Leading Cross-Functional AI Delivery

Übersicht

Leading Cross-Functional AI Delivery focuses on managing AI initiatives through structured execution and disciplined coordination. AI projects require close collaboration between data scientists, engineers, architects, legal teams, and business stakeholders.

This course equips you to guide that collaboration using practical project management frameworks adapted for AI development. You’ll explore agile methodologies for AI initiatives, resource planning techniques, and structured risk management practices.

The course also introduces Azure DevOps as a tool for organizing workstreams and maintaining visibility across teams. You will examine how generative AI tools can support project planning, documentation, and stakeholder communication, improving clarity and efficiency without replacing human oversight.

By the end of this course, you’ll be able to manage AI project execution from idea to delivery while maintaining alignment, mitigating risk, and supporting cross-functional coordination in enterprise environments.

Lehrplan

• Introduction to AI Project Management
Understanding AI project lifecycles
Key roles and responsibilities in AI delivery
Cross-functional team dynamics
• Frameworks for Managing AI Initiatives
Adapting agile methodologies for AI
Kanban and Scrum in AI projects
Setting up efficient workflows
• Resource Planning for AI Projects
Assessing and allocating resources in AI
Balancing technical and non-technical requirements
Tools for resource management
• Structured Risk Management in AI
Identifying potential risks in AI initiatives
Developing risk mitigation strategies
Continuous monitoring and adaptation
• Azure DevOps for AI Delivery
Introduction to Azure DevOps
Organizing workstreams and ensuring visibility
Collaboration and communication through DevOps
• Leveraging Generative AI Tools
Using AI for project planning and documentation
Enhancing stakeholder communication with AI
Best practices for integrating AI tools
• Aligning AI Projects with Business Goals
Identifying business objectives and aligning AI efforts
Metrics for measuring AI project success
Ensuring continuous alignment with business strategy
• Case Studies and Lessons Learned
Analyzing successful cross-functional AI projects
Common pitfalls and how to avoid them
• Final Project
Applying course concepts to a real-world scenario
Presenting a project plan to stakeholders

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Unterrichtet von

Microsoft

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Fachgebiete

Programming