Upon completion of this course, participants will be able to:

• Develop a strategic roadmap for evaluating and implementing **AI** solutions across the **HR** ecosystem (TA, L&D, Performance).
• Understand the functionality and strategic application of various **HR AI** tools (e.g., chatbots, screening algorithms, predictive models).
• Master the process of designing and implementing an **AI Governance Framework** specific to **HR** data and processes.
• Identify, audit for, and actively mitigate algorithmic bias in **AI** screening and predictive models to ensure fairness.
• Ensure compliance with emerging global **AI** regulations and data privacy laws (**EU AI Act, GDPR, etc.**).
• Develop an effective organizational change management plan to drive user trust and adoption of **AI** tools among employees and managers.
• Establish clear roles, responsibilities, and accountability for the ethical use and oversight of **AI** decision-making.
• Measure the **ROI** of **AI** implementation by tracking efficiency gains and quality improvements in **HR** functions.

• HR Leaders and Directors of Talent Management and **HRIS**
• Chief **HR** Officers (**CHROs**) and Executive Sponsors of **AI** initiatives
• **IT** and Digital Transformation Leaders collaborating on **HR AI** projects
• Compliance Officers and Legal Counsel focused on **AI** ethics and regulation
• **People Analytics** Professionals managing predictive models

• Group exercises designing an **AI Governance Framework** (including an ethical charter) for an organization.
• Workshops on auditing provided data samples to identify potential sources of algorithmic bias in a hiring model.
• Case studies analyzing the compliance failures and public backlash of flawed **HR AI** implementations.
• Individual exercises developing a change management communication plan to announce a new **AI**-powered employee assistant.
• Discussions on the legal challenges and compliance requirements of the **EU AI Act** for global companies.

Unit 1: Strategic Foundations of HR AI

• Defining the landscape of **AI** in **HR**: Generative **AI**, Machine Learning, and Robotic Process Automation (**RPA**)
• Developing a clear strategic vision and business case for **AI** adoption in core **HR** functions (e.g., Talent Acquisition, L&D)
• Identifying the key ethical, bias, and compliance risks unique to **AI** deployment in human capital decisions
• Framework for "human in the loop" design: maintaining oversight and accountability in automated processes

Unit 2: Implementation and Technology Strategy

• Mastering the vendor selection and procurement process for specialized **HR AI** solutions
• Strategies for integrating **AI** tools seamlessly with the existing **HRIS** and data architecture
• Developing a phased implementation roadmap with clear milestones, testing, and pilot programs
• Utilizing **RPA** (Robotic Process Automation) to achieve immediate efficiency gains in transactional **HR** tasks

Unit 3: AI Governance and Algorithmic Bias Mitigation

• Designing the **HR AI Governance Framework**: policies, procedures, and ethical review boards
• Understanding how data bias (historical data) leads to algorithmic bias in predictive models
• Techniques for auditing **AI** screening and scoring models for discriminatory outcomes and fairness (e.g., disparate impact)
• Ensuring transparency and explainability: communicating *how* **AI** decisions are made to candidates and employees

Unit 4: Compliance and Emerging Regulation

• Overview of global regulatory trends (e.g., **EU AI Act, NYC Local Law 144**) impacting **HR AI**
• Strategies for maintaining compliance with **GDPR** and other privacy laws when **AI** processes personal data
• Documenting **AI** usage: record-keeping and data retention requirements for algorithmic fairness audits
• Developing a legal risk assessment strategy for all **AI** tools used in high-stakes **HR** decisions (e.g., hiring, performance)

Unit 5: Change Management and Measuring ROI

• Leading the change management required to build employee and manager trust in **AI**-assisted decisions
• Strategies for training **HR** staff on the responsible use, interpretation, and limits of **AI** tools
• Key metrics for measuring **AI ROI** (e.g., Recruiter Efficiency, Reduction in Algorithmic Bias, **Time-to-Hire**)
• Action Planning: establishing a continuous monitoring and periodic re-auditing cycle for **HR AI** models