Udemy – Scaling AI-Assisted Development Across Engineering Teams

Scale AI-Assisted Development Across Engineering Teams and Multiply Developer Velocity. Transitioning from individual AI prompt tips to enterprise-wide AI-assisted software engineering requires clear governance, standardized workflows, and hard ROI metrics. This course delivers the exact playbook engineering managers, tech leads, and software architects need to evaluate AI toolchains, enforce security guardrails, prevent technical debt, and measure developer output across engineering organizations.

Who This Course Is For:

• Engineering Managers and Directors seeking to scale developer throughput and justify AI licensing ROI.
• Tech Leads and Software Architects responsible for defining team coding standards and prompt conventions.
• Platform Engineers and DevOps Leads building developer portals and automated CI governance.
• Senior Software Engineers preparing to lead AI implementation across squads.

What You Will Learn:

• Evaluate and standardize AI coding assistants across engineering teams using a weighted evaluation matrix.
• Deploy standardized team prompt repositories and system configurations directly into Git repositories.
• Implement DORA metrics and efficiency dashboards to accurately measure AI impact on developer velocity.
• Establish enterprise compliance guardrails for IP protection, data privacy, and security.
• Integrate AI assistants with Internal Developer Portals to streamline platform engineering.
• Detect, prevent, and refactor code bloat, hallucinated dependencies, and architectural drift.
• Build automated pull request review pipelines with static analysis security checks.
• Calculate seat-license ROI and present data-backed business cases to executive leadership.