AI for HR: Recruit, Develop and Retain Talent

Use AI responsibly for recruiting, workforce planning, performance, learning, employee experience, and retention

Created byDr. Law Payne

Last updated 10/2026

English

What you'll learn

- Build a practical AI Human Resources strategy connected to meaningful business outcomes.
- Diagnose workflows, evidence gaps, assumptions, risks, and decision bottlenecks.
- Use AI for research, analysis, generation, and coordination without delegating accountability.
- Apply human review, verification, privacy, and quality controls at the right moments.
- Measure efficiency, quality, user response, business value, and organizational learning.
- Develop a ninety-day roadmap for implementation, governance, and responsible scale.

Course content

1 section • 5 lectures • 49m total length

The Responsible AI Talent Operating Model9:44
Learning objective: Explain and apply business outcomes, employee impact, data boundaries, human review, fairness controls in a realistic professional or business context.

Lecture description:

This lecture explains business outcomes, employee impact, data boundaries, human review, fairness controls. Students work through a practical framework, apply it to a continuing business case, examine common failure modes, and complete an exercise they can use immediately.
Recruiting Selection and Candidate Experience9:49
Learning objective: Explain and apply job analysis, sourcing support, screening risk, structured interviews, candidate communication in a realistic professional or business context.

Lecture description:

This lecture explains job analysis, sourcing support, screening risk, structured interviews, candidate communication. Students work through a practical framework, apply it to a continuing business case, examine common failure modes, and complete an exercise they can use immediately.
Skills Workforce Planning and Internal Mobility9:54
Learning objective: Explain and apply skills taxonomy, capability gaps, workforce scenarios, career pathways, talent marketplaces in a realistic professional or business context.

Lecture description:

This lecture explains skills taxonomy, capability gaps, workforce scenarios, career pathways, talent marketplaces. Students work through a practical framework, apply it to a continuing business case, examine common failure modes, and complete an exercise they can use immediately.
Performance Learning and Manager Effectiveness9:54
Learning objective: Explain and apply goal quality, feedback support, learning design, manager coaching, performance safeguards in a realistic professional or business context.

Lecture description:

This lecture explains goal quality, feedback support, learning design, manager coaching, performance safeguards. Students work through a practical framework, apply it to a continuing business case, examine common failure modes, and complete an exercise they can use immediately.
Employee Experience Retention and AI Governance9:45
Learning objective: Evaluate and apply listening systems, retention signals, privacy, policy design, implementation roadmap in a realistic professional or business context.

Lecture description:

This lecture explains listening systems, retention signals, privacy, policy design, implementation roadmap. Students work through a practical framework, apply it to a continuing business case, examine common failure modes, and complete an exercise they can use immediately.

Requirements

- No coding experience is required unless a lecture explicitly demonstrates a technical workflow.
- Access to relevant AI tools is helpful but not required to understand the frameworks.
- Bring a real business, client, team, project, or professional objective for the exercises.
- Be willing to test assumptions and verify AI-generated claims.

Description

This course contains the use of artificial intelligence.

Artificial intelligence creates value when it improves a real decision, workflow, or customer outcome. AI for HR: Recruit, Develop and Retain Talent provides a structured system for professionals who want to improve talent decisions and employee experiences while protecting fairness, privacy, transparency, and human dignity.

This course is designed for HR professionals, people leaders, recruiters, learning leaders, consultants, and business executives. It moves beyond disconnected prompts and tool demonstrations. Students learn how to define outcomes, organize evidence, design practical workflows, evaluate quality, preserve human accountability, and connect AI activity to measurable results.

Across five substantial lectures, Dr. Law Payne explains the strategic logic and practical execution of AI Human Resources. Every lecture includes a decision framework, a continuing business case, quality controls, implementation guidance, and an assignment that learners can apply to an organization, client, career, or business.

The course emphasizes responsible practice. Students learn where AI can accelerate research, analysis, generation, and coordination, as well as where privacy, bias, accuracy, security, or reputation require stronger safeguards. The aim is not maximum automation. The aim is better performance with visible ownership.

Dr. Law Payne is a business educator, growth strategist, nine-figure entrepreneur, published author, and Doctor of Business Administration specializing in Organizational Leadership and Development. His experience includes more than one billion dollars in managed sales and more than fifteen years of teaching, coaching, and mentoring across business, psychology, leadership, fitness, and education.

By the end of the course, students will have a working AI Human Resources operating system and a ninety-day plan for disciplined implementation.

Who this course is for:

- HR professionals, people leaders, recruiters, learning leaders, consultants, and business executives.
- Professionals who want structured workflows instead of prompt collections.
- Teams seeking practical AI adoption with human accountability.
- Learners who want to connect AI activity to customer, operational, or financial outcomes.