UC-01 / ENGINEERING
A world-leading Munich-based technology company in film and media technology: AI capability integrated into a mature ALM landscape without interrupting the development process.
A-01 — STARTING POINT
An ALM landscape grown over years carries the entire product development: requirements in Polarion, tasks in Jira, code in Git, builds in Jenkins, knowledge in Confluence, artifacts in Artifactory. AI was to enter this environment — but a development operation with live projects cannot afford a process break, and every change must pass quality assurance and compliance.
A-02 — OUR ROLE
We have been working in this R&D daily, again and again, for 15 years — as external engineers, not as visitors. We know the processes, the tools and the people from a decade and a half of day-to-day work. That inside view is what separates an AI integration that gets adopted from one that gathers dust in a portal.
A-03 — IMPLEMENTATION
AI capability was integrated step by step into the existing workflows — where the engineers already work: support for requirements and review work, for test case creation, and in the build pipelines. Discipline by discipline, each proven in live operations rather than as a big-bang rollout.
A-04 — OUTCOME
The AI support runs in routine operations. Routine effort in review, test and pipeline work has dropped noticeably, and the toolchain remained continuous and traceable — no parallel system, no process break, no island solution. And because the integration came from inside, the teams carry it instead of working around it.
WHERE THIS FITS IN OUR APPROACH
This case shows stage 2 of our approach: integrating AI where the product is built — inside the existing toolchain, carried by process knowledge.
Stage 2 — AI in the development process →NEXT STEP
In an initial consultation we check whether this path carries for your company too — and where it would have to look different in your case.