Managing Generative AI Projects

Course Overview

Introduction to Generative AI Project Management

• Key Differences Between Generative AI and Traditional AI Projects
• The AI System Development Lifecycle
• Common AI Actors
• Challenges of AI Project Management
• Quantifying ROI for Generative AI Projects

Project Planning and Scoping for Generative AI

• Understanding the Requirements of Generative AI Projects
• Scoping for Exploration and Fuzzy Objectives
• Data as a Project Dependency and Deliverable
• Selecting a Model
• Planning for Iterative Development

Building and Leading Effective Generative AI Teams

• The AI System Lifecycle & Common AI Actors
• Challenges of AI Project Management
• Understanding Research and Development in AI Projects
• Setting a Cadence for AI Projects
• Quantifying ROI for AI Projects
• Establishing Communities of Innovation and Collaboration

Communicating Generative AI Project Progress and Outcomes

• Communicating Technical Concepts to Non-Technical Audiences
• Reporting on AI Performance Metrics
• Addressing Uncertainty and Risk in AI Projects
• Visualizing AI Progress and Outcomes
• Managing Stakeholders Expectations during Research and Development
• Preparing Comprehensive Project Reports
• Utilizing AI Tools for Effective Communication

Introduction to Risk Management & the NIST Risk Management Framework

• What is Risk, Risk Management, and a Risk Management Framework?
• Frame, Assess, Respond, and Monitor
• Prepare, Categorize, Select & Implement
• Assess, Authorize, and Monitor
• Legacy of the NIST RMF

Legal and Organizational Implications of Generative AI

• Data Privacy Regulations (e.g., GDPR, CCPA)
• Liability and Accountability for AI-Generated Content
• Developing Responsible AI Policies and Guidelines
• Building a Culture of Ethical AI Development
• Case Studies of Legal Challenges in Generative AI
• The Role of AI Ethics Boards and Committees

Prerequisites

Practical project management experience