Training on AI Project Management: From Pilot to Scale

Module 1: Managing the AI Project Lifecycle

• Understanding the unique aspects of AI projects
• Phases of the AI project lifecycle
• Defining AI project scope and objectives
• Planning AI project resources and timelines
• Managing AI project stakeholders
• Case Study: Developing a project plan for an AI initiative

Module 2: Collaborating with Data Science Teams

• Understanding data science workflows
• Working with data scientists and ML engineers
• Managing technical requirements and expectations
• Bridging the gap between business and technical teams
• Facilitating effective communication and collaboration
• Case Study: Building a collaboration plan with a data science team

Module 3: AI Project Risk and Quality Management

• Identifying AI project risks
• Managing data quality and availability risks
• Managing model performance and accuracy risks
• Managing ethical and bias risks in AI
• Implementing AI project quality assurance
• Case Study: Conducting a risk assessment for an AI project

Module 4: Data Governance, Privacy, and Security

• Understanding data governance frameworks
• Ensuring data privacy and compliance
• Managing data security in AI projects
• Implementing responsible data management practices
• Balancing innovation with data protection
• Case Study: Developing a data governance plan for an AI project

Module 5: Scaling AI Pilots to Production

• Managing pilot projects and learning from them
• Scaling successful AI pilots to production
• Integrating AI with existing systems and processes
• Managing the transition from pilot to full deployment
• Monitoring and maintaining AI systems post-deployment
• Case Study: Developing a scaling plan for an AI pilot

Module 6: AI Vendor and Partner Management

• Selecting AI vendors and partners
• Managing AI vendor relationships
• Evaluating and contracting AI solutions
• Ensuring vendor compliance and performance
• Managing vendor risks and dependencies
• Case Study: Developing an AI vendor management plan

Module 7: AI Project Communication and Stakeholder Engagement

• Building stakeholder buy-in and support
• Managing stakeholder expectations
• Communicating project progress and results
• Managing project changes and scope creep
• Facilitating effective project meetings and reporting
• Case Study: Developing a project communication plan

Module 8: Agile and Iterative AI Project Delivery

• Applying agile methodologies to AI projects
• Managing iterative development and feedback loops
• Delivering incremental value and learning
• Adapting to changing requirements and insights
• Continuous improvement in AI project delivery
• Case Study: Implementing agile practices in an AI project

Module 9: AI Project Financial Management

• Managing AI project budgets and costs
• Cost estimation and tracking for AI projects
• Managing financial risks and contingencies
• Value realization and ROI analysis
• Financial reporting and governance
• Case Study: Developing an AI project budget

Module 10: Post-Implementation and Lessons Learned

• Conducting post-implementation reviews
• Documenting lessons learned and best practices
• Building organizational knowledge on AI project management
• Continuously improving AI project management practices
• Sharing insights and successes across the organization
• Case Study: Conducting a post-implementation review for an AI project