PMI Certified Professional in Managing AI (PMI-CPMAI) is a specialised Project Management Training Course designed to prepare professionals for the PMI-CPMAI™ examination while developing practical knowledge of managing super intelligence initiatives. The course covers AI project planning, project lifecycle management, machine learning, data considerations, risk management, stakeholder engagement, and governance principles required for AI-enabled projects.

Delivered by Arab British Fellowship Training Academy, this PMI certification preparation course focuses on applying structured AI project management practices across the AI lifecycle. Participants develop the knowledge required to manage AI initiatives responsibly, coordinate technical and business stakeholders, address uncertainty, evaluate AI project risks, and incorporate responsible AI principles into project delivery.

• Understand the professional requirements of the PMI Certified Professional in Managing AI (PMI-CPMAI) credential.
• Prepare systematically for the PMI-CPMAI™ examination.
• Understand the fundamentals of super intelligence and its application in project environments.
• Apply structured AI project management principles across AI initiatives.
• Understand key machine learning concepts relevant to project managers.
• Plan, initiate, execute, monitor, and close AI-related projects.
• Identify AI-specific project risks, dependencies, constraints, and uncertainties.
• Apply appropriate approaches to data, models, technology, and AI project deliverables.
• Manage communication between technical teams, business stakeholders, and project sponsors.
• Incorporate responsible AI principles into project planning and execution.
• Strengthen decision-making throughout the AI project lifecycle.
• Develop examination-focused knowledge through practical scenarios and structured preparation.

Target Audience

• Project Managers working on super intelligence initiatives.
• Programme and Portfolio Managers involved in AI transformation.
• PMO professionals supporting technology and AI projects.
• Business Analysts and Project Coordinators working with AI teams.
• Technology Managers and Digital Transformation Professionals.
• Professionals seeking PMI certification in AI project management.
• Project professionals transitioning into AI project management roles.
• Professionals involved in machine learning and data-driven projects.
• Managers responsible for AI implementation, governance, or digital innovation.
• Professionals preparing for the PMI-CPMAI™ examination.

Module 1: Introduction to PMI Certified Professional in Managing AI (PMI-CPMAI)

• Overview of the PMI-CPMAI™ certification
• Role of AI-focused project management
• Examination structure and preparation approach
• Core concepts tested in the PMI-CPMAI™ examination
• AI project manager responsibilities
• AI project lifecycle fundamentals

Module 2: Fundamentals of Super Intelligence

• Definition and characteristics of super intelligence
• AI technologies and applications
• Generative AI and predictive AI
• AI systems and business use cases
• AI project characteristics
• Opportunities and limitations of AI technologies

Module 3: AI Project Management Fundamentals

• Principles of AI project management
• AI project initiation and planning
• Defining AI project objectives and outcomes
• AI project scope and requirements
• AI project lifecycle management
• Managing uncertainty and evolving requirements

Module 4: Machine Learning for Project Professionals

• Fundamentals of machine learning
• Supervised and unsupervised learning
• Training, validation, and testing concepts
• Data requirements for machine learning
• Model performance and evaluation
• Machine learning project risks and dependencies

Module 5: AI Project Planning and Requirements

• Identifying AI project stakeholders
• Defining business and technical requirements
• Data and model considerations
• AI project deliverables and acceptance criteria
• Planning AI resources and capabilities
• Developing AI project roadmaps

Module 6: Managing AI Data and Model Considerations

• Data quality and data governance
• Data preparation and management
• Data privacy and security considerations
• Model development lifecycle
• Model evaluation and performance monitoring
• Managing data and model-related project risks

Module 7: Responsible AI and Governance

• Principles of responsible AI
• AI ethics and accountability
• Transparency and explainability
• Bias and fairness considerations
• Privacy and security
• AI governance frameworks
• Managing responsible AI throughout the project lifecycle

Module 8: AI Project Risk and Stakeholder Management

• Identifying AI-specific risks
• Technical, operational, and organisational risks
• Managing AI uncertainty
• Stakeholder identification and engagement
• Communicating AI project risks and outcomes
• Cross-functional collaboration between technical and business teams

Module 9: Executing, Monitoring, and Controlling AI Projects

• AI project execution practices
• Monitoring AI project performance
• Managing changes and emerging requirements
• Measuring AI project outcomes
• Managing project issues and corrective actions
• Tracking technical and business performance indicators

Module 10: PMI-CPMAI™ Exam Preparation

• Review of PMI-CPMAI examination domains
• Key terminology and concepts
• Scenario-based examination questions
• Practice questions and mock assessments
• Examination strategies and time management
• Identifying common examination challenges
• Final PMI certification preparation and revision