Cognitive Project Management in AI (CPMAI)™ v7

Upon completing this workshop, participants will be able to:

• Understand foundational AI concepts including machine learning, cognitive computing, and generative AI, and distinguish between different types of AI such as AGI, strong AI, and narrow AI.
• Evaluate and select appropriate AI use cases using the seven patterns of AI, recognizing both potential and limitations across various business contexts.
• Apply the CPMAI methodology across its six defined phases to manage AI projects in a structured, iterative, and outcomes-driven manner.
• Formulate AI-specific business questions and scope initiatives to align with strategic goals using tools like the DIKUW Pyramid and Go/No-Go assessments.
• Manage the AI data lifecycle, including data preparation, labeling, engineering, governance, and pipeline design tailored for AI workloads.
• Develop and evaluate AI models, including the integration of machine learning algorithms, deep learning architectures, and pre-trained LLMs in enterprise environments.
• Deploy and operationalize AI models responsibly in production settings while addressing drift, scalability, versioning, and lifecycle management.
• Ensure AI projects adhere to ethical, transparent, and regulatory standards, incorporating responsible AI frameworks and privacy protections.
• Communicate AI project performance and explainability effectively to stakeholders using both business KPIs and technical metrics.
• Avoid common pitfalls in AI initiatives, including managing stakeholder expectations, vendor hype, and challenges unique to AI beyond traditional IT or software projects.