Master change leadership with our Singapore-focused module combining classical models (Kotter, Lewin, ADKAR) and AI-driven tools. Learn stakeholder engagement, digital transformation readiness, and data-powered strategies to lead effective organisational change.
Overview
Change Management focuses on managing and leading organizational change, blending classical change management theories with AI-driven strategies. Students explore frameworks for effective change (e.g. Kotter’s 8 Steps, Lewin’s Change Model, ADKAR) alongside modern digital transformation practices. Equal emphasis is placed on people-centric skills (stakeholder engagement, communication, overcoming resistance) and AI tools that facilitate change. By integrating AI analytics and automation, students learn to plan and implement changes more efficiently, using data to anticipate challenges and tailor change initiatives. This module prepares learners to champion change in a business world where technology and AI are continuously driving organizational evolution.
Learning Outcome:ChatGPT or similar GPT-based assistants – to draft change announcements, training materials, and FAQs for employees, ensuring clear and consistent communication.
Stakeholder analysis and engagement strategies during change
Learning Outcome:Survey and Sentiment Analysis Tools (e.g. Qualtrics with AI sentiment analysis) – to collect employee feedback and analyze sentiment toward changes, allowing early detection of resistance or issues.
Communication planning and managing resistance to change
Learning Outcome:AI-powered Chatbots (internal use) – for employee support during transitions (answering common questions about new systems or policies), providing 24/7 change helpline functionality.
Learning Outcome:Microsoft Power BI / Tableau – for visualizing change KPIs (e.g. productivity, adoption rates) and real-time dashboards to monitor the impact of change initiatives.
AI in Change Management: Using data analytics and predictive modeling to guide change; AI-based employee sentiment analysis to gauge morale; chatbots and NLP for change communication; digital adoption platforms to support technology rollouts
Learning Outcome: Predictive Analytics Platforms – to forecast outcomes of change (e.g. identifying departments at risk of low adoption), enabling proactive interventions (examples include IBM Watson Analytics or SAS Advanced Analytics for predictive modeling).