1. Foundations of Project Management This section ensures learners understand standard project management frameworks and practices before integrating AI: Project lifecycle phases (initiation to closure) Project scope, time, cost, quality management Stakeholder and communication management Risk and issue management Agile, Waterfall, and hybrid methodologies 2. Introduction to Artificial Intelligence Provides grounding in AI basics so learners can apply them in a project context: Definitions and key AI concepts Machine Learning (ML) and Deep Learning fundamentals Natural Language Processing (NLP) basics Data science and analytics overview Ethical considerations in AI 3. AI Applications in Project Management Core section focused on how AI transforms project delivery: Automating routine PM tasks (status reporting, scheduling) Intelligent task prediction and prioritisation Resource allocation and optimization using AI AI-driven risk prediction and mitigation Natural language processing for document analysis 4. Tools and Technologies Practical exposure to AI‑enabled project tools: AI project management platforms (e.g., Monday.com AI features, ClickUp AI, Microsoft Project with AI) Generative AI (ChatGPT, Bard) for PM tasks Predictive analytics tools (like Power BI with AI insights) Automation tools (RPA) for workflow efficiencies 5. Data‑Driven Decision Making How to leverage data for better project outcomes: Collecting and cleaning project data Using dashboards and visual analytics Forecasting project outcomes with AI Key performance indicators (KPIs) and metrics 6. AI‑Enhanced Planning and Scheduling Using AI to optimize project schedules and plans: Automated schedule generation Predictive estimation of time and effort Adaptive planning with real‑time insights 7. AI in Risk and Issue Management Focuses on predictive risk analysis: Risk identification using historical data AI models for probability and impact forecasting Early warning systems for potential issues 8. Ethical, Legal and Governance Aspects Examines responsible use of AI in projects: Bias, fairness, and transparency in AI models Data privacy and security considerations Compliance with governance standards (e.g., GDPR) 9. Change Management and Adoption Preparing teams for AI transformation: Leading digital transformation initiatives Training and upskilling stakeholders Organizational readiness for AI‑augmented PM 10. Case Studies and Real‑World Applications Applied learning through examples: AI success stories in real projects Lessons from AI implementation challenges Sector‑specific applications (IT, construction, healthcare) 11. Hands‑On Projects and Simulations Experiential learning components: Building AI dashboards for project insights Using AI tools to automate PM tasks Simulated project scenarios with AI interventions
