PMI Certified Professional in Managing AI (PMI-CPMAI) Training

• Understand why AI projects fail and how iterative, agile and cognitive approaches improve outcomes
• Apply the seven patterns of AI and the layers of trustworthy AI to real initiatives
• Scope AI opportunities, evaluate feasibility, and make data-driven go/no-go decisions (CPMAI Phase I)
• Determine data quality, quantity, privacy and infrastructure needs for AI (CPMAI Phase II)
• Manage data preparation, pipelines, labeling and synthetic data, including for generative AI (CPMAI Phase III)
• Guide model development, validation and generative-AI system building (CPMAI Phase IV)
• Evaluate models against business and technology KPIs and monitor for data and model drift (CPMAI Phase V)
• Operationalize AI with lifecycle management, governance and trustworthy-AI practices (CPMAI Phase VI)

No formal prerequisites are required to attend or to sit for the PMI-CPMAI (CPMAI v7) exam. Familiarity with project delivery concepts (scope, stakeholders, outcomes) and basic data/analytics vocabulary is helpful; no coding is required — technical depth is introduced at a management level with practical examples and exercises. Intended for project managers, product managers, PMO leaders, business analysts, consultants and technology professionals who support or lead AI initiatives.

• Module 1: The Need for AI Project Management
  • Why AI now, the seven patterns of AI, why AI projects fail, the fears and concerns of trustworthy AI, the layers of trustworthy AI, iterative and agile approaches for AI, and cognitive project management in AI.
• Module 2: CPMAI Phase I — Business Understanding
  • Determine the problem and whether AI is a good fit, evaluate AI feasibility, map business problems to AI patterns, make go/no-go decisions, determine ROI and success metrics, scope and schedule the project, and identify team needs and project-specific AI risks.
• Module 3: The Role of Data in AI — CPMAI Phase II
  • Determine data quality and quantity requirements, select data sets, understand data privacy, compliance and access requirements, coordinate data infrastructure, and understand analytics and key data roles.
• Module 4: CPMAI Phase III — Data Preparation
  • Data preparation and pipelines for AI, data quality checks and verification, data transformation and synthetic data, augmentation and labeling, data management for generative AI systems, and trustworthy AI in data preparation.
• Module 5: CPMAI Phase IV — Model Development
  • Machine learning and models, model development and validation, building generative AI systems, and mapping AI development to CPMAI Phase IV.
• Module 6: CPMAI Phase V — Model Evaluation
  • Model evaluation and iteration, model performance and data/model drift, evaluating models against business and technology KPIs, AI system monitoring and management, and explainable and interpretable AI systems.
• Module 7: CPMAI Phase VI — Model Operationalization
  • Moving AI models into operation, AI platforms and infrastructure, operationalizing generative AI, model lifecycle management, AI and model governance, trustworthy AI considerations in operation, the limits of AI, and moving to the next iteration.