CERTIFIED PROFESSIONAL IN MANAGING AI

Available Dates & Locations

This course is offered exclusively as In-House or Virtual, with no public sessions currently available – please contact us for inquiries.

COURSE OVERVIEW

This comprehensive course prepares professionals for the PMI Certified Professional in Managing AI (PMI-CPMAI) certification examination, aligned with PMI’s five core AI management domains. Participants will develop a thorough understanding of the need for AI project management, matching AI with business needs, identifying and managing data requirements, iterating AI development and delivery, testing and evaluating AI systems, and operationalizing AI solutions within organizational environments. Grounded in both trustworthy AI principles and iterative project management approaches, this course delivers structured instruction, module-based and domain-based practice questions, keyword mastery sessions, mind map reviews, case study analysis, and full mock exam simulations. Designed for professionals seeking to lead AI initiatives with confidence and earn a globally recognized credential, this course ensures participants are fully equipped to pass the PMI-CPMAI examination on their first attempt and drive responsible, value-generating AI projects across any industry.

COURSE OBJECTIVES

By attending this course, participants will be able to:

Explain why AI project management is essential today, identify the seven patterns of AI, and describe the principles of trustworthy and responsible AI.
Match AI solutions to business problems by evaluating feasibility, determining go or no-go decisions, defining ROI and success metrics, and scoping AI projects effectively.
Identify data needs for AI projects including data quality and quantity requirements, data privacy and compliance considerations, and key data roles and infrastructure needs.
Manage data preparation activities including data pipeline development, data quality checks, data transformation, augmentation, labeling, and synthetic data generation.
Oversee AI model development and delivery by managing machine learning models, model validation, generative AI systems, and iterative development approaches.
Test and evaluate AI systems by assessing model performance, managing data and model drift, applying explainable AI principles, and evaluating against business and technology KPIs.
Operationalize AI solutions by managing model lifecycle, AI platforms, infrastructure, governance frameworks, and trustworthy AI considerations in production environments.
Apply PMI-CPMAI exam tips, critical keywords, and the full exam content outline to answer scenario-based questions accurately and pass the examination on the first attempt.

CERTIFIED PROFESSIONAL IN MANAGING AI — course overview

TARGET COMPETENCIES

AI Project Management Foundations
Business Needs and AI Alignment
Data Identification and Governance
Data Preparation and Management
AI Model Development and Delivery
AI Testing and Evaluation
AI Operationalization and Governance
Exam Strategy and Readiness

This course is designed for a wide range of professionals who are involved in leading, managing, or contributing to artificial intelligence initiatives within their organizations across any industry or sector. It is particularly suited for project managers, program managers, and PMO professionals who are taking on AI-related projects and wish to formalize their understanding of how to manage AI initiatives through a globally recognized PMI credential. Data professionals, AI engineers, machine learning practitioners, and technology managers who work closely with AI systems and want to strengthen their project management and governance capabilities will also benefit significantly from attending. Business analysts, product managers, organizational change leaders, and strategy professionals who are responsible for evaluating AI feasibility, defining business requirements, and overseeing AI deployment will find the course directly applicable to their roles. No prior AI certification is required, and the course is designed to take each participant from foundational AI project management concepts through to full examination readiness.

This course is delivered through instructor-led sessions, module-based and domain-based practice questions, mind map reviews, real-world case study analysis, keyword and definition drills, very important question sessions, full mock exam simulations, and structured debrief discussions, ensuring participants are fully prepared for both the PMI-CPMAI examination and real-world AI project management practice.

Module 1: The Need for AI Project Management

Why AI is transforming project management and why organizations are investing in AI now
The seven patterns of AI and how they apply to different types of organizational challenges
Why AI projects fail and how to address the most common causes of AI initiative failure
Fears, concerns, and the layers of trustworthy AI in project and organizational contexts
Iterative and agile approaches for AI, cognitive project management, and mind map review

Module 2: Matching AI with Business Needs

Determining the problem being solved and evaluating AI feasibility for business use cases
Mapping business problems to AI patterns and determining the AI go or no-go decision
Determining AI project ROI, defining success metrics, and scoping and scheduling AI projects
Identifying AI project team needs and determining project-specific AI risks
Case studies, module summary, mind map review, and practice questions with full explanations

Module 3: Identifying Data Needs for AI Projects

Understanding the role of data in AI and how data drives model performance and outcomes
Determining data quality and quantity requirements and identifying the right data sets for AI projects
Understanding data privacy, compliance, and access requirements across regulatory environments
Coordinating data infrastructure and access needs and identifying key analytics and data roles
Mapping data identification activities to the CPMAI framework, case studies, mind map, and practice questions

Module 4: Managing Data Preparation Needs

Data preparation for AI projects: purpose, principles, and the data preparation workflow
Building and managing the data pipeline across parts one, two, and three
Data quality checks, verification processes, data transformation, and synthetic data generation
Data augmentation, data labeling, and data management for generative AI systems
Trustworthy AI in data preparation, go or no-go decision, CPMAI phase mapping, mind map, and practice questions

Module 5: Iterating AI Development and Delivery

Machine learning fundamentals: models, types, and their application in AI project delivery
Model development processes: training, tuning, and preparing models for validation
Model validation: evaluation approaches, acceptance criteria, and iteration decisions
Building generative AI systems across parts one and two including prompt engineering and architecture
Case studies, go or no-go decision, CPMAI phase mapping, mind map review, and practice questions

Module 6: Testing and Evaluating AI Systems

Model evaluation: evaluation frameworks, metrics, and structured assessment approaches
Model iteration: continuous improvement cycles and performance optimization strategies
Model performance, data drift, and model drift: detection, monitoring, and management
Evaluating models against business and technology KPIs and AI system monitoring and management
Explainable and interpretable AI systems, CPMAI phase mapping, case studies, mind map, and practice questions

Module 7: Operationalizing AI and Exam Readiness

Moving AI models into operation: deployment strategies, platforms, and infrastructure requirements
Ways to interact with AI models, operationalizing generative AI, and model lifecycle management
AI and model governance frameworks and trustworthy AI considerations in operational environments
Understanding the limits of AI and planning for next iteration after CPMAI phase six
PMI-CPMAI exam content outline review, keywords parts one and two, VIP questions, exam registration guidance, and full mock exam debrief