Planning and implementing projects with AI

At the end of the course, you will earn a Certificate of Completion from Spirit in Projects.

Are you a project manager facing the challenge of successfully leading AI projects? If you want to learn about the specific characteristics and success factors of AI projects and how to adapt traditional project management methods, then this training is perfect for you. Together, we will explore the key aspects of AI project management and optimally prepare you for your upcoming AI projects.

Advising

Objectives

  • Understand the characteristics and challenges of AI projects and manage them successfully
  • Develop an understanding of AI methods and their potential applications
  • Adapt and apply traditional project management techniques to AI projects
  • Identify AI-specific risks and develop appropriate mitigation strategies
  • Implement effective stakeholder management in AI projects

Target groups:

AI Expert, Project Manager, Scrum Master, Project Director, IT Project Director, Product Owner, Portfolio Manager, Demand Manager and for all who are ready to explore the possibilities.

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Syllabus

1. A practical grounding in AI-projects

  • Differences between AI projects and traditional IT projects
  • The AI project lifecycle
  • Success factors
  • Common pitfalls

2.  Core knowledge for project managers

  • Overview of AI techniques such as Machine Learning, Deep Learning and NLP
  • Different AI approaches
  • Understanding different AI architectures
  • Common AI development environments and tools

3. Requirements Management in AI projects

  • AI-specific requirements
  • Managing uncertainties and changing requirements
  • Data quality and data availability
  • Formulating success criteria

4. Planning and estimating

  • Relevant considerations
  • Iterative planning and experimentation
  • Resource planning (infrastructure, computing power, data)
  • Budgeting for AI projects

5. Forming a team and considering skills

  • Essential skills and level
  • Collaboration between business and data science
  • External partners and service providers
  • Develop AI competencies

6. Stakeholder Management in AI-projects

  • Identifying and analyzing AI-specific stakeholders
  • Managing inflated expectations
  • Adressing resistances in AI initiatives
  • Change Management
  • Communication strategies

7. Technical aspects of project management

  • Training a model and its evaluation
  • Managing data quality and data volume
  • Integrating AI in existing systems
  • Cloud vs. on-premise solutions

8. Risk management

  • AI-specific risks
  • Ethical and legal considerations
  • Data protection and compliance
  • Quality management and testing AI-systems

9. Performing and monitoring

  • Agility in AI-projects
  • Key performance indicators (KPIs)
  • Tracking and milestones
  • Monitoring of model quality and its performance

10. Best Practices

  • Analysis of successful AI-projects
  • Lessons Learned from failed AI-projects
  • Checklists and templates
Spirit in Projects