AI in HR: Plain-Language Glossary for People Teams

Artificial intelligence is reshaping the workplace — and HR is right at the center of the transformation. Whether it’s streamlining hiring, enhancing the employee experience, or guiding strategic decisions, AI can be a powerful partner when understood and used responsibly. This glossary breaks down the most common AI terms in plain language to help HR professionals build confidence, stay current, and make smart, people-first choices in an AI-driven world.

Core AI Concepts

• AI: Technology that allows computers to do things that usually require human intelligence — such as problem-solving, understanding language, or recognizing patterns.
• Machine learning (ML): A type of AI that learns from data to make better decisions over time — such as learning which candidates tend to succeed in a role. 
• Natural language processing (NLP): Lets computers understand and respond to human language — useful for analyzing resumes or answering employee questions. 
• Generative AI (GenAI): AI that can create new content, such as writing job descriptions, drafting emails, or summarizing candidate feedback. 
• Prompt/prompting: A prompt is the question or instruction you give to an AI tool — for example, asking ChatGPT to write a job ad or summarize a meeting. Prompting or prompt engineering is the skill of giving clear, specific input to get useful, accurate output. (Think of it like giving good directions so AI knows what you need.)
• Algorithm: A set of rules the AI follows to make decisions — kind of like a recipe, but for processing data.
• Training data: The examples or information used to “teach” AI how to do its job — such as resumes, employee reviews, or survey results.
• Robotic process automation (RPA): RPA involves automating repetitive tasks through AI-driven bots. HR applications include onboarding workflows, payroll processing, and background checks.

Member Resource: AI Prompting Guide (With Templates)

AI in the HR World

• Talent intelligence/talent analytics: Using AI tools to gather and analyze workforce data to help make smarter hiring, promotion, or retention decisions.
  • Predictive hiring: Identifying candidates likely to succeed in specific roles.
  • Attrition prediction: Forecasting which employees might leave and why.
  • Diversity analytics: Assessing inclusion and diversity metrics across the organization. 
• Resume parsing: Automatically pulling out key info from resumes, such as skills and experience, to save recruiters time.
• Candidate matching: AI that compares job requirements to a candidate’s background and recommends the best fits, including: 
  • Skill-based matching: Finding candidates with transferable skills for open roles.
  • Passive candidate sourcing: Identifying potential hires who aren’t actively job hunting.
• Bias mitigation: Steps taken to make sure AI hiring tools don’t unfairly favor or discriminate against certain groups.
  • Recruitment bias: Ensuring job descriptions and hiring algorithms are inclusive.
  • Performance reviews: Flagging potential biases in manager evaluations.
  • Promotion processes: Ensuring equitable advancement opportunities. 
• People analytics: Looking at employee data — such as performance or engagement — and using AI to spot trends or predict outcomes.
  • Workforce planning: Forecasting future hiring needs and skills gaps.
  • Employee engagement: Analyzing survey data to improve workplace satisfaction.
  • Succession planning: Identifying high-potential employees for leadership roles. 
• AI + HI = ROI: This phrase means artificial intelligence plus human intelligence equals return on investment. It’s a reminder that AI is powerful, but it works best when combined with human judgment, empathy, and experience. In HR, that means using AI to handle tasks like screening or analyzing data — while people focus on decisions, relationships, and strategy. It’s not about replacing HR — it’s about empowering it.
• Chatbot: A virtual assistant that can answer questions, schedule interviews, or help employees find HR info 24/7.
  • FAQs: Providing instant responses to employee questions.
  • Scheduling: Automating the coordination of candidate interviews.
  • Policy guidance: Offering employees quick access to HR policies.
• Digital interviewing: AI-assisted video interviews that analyze factors such as word choice or tone to help assess candidates (used with caution for fairness). 
• AI Agent: An AI “helper” that can take actions on your behalf — such as onboarding a new hire or guiding employees through a benefits process — without needing a person to step in.
• Employee experience platforms (EXPs): AI-powered platforms designed to improve employee engagement, learning, and well-being. Features include:
  • Tailored training recommendations.
  • AI-driven tools for peer or manager recognition.
  • Tools that track and recommend wellness initiatives.

Member Resource: How to Effectively Leverage AI in Interviewing

Data and Ethics

• Data privacy: Making sure all employee or candidate information used by AI is kept secure and used responsibly.
• Explainability: Being able to clearly understand how an AI tool came to a decision — important for trust and fairness. 
• Transparency: Knowing what AI is doing, what data it uses, and how it affects decisions in your workplace. 
• Fairness: Ensuring AI treats everyone equally and doesn’t reinforce bias or discrimination. 
• Auditability: The ability to look back and check how AI made its decision, like an HR paper trail.

Strategic and Emerging Ideas 

• Augmented intelligence: AI that supports people rather than replaces them — helping HR professionals work faster or smarter. 
• Predictive analytics: Using AI to forecast circumstances such as which employees might leave or who might need extra support.
  • Retention risks: Identifying employees at risk of leaving.
  • Hiring success: Predicting the likelihood of a candidate thriving in a role.
  • Workforce trends: Anticipating future skills or headcount needs. 
• Sentiment analysis: Analyzing employee feedback ( such as surveys or reviews) to understand how people feel at work. This is particularly useful for: 
  • Employee pulse surveys: Understanding employee happiness and engagement. 
  • Exit interviews: Identifying patterns in why employees leave. 
  • Culture monitoring: Detecting shifts in workplace morale. 
• Skills ontology: A map of skills and how they connect to roles — helps AI recommend training, promotions, or hiring options.
  • Upskilling initiatives: Identifying gaps and recommending training.
  • Career pathing: Suggesting internal mobility opportunities for employees.
  • Job description optimization: Ensuring clarity and relevance in postings. 
• Digital twin (of the workforce): A virtual model of your team — including roles, skills, and performance — used for workforce planning or scenario modeling.

Want to Go Deeper?

If you’re ready to take your knowledge to the next level, check out the SHRM AI+HI Specialty Credential. It’s designed specifically for HR pros who want to lead confidently in the age of AI — whether you’re selecting tools, navigating risks, or shaping strategy.