Opleiding: AI+ Project Management Practitioner™

Build stronger project foundations by combining AI-assisted planning with practical decision support.

Discover how AI enhances project planning, scheduling, task prioritization, and progress tracking while reducing manual effort and improving consistency. Learn to use data-driven insights for timeline forecasting, workload balancing, capacity planning, and early risk detection. Understand how AI supports accurate documentation, change control, audit readiness, compliance, and continuous risk monitoring. Develop the leadership skills needed to manage AI-augmented projects through automated reporting, real-time insights, and stronger alignment between teams and stakeholders.

• 1.1 Introduction to Project Management
• 1.2 Project Management Lifecycle
• 1.3 Advanced Project Management Tasks
• 1.4 Project Management Frameworks
• 1.5 Project Manager’s Roles and Responsibilities

• 2.1 Introduction to Artificial Intelligence (AI)
• 2.2 Introduction to Machine Learning (ML)
• 2.3 Neural Networks
• 2.4 AI and ML Applications and Trends
• 2.5 Case Studies on AI and ML Projects

• 3.1 The Importance of Data in Artificial Intelligence
• 3.2 Data Analysis Techniques
• 3.4 Applying Data Insights to Project Decisions
• 3.5 Tools for Data Visualization and Reporting
• 3.6 Challenges and Best Practices

• 4.1 AI in Risk Management – An Introduction
• 4.2 AI for Risk Mitigation and Response
• 4.3 AI for Financial and Resource Risk Management
• 4.4 AI in Risk Management: The Future Scope
• 4.5 Case Study – AI-based Project Risk Management

• 5.1 Introduction to Work Breakdown Structure (WBS)
• 5.2 AI for WBS Creation
• 5.3 AI in Project Scheduling
• 5.4 AI for Resource-Constrained Scheduling
• 5.5 Case Studies: AI-based WBS and AI Algorithms for Project Scheduling

• 6.1 Introduction to AI in Budgeting
• 6.2 AI for Estimating Costs and Budget Allocation
• 6.3 AI for Budget Optimization
• 6.4 Future of AI in Project Budgeting
• 6.5 Case  Study:  AI  Algorithms  for  Project  Scheduling, AI- Based Model for Estimating Costs and Budget Allocation

• 7.1 Introduction to AI in Human Resource Planning
• 7.2 AI for Workforce Allocation
• 7.3 AI in Skill Matching and Employee Performance Analysis
• 7.4 The Future of AI in Human Resource Planning
• 7.5 Case Studies: Designing AI-Based Models for HR Planning

• 8.1 Introduction to Stakeholder Management and AI
• 8.2 Identifying and Categorizing Stakeholders Using AI
• 8.3 Stakeholder Conflicts Management with AI
• 8.4 Ethics and Future Prospects in AI-based Stakeholder Management
• 8.5 Case Studies: AI Tools for Stakeholder Management

• 9.1 Introduction to Project Monitoring and AI
• 9.2 AI-based Tools for Monitoring Project Progress
• 9.3 AI for Risk Monitoring
• 9.4 Case Studies: AI Tools for Project Monitoring

• 10.1 Current State of AI in Project Management
• 10.2 Ethical Considerations in AI-Based Project Management
• 10.3 Technical Challenges in AI Integration

• 1. Understanding AI Agents
• 2. How Does an AI Agent Work
• 3. Applications and Trends of AI Agents in Project Management
• 4. Core Characteristics of AI Agents
• 5. Significance of AI Agents in Project Management
• 6. Types of AI Agents
• 7. Case Study-AI Agents for Agile Project Delivery – Atlassian in Action
• 8. Hands-On Activity

• Python for Project Analytics
• Machine Learning Libraries for Project Insights (Scikit-learn, TensorFlow)
• Project Data Handling Tools (Pandas, NumPy)
• Visualization Platforms for Project Dashboards (Power BI, Tableau)
• Project Data Storage using SQL & NoSQL Databases
• APIs for Project and Workflow Integration
• Cloud Platforms for AI-Enabled Project Management (AWS & Azure Services)
• OpenAI & LangChain for AI-Assisted Project Tools

Online proctored exam included, with one free retake.
Exam format:

50 questions, 70% passing, 90 minutes, online proctored exam

Access to all materials and exams is provided for 365 days after delivery.

Instructor-led OR Self-paced course + Official exam + Digital badge