About Course

CPMAI for Project Leaders: A Practical Intro to Managing AI Projects

This Stanton Press course is a guided introduction to CPMAI. the Certified Professional in Managing AI methodology. It is built for project managers, operations leaders, and technical decision makers who keep seeing AI projects start strong and then stall because the data, the scope, or the business case was never nailed down.

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This course supports two learning modalities:

Listen. Each lesson includes audio narration that supports the visual material. Learners can click the play icon at the top of the page to hear the content.
Read and engage. Learners can move through the topics, engage with the material, and complete activities as they progress.

What This Course Covers

The course explains why 70–80% of AI projects fail and shows that most failures come from how the project is managed, not from the underlying technology. Then it introduces CPMAI as a vendor neutral, data centric, iterative approach that fits alongside existing project frameworks and gives you a repeatable way to move from idea to pilot to production.

Across the lessons, learners will:

See the full CPMAI lifecycle from Phase 1: Business Understanding through Phase 6: Operationalization.
Learn how to make an AI go/no go decision using business, data, and implementation feasibility.
Map real world use cases to the seven patterns of AI so they do not pick the wrong pattern and the wrong data strategy.
Separate AI projects from traditional application development and see why AI must be managed like a data project first.
Work with the DIKUW pyramid to decide where AI actually adds value and where BI/reporting is enough.
Understand why up to 80% of AI effort is data preparation and how CPMAI handles it through pipelines, labeling, quality checks, and iteration.
Connect model work to MLOps. model monitoring, drift detection, versioning, and user adoption.
Adopt the CPMAI mindset to think big, start small, and iterate often to reduce risk and build stakeholder confidence.

Assessment

A 20 question quiz is included to reinforce key concepts. AI failure rates, CPMAI phases, data preparation, the DIKUW pyramid, and operationalization.

Outcomes

By the end of this introductory course, learners will be able to:

Explain what CPMAI is and why AI projects need a data centric approach.
Identify the phase based structure of CPMAI and what to accomplish in each step.
Decide whether an AI initiative should move forward using AI go/no go criteria.
Align AI projects to the right AI pattern to avoid scope and data mismatches.
Describe how CPMAI supports ongoing monitoring, retraining, and operationalization.

This intro also points to the deeper CPMAI training and certification path for learners who want to build full AI project management capability under Stanton Press.

See the full CPMAI lifecycle and learn what to accomplish in each of its six phases.
Make a confident AI "go/no-go" decision by assessing business, data, and implementation feasibility.
Align your project to one of the seven patterns of AI to avoid common scope and data mismatches.
Understand why AI projects must be managed as data-centric projects, not traditional software development.
Use the DIKUW pyramid to determine where AI adds real value and where simple reporting is enough.
Learn why data preparation is 80% of the work and how to manage it with data pipelines, labeling, and quality checks.
Describe the fundamentals of MLOps, including model monitoring, drift detection, and versioning.
Adopt the "think big, start small, iterate often" mindset to reduce risk and build stakeholder confidence.

Course Content

Introduction to the Course
Introduces the course's learning options and defines the CPMAI methodology, outlining how it helps prevent the common failures plaguing AI projects.

Lesson 1: Introduction

00:23
Lesson 02: Welcome

03:01

The CPMAI Methodology
Establishes the high failure rate of AI projects and introduces the six-phase, iterative, and data-centric CPMAI framework as the solution, contrasting it with traditional software development approaches.

The Seven Patterns of AI
Details the seven common patterns of AI application, which serve as a critical framework for classifying projects and determining the correct data strategies, technologies, and success metrics from the start.

Phase 1: Business Understanding
Covers the first CPMAI phase, focusing on defining the business problem, assessing project feasibility using the "Go/No-Go" decision, and prioritizing a value-driven pilot (MVP) over a simple proof of concept.

Phase 2: Data Understanding
Explores the second CPMAI phase, detailing how to inventory and assess data requirements using concepts like the DIKUW pyramid, the "V's" of Big Data, and the distinction between structured and unstructured data.

Phase 3: Data Preparation
Details the third and most time-intensive CPMAI phase, focusing on the critical work of designing data pipelines (for both training and inference) and executing the essential tasks of data cleaning, labeling, and transformation.

Phase 4: Model Development
Focuses on the fourth CPMAI phase, covering the selection of appropriate algorithms and tools, the process of training the model, and the importance of aligning the model's complexity with the defined business goals.

Phase 5: Model Evaluation
Covers the fifth CPMAI phase, emphasizing continuous evaluation of both technical performance and business value through MLOps, model iteration planning, and a strong focus on driving end-user adoption.

Phase 6: Operationalization
Details the final CPMAI phase of operationalization, which involves deploying the evaluated model into the live production environment and establishing processes for managing its performance, cost, and governance.

Course Conclusion
Concludes the course by reinforcing the core CPMAI philosophy of "think big, start small, and iterate often" to ensure incremental value delivery and long-term project success.

About Course

CPMAI for Project Leaders: A Practical Intro to Managing AI Projects

Choose Your Experience

What This Course Covers

Assessment

Outcomes

What Will You Learn?

Course Content

Introduction to the Course Introduces the course's learning options and defines the CPMAI methodology, outlining how it helps prevent the common failures plaguing AI projects.

Lesson 1: Introduction

Lesson 02: Welcome

The CPMAI Methodology Establishes the high failure rate of AI projects and introduces the six-phase, iterative, and data-centric CPMAI framework as the solution, contrasting it with traditional software development approaches.

Lesson 03: AI Success Rate

Lesson 04: Keys to Successful AI Projects

Lesson 05: CMPAI Methodology

Lesson 06: AI Success Depends on Data

Lesson 07: Traditional Project Approaches Don’t Work Well for AI

This is a quiz title

The Seven Patterns of AI Details the seven common patterns of AI application, which serve as a critical framework for classifying projects and determining the correct data strategies, technologies, and success metrics from the start.

Lesson 08: Seven Patterns of AI

Phase 1: Business Understanding Covers the first CPMAI phase, focusing on defining the business problem, assessing project feasibility using the "Go/No-Go" decision, and prioritizing a value-driven pilot (MVP) over a simple proof of concept.

Lesson 09: Business Understanding

Lesson 10: AI Go/No-Go Decision

Lesson 11: The Proof of Concept vs. the Pilot

Lesson 12: Phase 1 Questions

Phase 2: Data Understanding Explores the second CPMAI phase, detailing how to inventory and assess data requirements using concepts like the DIKUW pyramid, the "V's" of Big Data, and the distinction between structured and unstructured data.

Lesson 13: Phase 2: Data Understanding

Lesson 14: The DIKUW Pyramid

Lesson 15: The V’s of Big Data

Lesson 16: Unstructured and Structured Data

Lesson 17: Phase 2 Questions

Lesson 18: Common Pitfalls

Phase 3: Data Preparation Details the third and most time-intensive CPMAI phase, focusing on the critical work of designing data pipelines (for both training and inference) and executing the essential tasks of data cleaning, labeling, and transformation.

Lesson 19: Phase 3: Data Preparation

Lesson 20: The Data Pipeline

Lesson 21: Data Preparation Considerations

Lesson 22: Phase 3 Questions

Phase 4: Model Development Focuses on the fourth CPMAI phase, covering the selection of appropriate algorithms and tools, the process of training the model, and the importance of aligning the model's complexity with the defined business goals.

Lesson 23: Phase 4: Model Development

Lesson 24: Phase 4 Questions

Lesson 25: Phase 4 Questions

Phase 5: Model Evaluation Covers the fifth CPMAI phase, emphasizing continuous evaluation of both technical performance and business value through MLOps, model iteration planning, and a strong focus on driving end-user adoption.

Lesson 26: Phase 5: Model Evaluation

Lesson 27: The Rise of MLOps

Lesson 28: Iterate Models

Lesson 29: Adoption

Lesson 30: Phase 5 Questions

Phase 6: Operationalization Details the final CPMAI phase of operationalization, which involves deploying the evaluated model into the live production environment and establishing processes for managing its performance, cost, and governance.

Lesson 31: Operationalization

Lesson 32: Phase 6 Considerations

Course Conclusion Concludes the course by reinforcing the core CPMAI philosophy of "think big, start small, and iterate often" to ensure incremental value delivery and long-term project success.

Lesson 33: Think Big

Lesson 34: Your CPMAI Journey

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