14 Topics
5 Days
70 Sessions
Programme Overview
Training Description
Session Objectives
- Understand the unique lifecycle of AI projects
- Differentiate between traditional and AI project management
- Learn to manage the iterative and experimental nature of AI
- Master techniques for data governance and project planning
- Use AI tools for project automation and forecasting
- Understand the ethical and bias considerations in AI projects
- Develop a roadmap for scaling AI initiatives
- Effectively communicate complex technical concepts to stakeholders
- Manage risks and uncertainties inherent in AI development
- Create a framework for measuring the success and ROI of AI projects
About the Course
The course explores how AI is transforming traditional project management by enabling project teams to analyse large volumes of information, identify patterns, predict risks, optimise resources, automate repetitive activities, and support faster and more informed decisions.
Participants will learn how to combine human judgement and AI-powered intelligence to improve project performance while addressing critical considerations such as data quality, AI governance, cybersecurity, ethics, privacy, bias, and responsible AI adoption.
Curriculum & Topics
14 Topics | 70 Sessions
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Workshop 1.1:
The shift from traditional to cognitive project management
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Workshop 1.2:
The core principles of CPMAI
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Workshop 1.3:
Understanding the AI development lifecycle
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Workshop 1.4:
Key differences between managing software and AI projects
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Workshop 1.5:
The role of the AI Project Manager
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Workshop 2.1:
From ideation to deployment
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Workshop 2.2:
Data collection and preparation
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Workshop 2.3:
Model development and training
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Workshop 2.4:
Validation and testing of AI models
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Workshop 2.5:
The continuous improvement loop
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Workshop 3.1:
The importance of data quality
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Workshop 3.2:
Data governance and security
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Workshop 3.3:
Sourcing and handling large datasets
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Workshop 3.4:
Data labeling and annotation strategies
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Workshop 3.5:
The ethical implications of data
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Workshop 4.1:
Applying Agile principles to AI projects
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Workshop 4.2:
The iterative nature of model development
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Workshop 4.3:
The use of sprints and backlogs in an AI context
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Workshop 4.4:
Managing scope creep with data-driven insights
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Workshop 4.5:
Retrospectives and continuous learning
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Workshop 5.1:
Identifying and engaging key stakeholders
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Workshop 5.2:
Communicating project progress effectively
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Workshop 5.3:
Managing expectations around model performance
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Workshop 5.4:
The role of explainable AI (XAI) in stakeholder communication
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Workshop 5.5:
Building trust and transparency
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Workshop 6.1:
The challenge of estimating in an AI project
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Workshop 6.2:
Using a phased approach to planning
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Workshop 6.3:
Risk management and mitigation strategies
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Workshop 6.4:
Budgeting for data, compute, and talent
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Workshop 6.5:
The importance of a flexible roadmap
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Workshop 7.1:
Using AI tools for scheduling and resource allocation
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Workshop 7.2:
Predicting project risks and delays with machine learning
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Workshop 7.3:
Automating reporting and status updates
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Workshop 7.4:
The role of natural language processing (NLP) in project communication
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Workshop 7.5:
The future of AI-powered project management
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Workshop 8.1:
Understanding bias in data and models
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Workshop 8.2:
The ethical considerations of AI development
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Workshop 8.3:
Techniques for mitigating bias
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Workshop 8.4:
Regulatory and compliance requirements
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Workshop 8.5:
Building a framework for responsible AI
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Workshop 9.1:
The importance of a robust MLOps pipeline
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Workshop 9.2:
Continuous integration and continuous delivery for AI
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Workshop 9.3:
Monitoring model performance in production
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Workshop 9.4:
Managing versioning and updates
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Workshop 9.5:
The role of the PM in the deployment process
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Workshop 10.1:
Defining success metrics beyond traditional KPIs
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Workshop 10.2:
Measuring business value and ROI
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Workshop 10.3:
The importance of user adoption metrics
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Workshop 10.4:
A/B testing and experimentation
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Workshop 10.5:
Creating a portfolio of AI projects
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Workshop 11.1:
Building a multi-disciplinary team
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Workshop 11.2:
The role of the Project Manager as a coach
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Workshop 11.3:
Fostering a culture of experimentation and psychological safety
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Workshop 11.4:
Managing conflict and collaboration
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Workshop 11.5:
Leading without authority
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Workshop 12.1:
Analyzing successful and failed AI projects
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Workshop 12.2:
Lessons learned from real-world scenarios
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Workshop 12.3:
Identifying common pitfalls and how to avoid them
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Workshop 12.4:
Applying best practices to a case study
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Workshop 12.5:
Peer-to-peer discussion and analysis
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Workshop 13.1:
The challenges of scaling from pilot to production
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Workshop 13.2:
Creating a blueprint for enterprise-wide adoption
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Workshop 13.3:
The role of governance and oversight
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Workshop 13.4:
Managing the portfolio of AI projects
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Workshop 13.5:
Strategic planning for future AI investments
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Workshop 14.1:
The unique risks of AI projects
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Workshop 14.2:
Techniques for identifying and assessing risks
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Workshop 14.3:
Creating a proactive risk mitigation plan
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Workshop 14.4:
Managing uncertainty in model performance
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Workshop 14.5:
Contingency planning
