Subject Overview

This subject examines how artificial intelligence, automation, demographic change, new employment models and digital transformation are reshaping work and human resource management. It integrates foundational theories, HR applications, people analytics, managerial decision-making, responsible AI governance and future workforce strategy.

Learning Goal

Students will be able to critically evaluate future-of-work developments, design AI-enabled HR practices, use workforce analytics for strategic decisions, lead responsible digital transformation and develop ethical, legally compliant and human-centred workforce strategies.

Syllabus

1. Foundations of the Future of Work

Learning Objective: Explain the evolution of work and HR, key terminology, industrial revolutions, socio-technical systems theory and the relationship among technology, people, organizations and institutions.

Category: foundation

2. Forces Reshaping Work and Employment

Learning Objective: Assess technological, economic, demographic, environmental and geopolitical drivers, including automation, globalization, ageing, workforce diversity, labour-market polarization and sustainability transitions.

Category: core

3. Work, Job and Workforce Design

Learning Objective: Apply job characteristics, job demands–resources and work design theories to task decomposition, job redesign, skills-based work, remote and hybrid work, agile teams, platform work and contingent workforce models.

Category: core

4. Digital HR Transformation and Architecture

Learning Objective: Design digital HR transformation using HR operating models, cloud HRIS, employee platforms, workflow automation, data architecture, interoperability, digital maturity models and transformation roadmaps.

Category: strategic

5. Artificial Intelligence Foundations for HR

Learning Objective: Distinguish automation, machine learning, natural language processing, generative AI, agents and predictive systems; explain data and model lifecycles, capabilities, limitations and appropriate HR use cases.

Category: foundation

6. Strategic Workforce Planning and Skills Intelligence

Learning Objective: Use environmental scanning, workforce segmentation, demand-and-supply forecasting, scenario planning, skills taxonomies and skills graphs to identify capability gaps and evaluate build, buy, borrow or automate decisions.

Category: advanced

7. AI-Enabled Talent Acquisition and Mobility

Learning Objective: Evaluate AI applications in sourcing, screening, assessment, interviewing, onboarding, internal talent marketplaces and succession while addressing validity, candidate experience, bias and human oversight.

Category: application

8. Learning, Reskilling and Career Development

Learning Objective: Develop reskilling strategies using competency models, personalized learning, learning experience platforms, generative AI, coaching technologies, credentialing and measures of learning transfer and business impact.

Category: application

9. Performance, Employee Experience and Well-being

Learning Objective: Apply digital tools and AI to goal management, feedback, engagement, sentiment analysis, productivity and well-being while avoiding surveillance, work intensification and inappropriate algorithmic control.

Category: application

10. People Analytics and Evidence-Based HR Decisions

Learning Objective: Apply descriptive, diagnostic, predictive and prescriptive analytics; formulate workforce questions, select metrics, build dashboards, interpret models, conduct experiments and causal analysis, and communicate findings responsibly.

Category: advanced

11. Human–AI Collaboration and Organizational Change

Learning Objective: Design effective human–AI workflows, allocate decisions between people and machines, evaluate augmentation versus substitution, and lead adoption through change management, stakeholder engagement, trust and AI literacy.

Category: strategic

12. Responsible AI, Ethics and Inclusion

Learning Objective: Critically evaluate fairness, discrimination, transparency, explainability, accountability, accessibility, privacy, autonomy and dignity using ethical frameworks, bias audits, impact assessments and responsible AI principles.

Category: core

13. AI Governance, Law and Risk Management

Learning Objective: Establish governance structures for HR AI covering decision rights, regulatory compliance, data protection, cybersecurity, model validation, documentation, monitoring, audit, vendor due diligence, incident response and human appeal mechanisms.

Category: strategic

14. Future-Ready HR Strategy and Emerging Developments

Learning Objective: Integrate workforce strategy with business strategy; evaluate autonomous agents, immersive work, robotics, digital twins and evolving regulation; develop scenarios, investment cases, metrics and resilient human-centred future-of-work roadmaps.

Category: contemporary