What you'll learn:
- Explain generative AI fundamentals and its limits (bias, hallucinations, privacy) in HR use cases.
- Create inclusive job descriptions and candidate communications using AI prompts and review checklists.
- Apply AI to performance reviews: summarize feedback, draft narratives, and propose growth goals responsibly.
- Design AI-driven learning paths, coaching, and internal mobility based on skills and role requirements.
- Plan an AI rollout in HR: pick use cases, integrate tools, run pilots, and measure impact with KPIs.
- Implement responsible AI practices: audits, transparency, data minimization, and human-in-the-loop decisions.
How much of HR’s week is spent on repetitive work—drafting job posts, screening resumes, scheduling interviews, answering policy questions, and summarizing feedback?
Research and industry reporting suggest a large share of HR’s day-to-day workload (often cited as up to ~70%) can be automated or meaningfully supported by AI. At the same time, adoption is accelerating: many HR teams are already using some form of AI, and the vast majority of organizations say they plan to increase AI investment over the next few years.
That creates a massive opportunity—and a real risk.
Opportunity: use generative AI to reduce admin work and deliver a more personalized, human employee experience.
Risk: bias, privacy issues, weak data foundations, and “hallucinated” outputs that undermine trust.
This course is your practical guide to applying AI across the full talent lifecycle—recruiting, onboarding, performance, learning, mobility, engagement, and employee experience—while keeping humans in control.
In this course, you’ll learn how to:
Understand generative AI fundamentals (LLMs) and how it differs from predictive AI
Draft inclusive, high-quality job descriptions faster and tailor messaging to candidate segments
Improve sourcing and screening with AI matching—without going on autopilot
Use chatbots and automation for candidate FAQs and interview scheduling to boost candidate experience
Summarize messy feedback into clear performance review drafts and development narratives
Recommend individualized goals, learning paths, and coaching-style support at scale
Enable internal mobility, career pathing, and succession planning with skills intelligence
Track engagement signals and detect burnout risks early—without creating a surveillance culture
Personalize benefits, recognition, and day-to-day work patterns responsibly
Implement AI in HR the right way: workflow audits, tool selection, integrations, pilots, and metrics
Build ethical guardrails: bias audits, transparency, privacy, and human-in-the-loop decisions
By the end, you’ll have a clear playbook for what’s possible, what’s practical, and how to roll out AI in a way that improves both productivity and trust—so HR can spend more time on the “human” part of human resources.
