An established Swiss engineering org had rolled out generative AI coding tools but seen no measurable speedup after weeks. Two underlying problems: tooling adopted without methodology change, and no review or quality discipline to convert raw AI output into shippable code. The risk was a familiar one: the tools get blamed, the rollout stalls, and the org concludes "AI does not work for us."
Three working sessions across the engineering organisation covering the full SDLC transformation: change in ways-of-working, agentic spec writing, cross-model review pipeline, eval-driven implementation flywheels and visual QA. Tools became one ingredient in a methodology, not the methodology itself. Senior engineers reframed as agent supervisors and decision owners rather than line-by-line authors.
Reported 200%+ improvement in cycle time across the engineering organisation, with a measurable lift in average code quality on DORA-style metrics. Methodology now operating as the dev-org standard rather than a single team's experiment. Same playbook scheduled for adjacent dev orgs inside the company.
We go through where your R&D org stands, which codebases are ready, and whether the programme or a single module is the right first step.