Quality Champions Software

Even skilled and motivated agile teams sometimes fail to achieve their own software quality goals. This article presents a practice to assist agile teams in reaching their quality goals. This TensorBlue analysis is based on reporting and source material from InfoQ (https://www.infoq.com/articles/quality-champions-software/).

What Happened

InfoQ Homepage Articles How Quality Champions Foster Sustainable Software Quality Improvement at Swiss Post

How Quality Champions Foster Sustainable Software Quality Improvement at Swiss Post

We saw that motivated agile teams grapple to reach their software quality goals, leading to deteriorating software quality over time. We wanted to assist our freshly staffed team in reaching and maintaining their high software quality goals.

We identified that preventing the broken window effect, putting in a lot of effort for quantitative and qualitative analysis, creating incentives, and regularly paying attention to software quality helped our team.

To address those aspects, we created the lightweight practice "quality report," which consists of a role, ceremony, and artifact.

At the center of the practice is the quality champion, the software quality equivalent of the Scrum product owner. The role is mainly designed to incentivize people to constantly invest in long-term software quality.

With this practice in place, we have achieved and maintained high software quality for years and cope with the daily pressure of short-term achievements.

Even skilled and motivated agile teams sometimes fail to achieve their software quality goals. In this article, we present a simple yet effective practice we use to assist agile teams in reaching their quality goals and share our experience.

Implications for Product and Engineering Teams

For TensorBlue readers, the useful question is not just what happened, but how this changes product architecture, engineering priorities, AI delivery, observability, team workflows, or executive decision-making.

• Review whether this changes your AI roadmap, platform architecture, or engineering operating model.
• Identify the specific workflow, reliability, governance, or developer-productivity lesson that applies to your organization.
• Convert the lesson into a small production experiment with measurable quality, latency, cost, adoption, or risk metrics.
• Document source assumptions clearly so teams do not overgeneralize from incomplete public information.

TensorBlue Takeaway

The practical opportunity is to turn this signal into a concrete implementation decision: better AI systems, stronger product instrumentation, more reliable automation, and clearer technical governance. Teams that connect public technology shifts to their own delivery systems will move faster without adding unnecessary complexity.