Who Should Attend

This course is designed for professionals responsible for software engineering practices and development workflows.

Typical attendees include:

• Senior software engineers
• Technical leads
• Software architects
• Engineering managers
• Principal developers

The course is particularly valuable for teams that have already begun experimenting with AI-assisted development and want to introduce AI in a structured and responsible way across engineering teams.

Prerequisites

Participants should have a background in software development and engineering practices.

It is recommended that participants either:

• have experience using AI tools in development environments, or
• have attended the AI-Assisted Software Engineering for Developers course.

Learning Objectives

By the end of the course participants will understand how to:

• integrate AI tools into software engineering workflows
• scale AI-assisted development practices across teams
• maintain code quality and architectural standards when using AI-generated code
• review and validate AI-assisted development at scale
• apply governance and security practices for AI-assisted development
• introduce AI responsibly into production development environments

Course Content

AI Adoption at Engineering Team Scale

• common patterns of AI adoption in development teams
• challenges of scaling AI usage across engineering groups
• balancing productivity with engineering discipline

Designing Engineering Workflows with AI

• AI within the software development lifecycle
• defining responsible AI usage within development teams
• integrating AI tools into engineering practices

Reviewing AI-Generated Code

• approaches to reviewing AI-generated changes
• identifying risks in generated code
• maintaining developer accountability

Architecture and Security Considerations

• architectural oversight in AI-assisted development
• security considerations when using AI-generated code
• protecting intellectual property and sensitive codebases

AI in CI/CD Pipelines

• integrating AI into development pipelines
• AI-assisted testing and validation
• automation opportunities within CI/CD workflows

Managing Software Quality

• maintaining code maintainability and readability
• preventing AI-generated technical debt
• balancing productivity with long-term engineering quality

AI-Assisted Testing Strategies

• using AI to generate tests
• validating AI-generated test cases
• improving software quality through AI-assisted testing

Building AI-Capable Engineering Teams

• defining best practices for AI-assisted development
• evolving engineering roles and responsibilities
• introducing AI capabilities across development teams

Practical Exercises

Throughout the course participants will explore:

• examples of AI-generated code and development workflows
• engineering scenarios involving AI-assisted development
• approaches to integrating AI tools into development practices

The sessions include interactive discussion and practical exercises focused on real software engineering scenarios.

Duration

2 Days

Delivered as live instructor-led training, either:

• virtually
• or privately for engineering teams

Part of the AI for Software Engineering Teams Learning Path

This course forms the third stage of the AI for Software Engineering Teams learning path.

1. AI Adoption for Software Engineering Teams (1 Day)
Introducing AI safely into development environments.

2. AI-Assisted Software Engineering for Developers (2 Days)
Practical developer usage of AI tools in coding, testing, and debugging.

3. Scaling AI in Software Engineering Teams (2 Days)
Integrating AI into engineering workflows and development practices.