Project Management for Delivering AI Projects

Name: Project Management for Delivering AI Projects
Rating: 4.6 (32 reviews)

Lead AI Projects: Master the 6 Phases of CPMAI, 7 Patterns of AI, Data Strategy, Responsible/Trustworthy AI Governance

Created byRavindra Gajendragadkar

Last updated 4/2026

English

What you'll learn

Project Differentiation: Understand how AI initiatives differ from traditional Software and RPA projects to avoid common delivery failures.
The AI Strategic Lens: Distinguish between the 7 Patterns of AI (Predictive, Recognition, Autonomous, etc.) to choose the right project strategy.
Governance & Risk: Mitigate "Hidden Risks" including Ethical Bias, Regulatory Compliance, and Model Drift.
Technical Literacy for Leaders: Master ML concepts like Overfitting, Underfitting, and Decay to effectively challenge technical teams and vendors.
Lifecycle Mastery: Implement the CPMAI Lifecycle from initial Business Understanding to full Model Operationalization.
Value Realization: Inputs to Design dashboards and oversight rituals that track Business Value rather than just technical accuracy.
Decision-Making Literacy: Develop an AI Fluency mindset to distinguish between cognitive AI needs and simpler non-cognitive automation.
AIaaS Differentiation: Master the "Buy vs Build" strategy. Learn to manage Vendor selection,Governance, and learn commercial apects for scalable AI ROI.

Course content

7 sections • 39 lectures • 4h 27m total length

Introduction to the course5:16

Introduction to Human & Artificial Intelligence6:54
Introduces human vs AI intelligence to set the foundation for project management. Highlights opportunities and limitations essential for leaders.
What is Artificial Intelligence?3:48
Explains AI definitions, scope, and types—Narrow, General, Super-intelligent. Clarifies what AI can and cannot achieve.
Artificial vs Augmented Intelligence5:29
Contrasts Artificial and Augmented Intelligence. Emphasizes AI as a tool to enhance, not replace, human capability
Intro to Human and AI

Human Intelligence vs. Artificial Intelligence6:59
Outlines inherent AI limitations vs human intelligence. Shows where AI adds value and where human judgment is irreplaceable.
How AI Systems Learn: Machine Learning & Beyond6:25
Shows how AI systems learn using supervised, unsupervised, and reinforcement learning. Distinguishes algorithms from models.
Relationship between AI,ML & Data Science4:03
In this lecture you will learn how AI,ML and Data science relationship in order to develop of the AI Model
Role of Data in AI Projects10:40
Explains the pivotal role of data in AI projects. Reviews quality, governance, and ethical implications for outcomes.
Foundation of AI Projects

Cognitive (AI) Project Management9:13
Introduces Cognitive Project Management and how AI projects differ from traditional ones. Highlights evolving scope and delivery.
What is an AI Pattern?8:06
Predictive Pattern5:56
Covers Predictive Analytics pattern for anticipating outcomes. Focuses on business value, data quality, and decision-making.
Hyperpersonalization Pattern4:49
Explains Hyper-Personalization in AI. Highlights balancing real-time personalization with trust, ethics, and scalability.
Recognition Pattern5:22
Covers Recognition systems in AI. Discusses data quality, accuracy, and ethics to ensure reliable and fair outcomes.
Conversation Pattern4:36
Explores Conversational AI. Focuses on design, monitoring, and ethical safeguards for human-like interactions.
Goal Driven System Pattern4:54
Explains Goal-Driven Systems in AI. Shows why aligning AI actions with objectives is key for efficiency and outcomes.
Autonomous Systems Pattern4:51
Covers Autonomous AI systems. Discusses benefits, risks, and governance needed for safe, independent operation.
Patterns & Anomalies Pattern4:23
Explains Anomaly Detection in AI. Highlights risk spotting, proactive actions, and adaptive monitoring.
What is an AI Project & Seven AI Patterns

Data is the key9:07
Shows why reliable data underpins AI success. Covers quality, governance, and trust-building practices.
Guideline for Metrics w.r.t. AI Patterns5:46
This lecture provides generic guidelines about what could be essential metrics to be used for a particular AI Pattern
Business_Alignment and Problem Framing for AI Needs2:57
Explains why AI must align with business strategy. Shows how framing ensures measurable and relevant impact.
Explainability4:25
Covers interpretability and explainability. Highlights why clarity builds trust and supports adoption.
ML Concepts to Evaluate Model Performance4:20
This lecture addresses the concerns of How to manage an Over-fitting or Under-fitting Model and its impact of Business performance
Model Accuracy vs Business Value11:04
Explains why accuracy alone is not enough. Emphasizes evaluating AI through business impact and outcomes.
Change Management and Stakeholder Engagement2:15
Covers change management and stakeholder engagement. Highlights building trust, reducing resistance, and adoption.
Ethical AI and Bias Mitigation2:32
Focuses on ethical AI and bias mitigation. Encourages leaders to ensure fairness and responsible practices.
Regulatory Compliance Challenges2:14
Explains regulatory challenges in AI projects. Highlights compliance as critical for trust and sustainability.
Key Success Factors and Challenges in AI Projects

Setting The Stage for AI projects3:49
Covers starting AI projects with clarity and realistic expectations. Frames problems for effective outcomes.
Overview of AI Project Lifecycle4:38
Provides the overview of AI Project Management Phases
Business Understanding12:01
Shows why business understanding comes first. Covers defining objectives, value, and alignment before data work.
Data Understanding15:19
Explains importance of data understanding in AI. Highlights patterns, gaps, and aligning insights with goals.
Data Preparation11:40
Covers structured data preparation for AI projects. Shows how cleaning and transforming inputs builds reliability.
Model Development15:00
Explains disciplined model development. Highlights experimentation, iteration, and validation as key practices.
Model Evaluation8:45
Covers rigorous evaluation of AI models. Explains accuracy, reliability, and impact testing for real value.
Model Operationalization12:48
Explains operationalization phase of AI. Covers monitoring, scalability, and alignment for sustained outcomes.
Lifecycle of an AI project

Why_AI_as_a_Service4:30
Recognize why AI-as-a-Service is dominant,Seller vs Buyer dynamics; most AI will be consumed, not built.
Buy_vs_Build_Decisions10:13
Criteria for when to buy a service vs build in-house and Identify factors influencing buy vs build choices.
Commercial Models7:17
Explain pricing structures (subscription, usage-based, outcome-based).Advise about commercial models to CXOs to make decision
Due Diligence Checklist7:03
A checklist for vendor evaluation and risk management. in order to apply a due diligence for AI service providers.
WrapUp-Scaling with Services-Ethical & Other considerations8:28
Hoow services accelerate scaling but need governance and understand leadership implications in scaling with AI services.
AI as a Service

Requirements

Business professionals engaged in AI Program/Project Delivery Sponsorships
Project Managers engaged for Delivering AI Projects
Candidates aspiring for PMI-CPMAI Certification
SME's working in AI Project teams

Description

Stop Doing "AI Theater"—Start Delivering Business Impact from AI Projects

Most AI strategies fail because they are treated as IT-only experiments. Success in 2026 requires AI Leadership—the ability to rewire how work happens ethically and profitably. If you have struggled to track real value from AI Projects or are tired of dashboards that tell you nothing about business impact, this course is your roadmap.

Why This Course is Different

While other courses teach you how to build models, this course teaches you how to lead them. We use the CPMAI (Cognitive Project Management for AI) methodology—the industry standard for delivering successful AI projects. We focus on the "Silicon Workforce" era, where Agentic AI and autonomous systems manage entire workflows, demanding new forms of Strategic Governance and Porject MAnagement for delivering AI projects

The Three Core Pillars of AI Project Managers:

1. Business Framing & Pattern Recognition Before a single line of code is written, success is determined by framing. You will learn to use the 7 Patterns of AI to identify high-value use cases and verify if a problem actually requires AI or a simpler RPA/Software approach.

2. Technical Literacy for Non-Technical Leaders You don’t need to code, but you must know how to speak "Data." We demystify essential ML concepts like Model Drift, Decay, Overfitting, and Underfitting. You’ll learn to ask the right questions during Model Evaluation to ensure your delivery partners are being transparent about performance.

3. Operationalization & AIaaS Governance The most dangerous phase of an AI project is the moment it goes live. We cover Model Operationalization, monitoring for drift, and the commercial dynamics of AI-as-a-Service (AIaaS). You will walk away with a Due Diligence Checklist for vendor evaluation and risk management.

This program is specifically designed for professionals who are accountable for outcomes but may not be the ones writing the code:

Business Leaders & CXOs: Who need to justify AI investments and prove ROI.
Project & Program Managers: Who are tasked with delivering AI projects on time and within ethical guardrails.
Sponsors & Stakeholders: Who need to oversee vendor relationships and internal AI teams.
Operations Leads: Who are responsible for the long-term adoption and performance of AI tools.

What You Get Inside:

Downloadable Templates: Project charters, risk registers, and vendor-questioning guides.
Real-World Examples : Analysis of both successful enterprise rollouts and documented AI failures.
Certificate of Completion: To validate your AI Leadership skills to your organization and network.

Take control of your AI agenda today. Move beyond the hype and start Leading & Delivering AI initiatives that deliver real, sustainable business value..

Who this course is for:

CXOs (CEO, CIO, CDO, CHRO, CFO, COO),
Business Unit Heads or Transformation Leaders
Senior Project & Program Managers (esp. Agile/Portfolio leadersrs
AI/ML Delivery Managers and Product Owners
Data Scientists transitioning into leadership/stakeholder roles
Managers with Oversight of AI Project Deliveries
Functional HOD

Project Management for Delivering AI Projects

What you'll learn

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Course content

Introduction of the course1 lecture • 5min

Understanding Human & Artificial Intelligence3 lectures • 16min

Foundations of AI project4 lectures • 28min

What is an AI Project & Seven AI Patterns9 lectures • 52min

Key Success Factors and Challenges in AI Projects9 lectures • 45min

Lifecycle for AI project8 lectures • 1hr 24min

Bonus : AI as a Service (AIaaS)-Updated June-265 lectures • 38min

Requirements

Description

Who this course is for: