Opleiding: PMI Certified Professional in Managing AI (PMI-CPMAI)® Exam Prep E-Learning Course & Certification [PMI-CPMAI-E]

OVERVIEW

Your license to lead AI. With PMI-CPMAI™, you’ll gain the tools to build with AI effectively, giving you the playbook to secure AI success.

Whether you’re already delivering AI initiatives or eager to start, this certification gives you the structure and credibility to turn innovation into measurable, lasting value.

The 21-hour PMI-CPMAI™ Exam Prep Course provides the knowledge and skills to pass the exam and manage AI projects effectively.

Organized around the six CPMAI methodology phases, it uses scenario-based exercises, case studies and a downloadable workbook to help you apply concepts immediately. The self-paced format includes multimedia content, a guided review of Exam Content Outline (ECO) references, and independent study activities—so you can learn at your own pace while building a strong understanding of the material.

Included in this Bundle

PMI-CPMAI™ Certification + PMI-CPMAI™ Exam Prep Course

Updated 8/2026

OBJECTIVES

After completing this course, you will be able to:

• Align AI projects with business needs and define clear project goals.
• Identify and manage data requirements for AI projects.
• Prepare and manage data for AI applications.
• Develop and deliver AI solutions using iterative approaches.
• Test and evaluate AI systems for reliability and performance.
• Operationalize AI responsibly and manage governance.
• Apply the CPMAI methodology to effectively manage AI projects.
• Prepare for the PMI-CPMAI™ certification exam.

CONTENT

1. The Need for AI Project Management
   Why AI projects struggle, iterative delivery, and ethical and effective outcomes.

2. Matching AI with Business Needs (Phase I)
   Aligning AI solutions with business needs, assessing feasibility, defining ROI, and setting project scope.

3. Identifying Data Needs for AI Projects (Phase II)
   Data selection, compliance, and infrastructure requirements.

4. Managing Data Preparation Needs for AI Projects (Phase III)
   Data quality, augmentation, and compliance controls.

5. Iterating Development and Delivery of AI Projects (Phase IV)
   Building and validating machine learning and generative AI models.

6. Testing & Evaluating AI Systems (Phase V)
   Testing and monitoring AI models, addressing drift, and ensuring reliable and explainable results.

7. Operationalizing AI (Phase VI)
   Responsible AI implementation, governance, and continuous improvement.