Scaling AI-Assisted Development Across Teams

Individual developers can gain speed from AI coding tools, but teams often struggle with fragmented tools, inconsistent outputs, weak measurement, and unclear governance.
Individual developers can gain speed from AI coding tools, but teams often struggle with fragmented tools, inconsistent outputs, weak measurement, and unclear governance. In this course, Scaling AI-Assisted Development Across Teams, you'll learn to turn AI-assisted coding from an individual habit into a repeatable engineering capability. First, you'll explore how to evaluate tools, run a pilot, build a business case, and choose standards without forcing every team into the same workflow. Next, you'll discover how to define team practices, shared context, reusable prompts, and quality checkpoints that work across tools such as Cursor, OpenCode, Pi, Codex, and Claude Code. Then, you'll learn how to measure outcomes using productivity, quality, review, and delivery signals, and how to use those signals to improve the team's assets and workflows. Finally, you'll learn how to establish practical governance for intellectual property, auditability, acceptable use, and regulatory change. When you're finished with this course, you'll have the skills and knowledge needed to scale AI-assisted development across teams while preserving code quality, architectural consistency, accountability, and developer autonomy.
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