Leading your engineers towards an AI-assisted future

Overview
This blog post outlines a step-by-step approach for technical leaders to introduce AI-assisted development in their teams. It emphasizes starting with controlled experimentation, moving to broader adoption, and finally measuring impact, all under the principle of Aligned Autonomy.

Key Takeaways

• Begin with a focused experimentation phase to understand AI capabilities and limits.
• Define clear, measurable objectives and metrics for each phase.
• Provide organizational support: training, tooling, and a Community of Practice.
• Transition to adoption once experimentation shows value, then assess impact on productivity and quality.
• Use proven enablement strategies from past waves (DevOps, test automation, etc.) to guide AI rollout.

Who Would Benefit

• Engineering managers and directors facing executive pressure around AI.
• CTOs and VP of Engineering looking for a structured AI adoption roadmap.
• Technical leads responsible for team productivity and tool selection.
• Individual contributors interested in integrating AI into their daily workflow.

Frameworks and Methodologies

• Aligned Autonomy (clear goals with autonomous execution)
• Incremental experimentation
• Metric-driven adoption
• Community of Practice for peer support