AI initiatives fail due to:

• Poor business alignment

• Unclear ownership

• Ethical and regulatory risks

• Data and model governance gaps

• Treating AI like traditional IT projects

PMI-CPMAI™ equips professionals to manage AI differently — as adaptive, learning systems.

This PMI-CPMAI™ Exam Prep Course is structured to help you manage AI initiatives across the full lifecycle, aligned with the official PMI Exam Content Outline (ECO).

Module 1: The Need for AI Project Management

• Why AI initiatives fail despite advanced technology

• How AI differs from traditional IT and software projects

• PMI's value-driven, iterative approach to managing AI

• Foundations of responsible and trustworthy AI management

Module 2: Matching AI with Business Needs (Phase I)

• Identifying AI-appropriate business problems

• Aligning AI initiatives with organizational strategy

• Assessing feasibility, constraints, and readiness

• Defining success criteria, benefits, and ROI

📌 Aligned with ECO Domain: Identify Business Needs and Solutions (26%)

Module 3: Identifying Data Needs for AI Projects (Phase II)

• Determining data requirements for AI solutions

• Evaluating data availability, quality, and relevance

• Understanding data governance, privacy, and compliance

• Building data foundations to support AI initiatives

📌 Aligned with ECO Domain: Identify Data Needs (26%)

Module 4: Managing Data Preparation Needs for AI Projects (Phase III)

• Transforming raw data into AI-ready inputs

• Managing data quality, augmentation, and labeling

• Addressing compliance, security, and data controls

• Managing risks related to data bias and integrity

📌 Aligned with ECO Domain: Identify Data Needs (26%)

Module 5: Iterating Development and Delivery of AI Projects (Phase IV)

• Managing AI model development at a leadership level

• Understanding model types, including generative AI

• Supporting iterative and adaptive delivery approaches

• Validating models against defined business objectives

📌 Aligned with ECO Domain: Manage AI Model Development and Evaluation (16%)

Module 6: Testing & Evaluating AI Systems (Phase V)

• Interpreting AI performance metrics (manager's view)

• Identifying issues such as bias, drift, and unreliability

• Ensuring AI outputs are explainable and trustworthy

• Making informed go/no-go decisions

📌 Aligned with ECO Domain: Manage AI Model Development and Evaluation (16%)

Module 7: Operationalizing AI (Phase VI)

• Deploying AI solutions responsibly

• Managing AI governance and controls

• Supporting adoption and change management

• Monitoring performance and enabling continuous improvement

📌 Aligned with ECO Domains:
Support Responsible and Trustworthy AI Efforts (15%)
Operationalize AI Solution (17%)

Key Takeaway for Learners

By completing this course, you will be able to:

✔ Manage AI initiatives end-to-end
✔ Align AI solutions with business value
✔ Govern AI responsibly and ethically
✔ Make confident AI decisions without coding
✔ Clear the PMI-CPMAI™ certification exam

This course is designed for professionals who manage AI initiatives — not for those who build AI models. 

This exam prep course is ideal for:

• Project, Program & Portfolio Managers

• Product Managers & Owners

• Business & Systems Analysts

• Digital Transformation Leaders

• PMPs®, PgMP®, PfMP®, Agile professionals

• Consultants & AI initiative sponsors

• CXOs and senior decision-makers

✔ No AI coding or data science background required

Domain 1: Support Responsible and Trustworthy AI Efforts (15%)

After completing the course, learners will be able to:

1. Explain principles of responsible and trustworthy AI, including ethics, transparency, and accountability
   (Bloom: Understand)
   🔗 ECO Alignment: Supporting ethical, responsible, and trustworthy AI efforts

2. Identify ethical, legal, and compliance risks associated with AI initiatives
   (Bloom: Analyze)
   🔗 ECO Alignment: Identifying AI risks related to bias, fairness, privacy, and regulation

3. Evaluate AI initiatives against organizational governance and responsible AI guidelines
   (Bloom: Evaluate)
   🔗 ECO Alignment: Ensuring AI solutions align with governance and trust requirements

Domain 2: Identify Business Needs and Solutions (26%)

After completing the course, learners will be able to:

1. Identify business problems that are appropriate candidates for AI solutions
   (Bloom: Analyze)
   🔗 ECO Alignment: Identifying AI opportunities based on business needs

2. Analyze organizational readiness, constraints, and feasibility for AI adoption
   (Bloom: Analyze)
   🔗 ECO Alignment: Assessing feasibility and alignment of AI solutions

3. Define measurable success criteria, value metrics, and expected outcomes for AI initiatives
   (Bloom: Apply)
   🔗 ECO Alignment: Defining value and success measures for AI solutions

Domain 3: Identify Data Needs (26%)

After completing the course, learners will be able to:

1. Identify data requirements necessary to support proposed AI solutions
   (Bloom: Analyze)
   🔗 ECO Alignment: Identifying data needs to enable AI initiatives

2. Assess data quality, availability, and suitability from a managerial perspective
   (Bloom: Evaluate)
   🔗 ECO Alignment: Evaluating data readiness for AI use

3. Determine data governance, privacy, and security considerations impacting AI initiatives
   (Bloom: Apply)
   🔗 ECO Alignment: Addressing data governance and compliance needs

Domain 4: Manage AI Model Development and Evaluation (16%)

After completing the course, learners will be able to:

1. Describe AI model development and evaluation processes at a conceptual level
   (Bloom: Understand)
   🔗 ECO Alignment: Managing AI model development activities

2. Interpret AI performance metrics to support decision-making
   (Bloom: Analyze)
   🔗 ECO Alignment: Evaluating AI models against defined criteria

3. Evaluate risks such as bias, overfitting, and model drift during AI development
   (Bloom: Evaluate)
   🔗 ECO Alignment: Identifying and managing AI model risks

Domain 5: Operationalize AI Solution (17%)

After completing the course, learners will be able to:

1. Apply governance and control mechanisms to support responsible AI deployment
   (Bloom: Apply)
   🔗 ECO Alignment: Supporting operationalization of AI solutions

2. Analyze organizational change and adoption impacts of AI implementations
   (Bloom: Analyze)
   🔗 ECO Alignment: Supporting stakeholder adoption and integration

3. Monitor AI solutions post-deployment to support continuous improvement
   (Bloom: Apply)
   🔗 ECO Alignment: Monitoring and optimizing AI solutions over time

Exam Readiness Outcome (Cross-Domain)

1. Apply PMI terminology, principles, and value-delivery mindset to scenario-based AI management questions
   (Bloom: Apply)
   🔗 ECO Alignment: All domains — scenario-based decision making