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

• Evaluate and select appropriate **AI** and technology solutions for different stages of the talent acquisition pipeline.
• Master the use of automation tools (**RPA**) to streamline high-volume, transactional recruiting tasks.
• Understand the ethical risks of algorithmic bias in **AI** screening and develop mitigation strategies.
• Apply data analytics from **ATS/CRM** systems to drive predictive recruiting and optimize sourcing channels.
• Develop a strategic implementation roadmap for integrating new **TA** technology with existing **HRIS** and systems.
• Ensure compliance with emerging global regulations concerning the use of **AI** in hiring (e.g., **EU AI Act**).
• Utilize **AI** tools for enhanced candidate experience, personalized communication, and chatbot engagement.
• Measure the return on investment (**ROI**) and effectiveness of **AI** and technology tools in talent acquisition.

• HR Leaders and Directors of Talent Acquisition
• HR Technology and **HRIS** Specialists
• Recruitment Operations and Analytics Managers
• C-Suite Executives responsible for **HR** Digital Transformation
• Compliance and Legal professionals focused on **AI** ethics

• Group exercises evaluating case studies of successful and ethically challenged **AI** hiring implementations.
• Workshops on auditing sample **AI** screening results for potential algorithmic bias.
• Individual exercises developing a technology stack roadmap and implementation plan for an organization.
• Discussions on the ethical and legal implications of the **EU AI Act** for recruitment strategies.
• Role-playing scenarios coaching a recruiter on effectively using a new **AI** sourcing tool.

Unit 1: The Landscape of AI in Talent Acquisition

• Defining the various applications of **AI** in **TA** (sourcing, screening, interviewing, engagement)
• Understanding the strategic shift: from manual recruitment to intelligent automation (**RPA**)
• The competitive advantage of using predictive analytics for talent forecasting and pipeline health
• Key considerations for build vs. buy decisions for **TA** technology solutions

Unit 2: Ethical AI and Algorithmic Bias Mitigation

• Understanding how algorithmic bias is created and perpetuated in training data and algorithms
• Strategies for auditing **AI** screening tools for bias and ensuring equitable outcomes
• Compliance with emerging regulations (e.g., **EU AI Act, NYC Law 144**) for automated decision-making
• The ethical responsibility of recruiters to understand and interpret **AI** outputs transparently

Unit 3: AI-Powered Sourcing and Candidate Experience

• Utilizing **AI** sourcing tools to identify passive candidates and expand diversity reach
• Implementing and managing recruitment chatbots and virtual assistants for 24/7 candidate engagement
• Strategies for using **AI** to personalize job recommendations and communication throughout the candidate journey
• Mastering data integration between **ATS (Applicant Tracking System)** and **CRM (Candidate Relationship Management)** systems

Unit 4: Technology Implementation and Change Management

• Developing a robust technology implementation roadmap and managing vendor selection and relationship
• Strategies for change management: training recruiters and hiring managers on new **AI** tools and processes
• Ensuring seamless integration between new **TA** tech and core **HRIS** and payroll systems
• Managing data security and privacy (**GDPR, CCPA**) compliance within the **TA** technology ecosystem

Unit 5: Measurement, ROI, and Future Trends

• Key metrics for measuring the **ROI** of **AI** implementation (e.g., recruiter efficiency, Time-to-Hire reduction)
• Analyzing **AI** impact on Quality-of-Hire and diversity outcomes and making adjustments
• Forecasting the next generation of **AI** in **TA** (e.g., virtual reality assessments, generative **AI** job descriptions)
• Building an internal capability for **TA** data analytics and predictive modeling for sustained advantage