Module 1: AI and Its HR Applications - 1 Day
Understanding AI and Its Principles
- Definition of Artificial Intelligence, Machine Learning and Deep Learning
- Distinction between Weak AI, Strong AI, and Automation
- Overview of AI use cases in business and HR
- Debunking common misconceptions about AI
Mapping AI Use Cases in HR
- Analysing current AI tools for recruitment, training and talent management
- Identifying challenges and opportunities for each use case to engage stakeholders
- Addressing ethical and operational considerations
Module 2 : AI for Recruitment – 0,5 Day
AI in Recruitment: Features and Challenges
- How CVs screening and matching tools work
- Recruiting chatbots and automated interviews
- Case study: successes and pitfalls of AI in recruitment
- Hands-on session: testing an AI-based CV screening tool and evaluating its effectiveness
Algorithmic Bias and Regulatory Framework
- Understanding algorithmic biases (with real-world examples)
- Regulations and legal obligations
- Case study: a real instance of AI-driven discrimination
- Brainstorming solutions to minimise bias
- Creating a personal guide to ethical best practices
Module 3: AI for Corporate Training and Workforce Planning – 0,5 Day
AI in Training & Skills Development
- Adaptive learning and personalised training paths
- Competency tracking and learning analytics
- Designing smart, AI-driven learning journeys
- Building an AI-integrated training plan
Performance Evaluation and Strategic Workforce Planning
- AI’s impact on job roles and organisational structure
- Identifying roles that can be automated or transformed
- Mapping future skills needed in the company
- Developing competency frameworks for emerging roles
MODULE 4: AI, Data and Strategic HR Decision-Making – 1 Day
Why use data in HR?
- Improve recruitment and internal mobility
- Monitor employee performance and engagement
- Anticipate turnover and support workforce planning
Where and how to collect HR data?
- Internal sources: HRIS, surveys, feedback, talent management tools
- External sources: benchmarks, professional networks, market trends
- Address data challenges: quality, bias, GDPR, and stakeholder support
Identifying and structuring relevant data
- Define key data for real-life use cases (e.g., turnover prediction)
- Ensure data reliability and usability
Using data to support decision-making
- Analyse and interpret HR data
- Hands-on use of data structuring tools
- Visualise HR KPIs for informed management
- Introduction to predictive analytics and machine learning in HR
Building a data-driven HR dashboard
With Artificial Intelligence & Wrap Up
