Course Outline
Module 1: Foundational AI Principles for Program Managers
Key areas:
Analysis of Large Language Model (LLM) architecture
Core concepts including prompt construction, hallucination mitigation, and context management
Distinguishing generative AI systems from legacy automation frameworks
Survey of standard enterprise AI utilities integrated into the curriculum (e.g., ChatGPT, Claude, Microsoft Copilot)
Demonstrations of text generation and interpretation processes
Module 2: Prompt Engineering Strategies for Program Management
Key areas:
Foundations of effective prompt architecture
Techniques for zero-shot, few-shot, and role-based instruction
Designing prompts tailored to specific program management functions
Methods for assessing and iteratively refining AI-generated outputs
Applied exercises involving prompt creation for realistic program scenarios
Module 3: AI Applications in Program Initiation
Key areas:
Leveraging AI to assist in drafting program charters
Synthesizing program objectives and defining scope parameters
Automating stakeholder mapping and analysis workflows
Utilizing AI support for early-stage risk identification
Validating AI-generated content for factual accuracy and completeness
Module 4: AI Applications in Planning and Scheduling
Key areas:
Generating Work Breakdown Structures (WBS) via AI assistance
Developing milestone frameworks and temporal schedules
Applying AI-supported estimation methodologies
Formulating resource allocation recommendations
Refining planning deliverables through structured prompt iteration
Module 5: AI Applications in Risk and Issue Governance
Key areas:
Implementing predictive risk analytics using AI models
Constructing risk registers based on program descriptors
Streamlining issue classification and triage processes
Formulating mitigation strategies with AI support
Organizing risk data via structured prompts and tabular formats
Module 6: AI Applications in Stakeholder Engagement
Key areas:
Automating the generation of program status reports
Drafting executive summaries derived from raw program data
Modulating communication tone and style for diverse stakeholder groups
Translating technical specifications into accessible business updates
Handling sensitive communications with AI-assisted guidance
Module 7: AI Ethics and Governance Frameworks
Key areas:
Analyzing risks associated with algorithmic bias and model hallucination
Upholding ethical standards in the professional deployment of AI
Navigating data privacy requirements and regulatory contexts such as GDPR
Establishing internal AI usage policies for program teams
Implementing human-in-the-loop validation protocols
Module 8: Enterprise AI Tools and Adoption Strategies
Key areas:
Operationalizing Microsoft Copilot within Word, Excel, Teams, and Outlook
Determining optimal use cases for Copilot versus alternative platforms
Evaluating comparative capabilities of various AI tools for program support
Constructing a phased AI adoption roadmap for organizational units
Developing individual action plans for integrating AI into program workflows
Requirements
Enrollees are expected to possess foundational experience within program management environments or cross-functional teams. Proficiency in core program management principles, including planning methodologies, risk governance, and stakeholder communication standards, is advised.
Prior technical proficiency with Artificial Intelligence tools is not a prerequisite for participation.
Testimonials (1)
Understanding that in project management is more about tailoring and assessing each situation than just deciding what to do next.
