Task 1: Differentiating AI Project Management Approaches
- Identify common reasons for AI project failures
- Explain why AI projects differ from traditional software development
- Evaluate ROI justifications for AI initiatives
- Address data quantity and quality challenges
- Recognize proof-of-concept limitations and pitfalls
- Manage the gap between models and real-world implementation
- Implement continuous AI project lifecycles
- Avoid vendor hype and product mismatches
- Manage stakeholder expectations to prevent overpromising
- Compare waterfall, lean, and agile methodologies for AI projects
- Adapt traditional methodologies for data-centric projects
- Apply CPMAI frameworks to AI initiatives
- Navigate the six phases of CPMAI methodology
Task 2: Executing the Business Understanding Phase
- Formulate AI-specific business questions
- Apply the data, information, knowledge, understanding and wisdom (DIKUW) Pyramid
- Prioritize and scope AI projects effectively
- Differentiate between pilots and proofs of concept
- Estimate time-to-ROI for various AI project types
- Leverage generative AI to accelerate project timelines
- Separate cognitive from non-cognitive components
- Determine when to implement automation versus AI
- Match business needs to the seven patterns of AI
- Assemble appropriate AI project teams
- Distinguish roles of data scientists and citizen data scientists
- Establish acceptable performance metrics
- Conduct AI Go/No-Go assessments
- Analyze real-world CPMAI Phase I implementation examples
Task 3: Managing the Data Understanding Phase
- Address AI-specific aspects of data understanding
- Identify appropriate datasets for machine learning
- Evaluate training data requirements
- Validate “ground truth” data quality
- Optimize AI projects with limited data availability
- Determine when to iterate back to previous phases
- Analyze real-world CPMAI Phase II implementation examples
Task 4: Coordinating the Data Preparation Activities
- Formulate data preparation requirements
- Apply generative AI to streamline data preparation
- Determine data labeling requirements
- Perform data cleansing and enhancement
- Conduct CPMAI Phase III Go/No-Go assessments
- Analyze real-world CPMAI Phase III implementation examples
Task 5: Determining the Approaches for Model Development
- Define AI model development requirements
- Accelerate development with appropriate shortcuts
- Incorporate pre-trained models and generative AI effectively
- Conduct CPMAI Phase IV Go/No-Go assessments
- Analyze real-world CPMAI Phase IV implementation examples
Task 6: Conducting Model Evaluation and Maintenance
- Formulate model evaluation questions and criteria
- Design comprehensive evaluation plans
- Implement model iteration processes
- Address data drift and model drift challenges
- Execute CPMAI Phase V Go/No-Go assessments
- Analyze real-world CPMAI Phase V implementation examples