A Global Wellness Brand Lifted PR Velocity 40% in 6 Weeks

A leading global health and wellness brand had no meaningful AI tool adoption across its engineering organization. Leadership knew the team was falling behind but had no definition of what good looked like and no credible path to get there. The risk was the standard enterprise failure mode: buy licenses, announce a rollout, watch usage flatline.

Lazer embedded three engineers to run enablement as an engineering engagement rather than a training program: diagnose, build, measure, repeat. The team audited existing tooling, workflows, and team appetite, selected a stack centered on GitHub Copilot and coding agents, and drove adoption in deliberate waves: the six-engineer core frontend team first, then the eleven-person dev team, then the full 38-engineer organization. Hands-on pairing, live PR examples, and adoption metrics baked directly into team OKRs replaced the usual launch-and-hope approach.

Results were measured before and after: PR velocity, deployment frequency, tickets shipped, and the share of tasks completed by non-developers. The engagement is now extending into a software-factory build: an autonomous system that picks up low-complexity tasks end to end, freeing developers entirely, while PMs and UX deliver lightweight work with developer touchpoints only at review.

Lazer embedded three engineers and treated the rollout as an engineering engagement rather than a training program, following a diagnose-build-measure-repeat loop. They began by auditing the client's existing tooling, workflows, and the team's appetite for change, then selected a stack centered on GitHub Copilot and AI coding agents. Rather than announcing a company-wide launch, they drove adoption in deliberate waves: the six-engineer core frontend team first, then the eleven-person dev team, and finally the full thirty-eight-engineer organization. Each wave leaned on hands-on pairing and live pull-request examples instead of slide-based training, and adoption metrics were wired directly into team OKRs so usage became a tracked commitment rather than a suggestion. Progress was captured with before-and-after measurement across PR velocity, deployment frequency, tickets shipped, and the share of tasks completed by non-developers, giving leadership CFO-grade evidence. The engagement is now extending into a software-factory build: an autonomous system that picks up low-complexity tasks end to end, with product and UX contributors handing off lightweight work and developers involved only at review.

Mid-size engineering organizations (20 to 200 engineers) whose leadership knows AI tooling matters but has no internal playbook for making adoption real and measurable.