Artificial intelligence is changing how organizations operate, innovate, and deliver value, and successfully managing AI initiatives requires a different approach than traditional technology projects.

The PMI Certified Professional in Managing AI (PMI-CPMAI)™ Exam Prep Course prepares professionals to understand and apply the principles behind successful AI initiatives while building the knowledge needed to pursue the PMI-CPMAI™ certification.

Through practical instruction, examples, exercises, and exam-focused review, participants will explore the CPMAI methodology and learn how to guide AI initiatives from initial business need through data preparation, solution development, testing, deployment, governance, and continuous improvement.

Whether you are already working with AI or preparing to take on greater responsibility for AI-enabled initiatives, this course provides a structured framework for turning AI opportunities into measurable business outcomes.

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What you'll learn

  • Explain why AI initiatives require a specialized, iterative approach to project and product management.
  • Understand the CPMAI methodology and how its six phases guide an AI initiative from idea through operation.
  • Identify business problems and opportunities that are appropriate for AI solutions.
  • Evaluate AI initiatives based on business value, feasibility, expected outcomes, and risk.
  • Identify the data required to support an AI initiative and recognize common data quality, governance, privacy, and compliance considerations.
  • Understand approaches for preparing data for machine learning, generative AI, and other AI solutions.
  • Apply iterative approaches to developing, validating, and improving AI solutions.
  • Understand how AI systems are tested and evaluated for effectiveness, reliability, explainability, and alignment with business objectives.
  • Recognize ethical, responsible AI, governance, and risk considerations throughout the AI lifecycle.
  • Understand what is required to operationalize and continuously improve AI solutions.
  • Apply CPMAI concepts to realistic business scenarios.
  • Reinforce key concepts and terminology covered on the PMI-CPMAI™ certification exam.
  • Develop a focused strategy for continued study and exam preparation.
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This course is designed for professionals who want to strengthen their ability to lead, manage, or contribute to AI initiatives, including:

  • Project and Program Managers
  • Product Managers and Product Owners
  • Agile Practitioners and ScrumMasters
  • PMO and VMO Leaders
  • Business Analysts
  • Transformation and Innovation Leaders
  • Technology and IT Leaders
  • Consultants and Coaches
  • Data and Analytics Professionals
  • Business leaders responsible for AI-enabled initiatives
  • Professionals preparing for the PMI-CPMAI™ certification exam

No prior AI, technical, or project management certification is required to pursue the PMI-CPMAI™ certification. Familiarity with project or product management and basic AI concepts may help participants get even more value from the course.

SAMPLE AGENDA

AI Project Management & the CPMAI Methodology

Explore why AI initiatives differ from traditional technology projects and why many AI efforts struggle to deliver expected outcomes.

Topics include:

  • Characteristics of AI initiatives
  • Common causes of AI project failure
  • Iterative and data-centric approaches to AI
  • Overview of the CPMAI methodology
  • Responsible and ethical AI principles
  • Understanding the AI project lifecycle
  • PMI-CPMAI™ exam structure and preparation strategy

Phase 1: Matching AI with Business Needs

Learn how to start with business value rather than technology and determine whether AI is the right solution for a given opportunity.

Topics include:

  • Defining the business problem
  • Identifying desired outcomes
  • Connecting AI opportunities to organizational strategy
  • Determining whether AI is appropriate
  • Assessing feasibility and constraints
  • Establishing scope and success criteria
  • Understanding expected value and ROI
  • Aligning stakeholders around the initiative

Phase 2: Identifying Data Needs

Explore how to determine what data an AI solution requires and whether the organization has the appropriate data, infrastructure, permissions, and governance in place.

Topics include:

  • Identifying required data
  • Structured and unstructured data
  • Data availability and accessibility
  • Data quality considerations
  • Privacy, security, and regulatory considerations
  • Data governance
  • Infrastructure and architecture considerations
  • Recognizing potential data limitations and risks

Phase 3: Preparing Data for AI

Understand how raw organizational data is transformed into usable inputs for AI systems.

Topics include:

  • Data preparation and preprocessing
  • Cleaning and validating data
  • Addressing incomplete or inconsistent data
  • Data labeling and augmentation
  • Bias within datasets
  • Data quality controls
  • Privacy and compliance requirements
  • Creating AI-ready datasets

Phase 4: Iterative Development & Delivery

Learn how AI solutions are developed through experimentation, iteration, validation, and continuous learning rather than traditional linear delivery.

Topics include:

  • Machine learning and generative AI concepts
  • Selecting appropriate AI approaches
  • Model development and experimentation
  • Prototyping and iterative delivery
  • Training and validating models
  • Human-in-the-loop approaches
  • Collaboration between business and technical teams
  • Managing uncertainty during AI development

Phase 5: Testing & Evaluating AI Systems

Explore how teams determine whether an AI solution is accurate, reliable, useful, explainable, and appropriate for deployment.

Topics include:

  • Evaluating AI performance
  • Testing against business requirements
  • Accuracy and reliability
  • Bias and fairness
  • Explainability and transparency
  • Model drift
  • Risk and failure scenarios
  • Monitoring performance
  • Determining readiness for deployment

Phase 6: Operationalizing AI

Learn what is required to successfully move AI solutions into real-world use and maintain their effectiveness over time.

Topics include:

  • Deploying AI into operational environments
  • AI governance and oversight
  • Responsible AI controls
  • Adoption and change management
  • Performance monitoring
  • Managing model and data drift
  • Continuous improvement
  • Scaling successful AI solutions
  • Managing AI throughout its lifecycle

Exam Prep & Knowledge Review

Conclude the course by reinforcing key CPMAI concepts and preparing participants for continued exam study.

Topics include:

  • Review of the CPMAI methodology
  • Key terminology and concepts
  • Scenario-based questions
  • Applying the methodology to real-world situations
  • Common areas of confusion
  • Exam-focused knowledge review
  • Practice questions and discussion
  • Individual study and preparation strategy

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