AI in Your Engineering Team: Where to Start

Most teams either move too slow on AI or chase the wrong things. As an Engineering Manager who has led AI adoption in SDLC, I can help you cut through the noise. We'll look at your current setup, where your biggest friction is, and what's actually worth building vs buying. You'll leave with a clear, prioritised view of your next move.

What we can cover in this call:

Where AI fits in your current dev process and where it doesn't

Build vs buy decisions for your specific team size and stack

How to get engineering buy-in without it becoming a top-down mandate

AI in code review, testing, documentation, and sprint planning

What metrics to track so you can actually show impact

FAQs:

Q: Do I need to be technical to get value from this?

A: No. This is aimed at EMs, tech leads, and founders -- not just hands-on engineers.

Q: We're already using GitHub Copilot. Is this still useful?

A: Yes. Tool adoption is the easy part. Strategy, workflow integration, and team habits are where most teams get stuck.

Q: What if we haven't started with AI at all yet?

A: That's actually the best time to do this. We'll map a starting point that fits your team, not a generic framework.