Course Objective & Scope
Standard Agile or Waterfall practices alone struggle with AI projects because schedules depend on data readiness and models behave probabilistically. Grounded in the PMI-CPMAI (Cognitive Project Management for AI) methodology, this programme teaches project professionals how to run AI initiatives rigorously from business framing through production operations.
The six CPMAI phases, seven patterns of AI, Go/No-Go gate reviews and Trustworthy AI checklists are practised through case studies and exam-style scenario questions. Participants leave with ready-to-use CPMAI workbook templates tailored to enterprise AI delivery.
Who Should Attend?
- Project and programme managers with PMP, Agile/Scrum or PMO backgrounds
- PMO leaders embedding PMI-CPMAI standards into enterprise governance
- Technical product managers and business analysts coordinating data and AI teams
- Professionals preparing for PMI-CPMAI certification and structured AI delivery
Key Takeaways
- Execute all six CPMAI phases from Business Understanding through Model Operationalisation
- Map business problems to the seven patterns of AI to scope requirements accurately
- Run Go/No-Go gate reviews covering data readiness, ethics, security and business viability
- Integrate generative AI, RAG and agent workflows into iterative CPMAI and Agile sprints
- Apply the CPMAI workbook and methodology terminology across real-world project scenarios
Curriculum
Introduction to CPMAI and the Seven Patterns of AI
- Why AI projects fail: fundamental differences between software delivery and data/AI delivery
- From CRISP-DM to CPMAI: iterative, data-centric and agile execution
- Classifying business needs with the Seven Patterns of AI and hybrid combinations
Phases I & II: Business Understanding and Data Understanding
- AI suitability triage: does this problem truly require AI?
- Dual definition of success across business KPIs and technical model metrics
- Auditing data sources, data quality, labelling effort and governance constraints
Phases III & IV: Data Preparation and Model Development
- Work packages for data cleaning, augmentation, synthetic data and RAG knowledge bases
- Managing model selection, training/fine-tuning, prompt engineering and experiment tracking
- Synchronising data and model iterations with Agile sprint cadences
Phases V & VI: Model Evaluation and Operationalisation
- Multi-dimensional evaluation: technical metrics, business impact, bias and hallucination rates
- Deployment planning, MLOps/LLMOps handover, drift monitoring and retraining triggers
Trustworthy AI and CPMAI Workbook Workshop
- Checklists for transparency, explainability, privacy, security and accountability
- Completing the CPMAI Workbook on an end-to-end enterprise case study
- Scenario-based practice questions and methodology review
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