Leading AI Adoption in Engineering Teams

AI is transforming how we build software, creating new challenges for both engineers and leaders.

Workflows are shifting toward context-driven, AI-assisted development. Beyond tools, this is a fundamental change in how we create, innovate, and deliver.

To succeed, two things matter:

1️⃣ Workflows that use AI to deliver speed and quality. 2️⃣ Cultures where people feel safe to learn, experiment, and grow.

But adoption rarely succeeds top-down. It usually happens through one small, visible experiment at a time.

Here are some practical starting points:

💡 For Leaders: Give a few engineers dedicated time to explore AI on safe tasks - like unit tests or documentation. Share their progress so the team can learn together. 💡 For Engineers: Try low-stakes experiments in your daily workflow - boilerplate code, refactoring, or PR reviews.

Tools you can start with:

🚅 Coding agents like Cline and Windsurf (both have VS Code plugins). If you want to go further, connect these tools to an MCP server like Context7 to reduce hallucinations. 🚅 PR review assistant like Gemini Code Assist to spark team-wide learning.

The future of engineering will be defined at the intersection of AI-driven workflows and human-centered leadership.

👉 If you had to place the first bet on AI adoption in your teams, would you start with workflows or culture?