AI Project Management
A free, structured introduction to the PMI-CPMAI methodology, covering why AI projects fail, the CPMAI mindset, the six phases, and key governance topics.
Learn from: the original creator. Original content published on YouTube. DigitalSkillX organizes these public resources into a structured learning path. DigitalSkillX does not claim ownership or partnership.
Now playing: The 6 CPMAI Phases Explained
This learning path is based on the PMKinetic PMI-CPMAI Crash Course playlist. It provides a high-level, exam-aligned overview of the PMI Cognitive Project Management for AI methodology. You will learn why traditional project management fails in AI projects, how the CPMAI mindset works, what the six phases are and why they are iterative, and how CPMAI compares to Agile and CRISP-DM. The path also covers essential governance topics like trustworthy AI, ethics, privacy, security, bias, transparency, and explainability. It is designed to build the correct mindset and prepare you for deeper study, but it does not replace full exam preparation.
Self-paced learners who want a clear free path with creator-credited YouTube lessons.
Lesson 1: PMI-CPMAI Foundation: Why AI Projects Fail
This foundational video explains what PMI-CPMAI is and why most AI projects fail, emphasizing that the root cause is rarely technology. It introduces the management gap CPMAI was designed to solve and how PMI expects AI projects to be managed.
Open on YouTubeLesson 2: Why Traditional PM Fails in AI Projects
This video explains why traditional project management breaks down in AI projects. It highlights how assumptions of predictability fail, and how uncertainty, data, and experimentation change project control.
Open on YouTubeLesson 3: The CPMAI Mindset PMI Expects
The CPMAI mindset is about how you think and make decisions under uncertainty, not just tools or algorithms. This video explains that learning is a formal project output in AI initiatives and how PMI expects that mindset to be applied.
Open on YouTubeLesson 4: The 6 CPMAI Phases Explained
PMI-CPMAI is built around six phases, but they are not linear steps. This video explains what the six phases are, why they are not a simple sequence, and how they interact in real AI projects to reduce uncertainty and risk.
Open on YouTubeLesson 5: Why CPMAI Is Iterative, Not Linear
AI projects cannot be managed with linear, end-to-end planning because too many critical elements are unknown at the start. This video explains why iteration is required, what iteration means in CPMAI, and which signals trigger iteration.
Open on YouTubeLesson 6: CPMAI vs CRISP-DM vs Agile
This video compares CPMAI with CRISP-DM and Agile, explaining what truly changes when managing AI initiatives with different approaches. It covers why AI projects do not behave like predictable delivery projects and what CRISP-DM does well.
Open on YouTubeLesson 7: Trustworthy AI in AI Governance
Trustworthy AI is about whether an AI system can be trusted to influence real decisions, not just accuracy. This video explains what Trustworthy AI means in PMI-CPMAI, why PMI treats trust as a project responsibility, and why trust must be designed.
Open on YouTubeLesson 8: Ethical AI vs Responsible AI
Ethical AI and Responsible AI are often used as synonyms, but in PMI-CPMAI they are distinct. This video clarifies what each focuses on and why PMI separates these concepts, and how CPMAI uses ethics and responsibility.
Open on YouTubeLesson 9: Data Privacy in AI Projects
Data privacy in AI projects is a core project management responsibility, not just a legal or technical concern. This video explains why data privacy is a critical project risk and what it means beyond encryption.
Open on YouTubeLesson 10: AI Security Basics for Project Managers
AI security is not just technical; it is a management responsibility. This video covers access control in AI systems, why logging is essential for visibility and governance, and other security basics for project managers.
Open on YouTubeLesson 11: Why AI Systems Become Biased
Bias in AI does not start in the algorithm; it starts in data selection, labeling decisions, sampling strategy, and model evaluation. This video explains where bias really begins and why accuracy alone is misleading.
Open on YouTubeLesson 12: Explainability vs Interpretability
Transparency in AI projects is a governance requirement under PMI CPMAI. This lesson covers what transparency means, the required artifacts across all six phases, how audit trails protect AI initiatives, and why missing documentation is risky.
Open on YouTubeLesson 13: Trustworthy AI Exam Traps
This video focuses on common Trustworthy AI exam traps that cause candidates to fail the PMI CPMAI exam. It clarifies the differences between explainability and interpretability and why black-box models are problematic from a governance perspective.
Open on YouTubeLesson 14: Defining the AI Problem
Many AI projects fail because teams start building models before clearly defining the problem. This lesson teaches how to define the right AI problem before model development, a concept essential in CPMAI's Business Understanding phase.
Open on YouTubeLesson 15: Business Objectives vs AI Objectives
This video explains the difference between Business Objectives and AI Objectives, a key concept in CPMAI and a common reason candidates fail the exam. It clarifies how these objectives differ and why they matter.
Open on YouTubeLesson 16: AI Feasibility Analysis for Project Managers
This video covers AI feasibility analysis for project managers, explaining how to assess whether an AI project is viable before committing resources. It is part of the CPMAI methodology and essential for exam preparation.
Open on YouTubeLesson 17: Transparency Artifacts PMI Expects
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