PMI-CPMAI Exam | AI Project Management Training | Certified Practitioner in AI Project Management

The PMI-CPMAI™ e-Learning Program is a self-paced training solution designed to help you prepare effectively for the Certified Practitioner in AI Project Management (PMI-CPMAI™) certification. Built around PMI’s latest standards and the CPMAI™ Exam Content Outline, this program provides structured, high-quality recorded lessons that guide you through the essential knowledge areas needed to lead AI-driven projects responsibly and successfully.

Whether you are a traditional project manager looking to understand AI-driven delivery, or a data/AI professional seeking a formal project management framework, this program offers the flexibility to learn anytime, revisit concepts, and progress at your own pace. With comprehensive coverage of responsible AI, ethical considerations, AI lifecycle management, and data preparedness, you will gain the skills required to confidently attempt the PMI-CPMAI™ exam and elevate your career in AI-enabled project environments.

Program Highlights

• Self-Paced e-Learning program
Access structured recorded videos and lessons that follow PMI’s CPMAI™ exam blueprint, allowing you to learn on your own schedule without attending live classes.

• AI Project Management Frameworks
Gain clarity on PMI’s guidelines for Responsible AI, AI readiness, foundational AI concepts, and structured approaches to managing AI initiatives.

• Practical AI Use Cases & Examples
Understand how AI methods—including machine learning, automation, and advanced analytics—apply in real-world project scenarios, improving decision-making and delivery outcomes.

• Flexible Anytime Access
Revisit recordings as often as needed and progress at a comfortable pace, ideal for busy professionals balancing multiple commitments.

Key Learning Outcomes

• Foundations of AI Project Management & the PMI-CPMAI™ Certification
Understand the exam structure, certification requirements, and the core principles behind AI technologies such as Machine Learning, data pipelines, and model evaluation.

• AI Readiness & Data Fundamentals
Learn how to assess an organization’s AI maturity, identify the right data needs, prepare datasets, and evaluate data quality to support AI initiatives.

• AI Project Lifecycle & Delivery Approach
Master the complete lifecycle of an AI initiative—from business problem definition and data exploration to model development, validation, deployment, and continuous monitoring.

• Responsible, Ethical & Human-Centered AI
Gain insights into PMI’s Responsible AI guidelines, ethical considerations, bias mitigation, transparency, trust, and human oversight in AI systems.

• Collaboration Across Technical & Business Teams
Develop strategies for communicating AI requirements, benefits, and risks to technical experts, executives, customers, and end-users to ensure alignment and adoption.

• Governance, Risk & Value Management in AI Projects
Learn how to establish governance practices for AI initiatives, identify AI-specific risks, ensure compliance with responsible AI standards, and track measurable business value.

What you will learn

• Module 1 – The Need for AI Project Management
Understand why many AI projects struggle and learn how iterative, phased delivery improves outcomes. Gain clarity on how the CPMAI™ methodology supports ethical, responsible, and effective AI project execution.

• Module 2 – Matching AI with Business Needs (Phase I)
Learn how to align AI opportunities with real business challenges, assess feasibility, estimate ROI, and establish clear scope and success criteria for AI-driven initiatives.

• Module 3 – Identifying Data Needs for AI Projects (Phase II)
Explore how to identify and select the right data for AI initiatives, ensure compliance with legal and ethical standards, and build the foundational data infrastructure required for the full AI project lifecycle.

• Module 4 – Managing Data Preparation Needs for AI Projects (Phase III)
Develop skills to prepare and refine raw data through cleansing, transformation, labeling, augmentation, and validation, ensuring datasets are fully AI-ready and compliant.

• Module 5 – Iterating Development and Delivery of AI Projects (Phase IV)
Learn how to design, build, and validate AI models—including machine learning, predictive analytics, and generative AI—using an iterative, test-driven development approach.

• Module 6 – Testing & Evaluating AI Systems (Phase V)
Master techniques for evaluating AI model performance, detecting and managing drift, improving reliability, and ensuring outputs remain explainable, trustworthy, and aligned with business goals.

• Module 7 – Operationalizing AI (Phase VI)
Understand how to deploy AI systems into production responsibly, establish governance and risk controls, and plan for continuous improvement, monitoring, and lifecycle maintenance.