What We Learned Taking a Culture-First Approach to AI Adoption at scale

Most AI transformation stories focus on tooling, targets, and adoption curves. At Culture Amp, our primary focus was people and culture. We still wanted to drive and accelerate our impact, but we weren’t willing to compromise on our focus on people to get there. We then partnered with an engineering analytics firm to measure whether our approach actually made a difference.

This is a co-presentation from Culture Amp's Director of Developer Experience and Director of Engineering Enablement. Having both roles in an org our size is an unusual choice, and it signals how seriously we take people and culture alongside technical delivery. Together, we helped lead an AI rollout grounded in trust over mandates, enablement over directives, and learning loops over training checklists.

We'll walk through what we built: a rollout shaped by pioneers and champions, rituals designed around psychological safety, hack days and storytelling that made experimentation feel normal. We'll share the data from a six-month research program across 88 engineers, tracking DORA metrics, adoption telemetry, and developer sentiment against industry benchmarks.

PR sizes stayed flat while merge frequency climbed, which runs counter to the industry trend of AI-inflated code volume. Code review engagement went up, not down. 39% of engineers reported faster delivery. But we'll also be honest about what didn't work: MTTR increased post-rollout, decentralised messaging created confusion, and out-of-hours commits rose. Even with our people focus, we were moving faster than was comfortable for everyone, and we had to own that tension.

We're not presenting a blueprint. We're sharing what happened when we tried to go as fast as we could without losing sight of the people doing the work. If you've been wondering whether investing in engineering culture pays off during an AI transformation, we think you'll find this useful.