Agile Methods for AI-Enabled Projects

Integrate GenAI into agile sprints, MVPs, and cross-functional teams.

Pillar

Process – Workflow, Governance, Risk & Efficiency

Overview

This course guides participants on applying agile methodologies tailored for AI projects, emphasizing iterative development, rapid prototyping, and collaboration across diverse teams. It focuses on adapting sprint planning, MVP creation, and feedback loops to successfully deploy Generative AI solutions.

Learning Objectives

Participants will be able to:

• Understand agile principles in the context of AI and GenAI development

• Plan and execute AI-enabled sprints with clear goals and deliverables

• Build Minimum Viable Products (MVPs) for AI solutions to validate concepts

• Facilitate cross-functional collaboration among data scientists, engineers, and business teams

• Incorporate continuous feedback and learning to improve AI models and workflows

Target Audience

• Agile coaches and scrum masters

• AI project managers and product owners

• Software developers and data scientists

• Business analysts and process owners

Duration

20 hours over 4 days (5 hours per day)

Delivery Format

• Interactive lectures and agile simulation exercises

• Sprint planning and MVP development workshops

• Group discussions and retrospectives

Materials Provided

• Agile project templates tailored for AI initiatives

• Sprint planning and backlog management tools

• Case studies on AI project agile implementation

• Certificate of completion

Outcomes

• Confidently apply agile frameworks to AI projects

• Deliver functional AI MVPs quickly to gather real-world feedback

• Enhance collaboration and communication in AI development teams

• Adapt and improve AI solutions through iterative cycles

Outline / Content

Day 1: Agile Fundamentals for AI Projects

• Overview of agile methodologies and principles

• Challenges unique to AI and GenAI project management

• Defining roles and responsibilities in AI agile teams

Day 2: Sprint Planning and MVP Development

• Designing AI-focused user stories and acceptance criteria

• Planning sprints with clear objectives and deliverables

• Building and testing AI MVPs

Day 3: Cross-Functional Collaboration and Communication

• Facilitating teamwork across data science, engineering, and business units

• Managing dependencies and risks in AI agile projects

• Effective communication and stakeholder engagement

Day 4: Continuous Feedback and Iteration

• Collecting and incorporating user feedback on AI outputs

• Retrospectives and process improvements

• Scaling agile practices for larger AI initiatives

• Workshop: Simulated AI sprint with planning, execution, and review