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AP 2601
AI+ Project Management Practitioner™
Build stronger project foundations with AI+ Project Management Practitioner ™ by combining AI-assisted planning with practical decision support.
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What is included
Instructor-led OR Self-paced course + Official exam + Digital badge
Duration
- Instructor-Led: 5 days (live or virtual)
- Self-Paced: 40 hours of content
Recommended background
Foundational knowledge of project management practices, familiarity with common project tools, and basic understanding of AI concepts such as machine learning and predictive analytics. Ideal for professionals with project exposure seeking to apply AI to improve project efficiency and delivery.
Examination
50 questions, 70% passing, 90 minutes, online proctored exam
About this certification
- Intelligent Project Operations: Discover how AI enhances planning, scheduling, task prioritization, and progress tracking to reduce manual effort and improve project consistency.
- Predictive Planning & Resource Optimization: Use data-driven insights for timeline forecasting, workload balancing, capacity planning, and early risk detection to keep projects on track.
- Governance, Compliance & Risk Awareness: Understand how AI supports documentation accuracy, change control, audit readiness, and ongoing risk monitoring in project environments.
- Leadership Foundations for AI-Augmented Projects: Build skills to lead teams using AI-enabled workflows, including automated reporting, real-time insights, and improved stakeholder alignment.
Curriculum
Module 1: Project Management Overview+
- 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
Module 2: Introduction to AI and ML+
- 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
Module 3: Data Driven Decision Making+
- 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
Module 4: AI-Driven Project Risk Management+
- 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
Module 5: Planning Project Work Breakdown and Structuring and Project Scheduling by AI+
- 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
Module 6: Effective Project Budgeting Using AI+
- 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
Module 7: AI for Planning Human Resources+
- 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
Module 8: Stakeholder Management Using AI+
- 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
Module 9: AI-based Project Monitoring+
- 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
Module 10: Transformative Role of Project Management+
- 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
Additional Module: AI Agents for Project Management Practitioner+
- 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
Who should enrol
Aspiring Project Managers: Individuals looking to build a strong foundation in project management while gaining exposure to AI-enabled workflows.
Early-Career Project Professionals: Project coordinators, analysts, or junior PMs seeking to enhance planning, tracking, and reporting using AI tools.
Business and Technical Professionals: Professionals involved in project execution who want to understand how AI can support timelines, resources, and risk awareness.
Team Leads and Supervisors: Leaders responsible for overseeing projects who want better visibility and decision support through AI-assisted insights.
Professionals Transitioning into AI-Supported Roles: Individuals aiming to stay relevant as project environments increasingly adopt AI-driven tools and data-supported execution.
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