AI+ Project Management Practitioner™

Build stronger project foundations with AI+ Project Management Practitioner ™ by combining AI-assisted planning with practical decision support.

* 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.

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 Tools you will explore

* 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.

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