experience through always-available assistance.

Examples in action:

A candidate receives a phone screen from a voice AI agent, interacting naturally while enabling the team to gather pertinent information efficiently.

An HR business partner uses a conversational AI system to handle dozens of daily employee questions with instant, personalized responses.

Talent Management & Workforce Analytics

AI extends beyond recruiting to help employees grow, managers plan, and leaders make informed workforce decisions.

Skills Intelligence

Skills intelligence uses AI to map, analyze, and assess workforce skills, identifying strengths, gaps, and future requirements. By providing a detailed understanding of current capabilities, it informs decisions about talent development and strategic hiring.

What it does for candidates/employees: Makes career opportunities and growth paths more visible by clearly mapping their skills to potential roles.
What it does for teams: Informs strategic planning for reskilling, upskilling, and internal mobility based on comprehensive skills analysis.

Examples in action:

A technology company planning a cloud migration identifies that 30% of developers have foundational cloud skills, with specific gaps requiring targeted training programs.
A business intelligence employee explores advancement opportunities, with skills intelligence revealing their positioning for product management roles with specific development recommendations.

Career Pathing AI

Career pathing AI analyzes employees' skills, experience, and career goals to suggest personalized development paths. By identifying growth opportunities, it recommends potential roles, training programs, and skill-building activities aligned with individual aspirations.

What it does for candidates/employees: Offers clear guidance on career progression and specific development steps needed for advancement.
What it does for teams: Improves retention by showing employees concrete future paths and development opportunities within the organization.

Examples in action:

A customer success representative interested in leadership receives three potential paths: team lead, account management, or training roles, with specific requirements for each.
A finance analyst discovers that adding data visualization skills and cross-functional project experience significantly increases advancement opportunities across multiple tracks.

People Analytics

People analytics utilizes AI and data science to analyze workforce data, uncovering patterns in employee engagement, performance, and productivity. Analyzing metrics like turnover, engagement, and collaboration provides actionable insights for optimizing workforce strategy.

What it does for talent: Provides access to more relevant opportunities for advancement because leaders are more informed.
What it does for teams: Guides evidence-based decisions on hiring, retention, and diversity through comprehensive workforce insights.

Examples in action:

A department with an increasing level of turnover discovers that high performers leave when they don't receive stretch assignments within 18 months of hiring.

Leadership learns that top-performing teams share common characteristics: regular one-on-ones, cross-team collaboration, and managers with leadership development experience.

Workforce Intelligence

Workforce intelligence integrates HR and business data to provide comprehensive views of talent supply, organizational needs, and workforce dynamics. By combining insights on skills, performance, and business objectives, it aligns talent strategies with organizational goals.

What it does for candidates/employees: Supports balanced staffing decisions that prevent overwork and ensure adequate resources for success.
What it does for teams: Aligns workforce planning with business priorities through integrated data analysis and strategic insights.

Examples in action:

A technology team shows skill gaps in cloud architecture, and workforce intelligence identifies three internal developers who could be upskilled rather than hiring externally.
An engineering manager notices increased attrition risk, with workforce intelligence revealing that high performers leave without technical leadership opportunities, prompting targeted development programs.

AI Safety, Ethics & Compliance

Trustworthy AI must be fair, transparent, and accountable. These terms cover the safeguards that keep AI reliable and responsible.

AI Regulation

AI regulation encompasses rules, policies, and standards governing how artificial intelligence is developed, deployed, and used responsibly. These regulations address transparency, fairness, accountability, and data privacy, particularly in sensitive areas affecting people's lives.

What it does for candidates/employees: Protects against unfair or biased treatment while ensuring transparency in AI-driven decisions.
What it does for teams: Provides compliance frameworks and builds trust in AI systems through adherence to established standards.

Examples in action:

A multinational company maintains GDPR compliance by providing candidates transparency about AI decision-making and conducting regular bias audits on hiring tools.
A talent acquisition team implements fairness monitoring and maintains audit trails for all AI decisions, building candidate trust while reducing legal risk.

Algorithmic Bias

Algorithmic bias occurs when AI systems produce unfair or skewed results due to flawed data, biased training inputs, or underlying assumptions in design. This can lead to unequal outcomes in hiring and performance evaluations, reinforcing existing inequalities.

What it does for candidates/employees: Reduces chances of being overlooked unfairly due to systematic biases in AI systems.
What it does for teams: Requires regular audits and monitoring to maintain fairness and compliance with ethical standards.

Examples in action:

A company's AI hiring tool consistently ranks male candidates higher for technical roles, prompting retraining on balanced data and bias detection implementation.
An employee performance system shows subtle bias against remote workers on collaboration ratings despite equal productivity, leading to redesigned evaluation criteria.

Bias Detection

Bias detection uses AI tools and analytics to identify, measure, and reduce discriminatory patterns in data, algorithms, and decision-making systems. By monitoring outcomes across different groups it helps organizations evaluate fairness and take corrective action.

What it does for candidates/employees: Promotes equity in processes and builds confidence that opportunities are distributed fairly.
What it does for teams: Supports compliance efforts and strengthens trust in AI-powered tools through ongoing fairness monitoring.

Examples in action:

A financial services company runs weekly bias monitoring reports on hiring recommendations by demographic groups, triggering investigations when disparities appear.
An internal mobility platform monitors whether career advancement suggestions and leadership development opportunities are distributed fairly across all employee groups.

Human-in-the-Loop

Human-in-the-loop (HITL) combines AI automation with human oversight, keeping people actively involved in important decisions. AI systems handle data processing and generate recommendations, while humans review, guide, or override outputs as needed.

What it does for candidates/employees: Maintains human oversight in decision-making processes while providing automated efficiency where appropriate.
What it does for teams: Balances automation efficiency with accountability and ethical oversight in decision-making processes.

Examples in action:

An AI system screens resumes and identifies top candidates, but all final hiring decisions require human review to avoid overlooking unusual qualifications.

Employee performance evaluations use AI to analyze metrics and feedback patterns, but all ratings require manager review with context about individual circumstances.

Responsible AI

Responsible AI encompasses the practice of designing, developing, and deploying artificial intelligence ethically and transparently, with fairness, safety, and accountability at its core. It emphasizes respect for privacy, bias reduction, and alignment with human values.

What it does for candidates/employees: Builds trust that personal data and career opportunities are handled ethically and responsibly.
What it does for teams: Reduces risks while aligning AI use with organizational values and ethical standards.

Examples in action:

An organization implements comprehensive AI governance frameworks with regular ethical reviews and transparent decision-making processes for all talent-related AI applications.
A talent team establishes clear guidelines for AI use in hiring, including bias monitoring, data privacy protection, and candidate rights to explanation.

Hallucination

Hallucination in AI refers to instances where systems generate information that sounds confident but is factually incorrect or disconnected from reality. These outputs appear convincing but can be misleading, highlighting the importance of human oversight and fact-checking.

What it does for candidates/employees: Assures that they receive accurate, reliable information through proper oversight and verification systems.
What it does for teams: Highlights the need for review processes and fact-checking in generative AI outputs to maintain accuracy.

Examples in action:

A candidate asks an AI chatbot about salary ranges and receives outdated information, prompting the implementation of fact-checking systems that verify responses against current data.
An HR team uses generative AI to draft job descriptions, but occasionally receives content including non-existent benefits, establishing review processes for all AI-generated content.

AI Infrastructure & Systems

AI becomes most valuable when it is scalable and consistent across the enterprise. These terms describe the systems that make large-scale adoption possible.

Enterprise Ontologies

Enterprise ontologies are structured frameworks that AI uses to define, organize, and connect roles, skills, processes, and relationships within organizations. By creating a common language for understanding work, they enable AI systems to interpret workforce data accurately and support talent management applications.

What it does for candidates/employees: Provides consistent recognition and matching of skills across different systems and departments.
What it does for teams: Creates a shared structure across HR systems for accurate insights and improved interoperability between platforms.

Examples in action:

A global technology company uses enterprise ontology to understand that "Machine Learning Engineer" experience directly relates to "Senior Data Scientist" roles through semantic connections.
An organization adds custom skills specific to proprietary technologies and internal methodologies, receiving semantic intelligence for AI-powered recommendations.

Pre-trained Industry Models

Pre-trained industry models are AI systems developed using large, domain-specific datasets, providing built-in knowledge tailored to particular fields. These models come equipped with specialized understanding, enabling faster adoption with minimal customization.

What it does for candidates/employees: Delivers recommendations that are relevant and contextually appropriate to their specific industry or role.
What it does for teams: Speeds up AI adoption with specialized tools that fit immediately without extensive training or customization periods.

Examples in action:

A healthcare system implements AI recruiting that already understands differences between nursing specializations and physician certifications, providing accurate matching immediately.
A financial services company deploys AI models that recognize distinct competency requirements for "quantitative analyst" roles versus "risk analyst" positions.

Intelligent Automation

Intelligent automation combines AI with automated processes to handle repetitive tasks at scale, streamlining operations by reducing manual effort and improving consistency across workflows.

What it does for candidates/employees: Delivers faster onboarding experiences and more responsive day-to-day support through automated processes.
What it does for teams: Automates scheduling, data entry, and routine workflows, freeing up time for strategic and relationship-focused activities.

Examples in action:

A new employee's job acceptance automatically triggers account creation, equipment ordering, training enrollment, and buddy assignment across all necessary systems.
Interview coordination across multiple time zones gets handled automatically, with systems analyzing availability and expertise alignment while suggesting backup interviewers.

Moving Forward with AI

The terms in this glossary are more than definitions; they are a foundation for understanding how AI is shaping the future of work. With this knowledge, you can cut through hype, ask sharper questions, and spot real opportunities. AI is no longer just for specialists; it’s a language every professional should be comfortable using.

Stop Guessing, Start Growing: Unlock Phenom’s AI & Automation Toolkit.

Download it today to access the Maturity Model Guide and take the assessment to see exactly where your organization stands — and how Phenom helps you move forward with confidence.

404.