AI+ Project Management Practitioner™
AP 2601
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.
Why This Certification Matters
At a Glance: Course + Exam Overview
- Instructor-Led: 5 days (live or virtual)
- Self-Paced: 40 hours of content
Who Should Enroll?
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.
Job Roles & Industry Outlook
AI-Augmented Delivery Professional
Work alongside project leaders using AI-powered dashboards and insights to support planning, coordination, and delivery tracking.
Project Intelligence Associate
Translate project data into meaningful signals using AI-assisted reporting, helping teams anticipate delays and workload challenges.
Operations and Execution Analyst
Apply AI tools to improve task flow, resource visibility, and operational rhythm across ongoing initiatives.
Transformation Program Support Specialist
Assist digital transformation efforts by integrating AI-enabled project workflows into existing delivery and governance structures.
Future-Ready Project Practitioner
Build early expertise in AI-supported project environments, creating a strong foundation for advanced project or transformation roles.
Skills You’ll Gain
- AI-Assisted Project Planning & Estimation
- Intelligent Scheduling and Workload Balancing
- Automated Project Tracking and Reporting
- Predictive Delivery and Risk Signals
- AI-Powered Decision Support for Projects
- Workflow and Process Automation
- Project Data Interpretation and Insights
- Stakeholder Communication Enablement
- Secure Handling of Project Information
- Responsible Application of AI in Project Execution
What You'll Learn
- 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
Tools You'll 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
Prerequisites
- Basic understanding of project management principles and processes.
- Familiarity with project management tools and techniques.
- General knowledge of artificial intelligence concepts (machine learning, predictive analytics, etc.).
- Experience in managing or overseeing projects, preferably in a technical or business context.
- Willingness to learn and apply AI-based tools to enhance project management efficiency.
Exam Details
Duration
90 minutes
Passing Score
70% (35/50)
Format
50 multiple-choice/multiple-response questions
Delivery Method
Online via proctored exam platform (flexible scheduling)
Exam Blueprint:
- Project Management Overview – 10%
- Introduction to AI and ML - 10%
- Data Driven Decision Making - 10%
- AI – Driven Project Risk Management – 10%
- Planning Project Work Breakdown and Structuring and Project Scheduling by AI – 10%
- Effective Project Budgeting Using AI - 10%
- AI for Planning Human Resources - 10%
- Stakeholder Management Using AI - 10%
- AI – Based Project Monitoring - 10%
- Transformative Role of Project Management - 10%
Choose the Format That Fits Your Schedule
What’s Included (One-Year Subscription + All Updates):
- High-Quality Videos, E-book (PDF & Audio), and Podcasts
- AI Mentor for Personalized Guidance
- Quizzes, Assessments, and Course Resources
- Online Proctored Exam with One Free Retake
- Comprehensive Exam Study Guide
Instructor-Led (Live Virtual/Classroom)
- 5 days of intensive training with live demos
- Real-time Q&A, peer collaboration, and hands-on labs
- Led by AI Certified Trainers and delivered through Authorized Training Partners
Self-Paced Online
- 40 hours of on-demand video lessons, e-book, podcasts, and interactive labs
- Learn anywhere, anytime, with modular quizzes to track progress
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Get CertifiedFrequently Asked Questions
Yes, the certification includes hands-on project scenarios using realistic project data. You’ll be able to apply AI-supported planning, tracking, and reporting techniques directly in active project environments.
This course uniquely focuses on applying AI to everyday project workflows—planning, scheduling, monitoring, and risk awareness—rather than abstract theory, making it practical for real project execution.
You’ll work on AI-assisted project planning models, automated status reporting workflows, risk identification scenarios, and intelligent project support tools based on real delivery challenges.
The course combines guided modules, interactive examples, and applied project exercises that reinforce learning through hands-on practice and real-world project use cases.
It builds practical experience with AI-enabled project tools, data-informed decision-making, and automation—skills increasingly expected in modern project coordination and management roles.
