Developers use AI to write code. That’s reality. The question for management isn’t whether to allow it, but how to ensure it doesn’t erode your software’s maintainability.
At Boncode, we measure maintainability. Not AI usage patterns or governance frameworks, but the actual impact on your code’s long-term health. Because whether code comes from a developer or Copilot, what matters is: can your team maintain it?
Maintainability: The Most Important Quality Aspect
We analyze code using ISO 25010 standards, with special attention to maintainability. This single score, between 0 and 100, tells you whether your software is becoming easier or harder to work with. We believe maintainability is the dominant quality aspect. Why? Because if systems are maintainable, you can fix any problem you encounter much easier. Whether it is related to security or any other component of ISO 25010. Unmaintainable systems are like a block of concrete: they will be there forever, but no changes are to be made. You’re stuck.
When AI enters your codebase, we see patterns. Not in how it’s used, but in what it produces. AI-generated code often shows higher complexity scores and increased duplication. Multiple solutions for the same problem. Inconsistent patterns across modules. Each piece works, but together they reduce your maintainability score, directly impacting your delivery speed and budget.
Software with a maintainability score below 60 takes significantly more time to modify. Features that should take a week stretch to nine days or longer. That 20%+ slowdown compounds over time, affecting roadmaps and budgets. This isn’t a developer problem; it’s the result of ungoverned AI patterns creating architectural drift.
Beyond Code: The Component Risk
Maintainability isn’t only about code structure. It’s also about the components your engineers bring into the system.
AI doesn’t just generate code. It suggests dependencies. Libraries you’ve never used. Packages with unknown licenses. Components with security vulnerabilities. Your Software Bill of Materials (SBOM) suddenly includes elements nobody consciously chose.
We’ve found AI-suggested components that violated security policies and created compliance issues. When auditors ask why you’re using a particular library, “the AI suggested it” isn’t an acceptable answer.
Making AI Work: Practical Thresholds
The companies succeeding with AI aren’t restricting it. They’re measuring its impact on maintainability and adjusting when specific thresholds are crossed.
Start with your baseline. What’s your current maintainability score? Then track it weekly as AI adoption grows. When it drops 5 points, investigate. At 10 points, intervene. When duplication rises more than 15% or new dependencies appear without clear ownership, check what is driving this trend. Is it AI? Something else?
We’ve found AI can actually improve maintainability in test code while degrading it in business logic. Same tool, different impact. But you only know this through measurement.
Your Next Step
AI in software development is like any other tool. Used blindly, it creates technical debt. Used with measurement, it can accelerate development without sacrificing quality.
The sweet spot for maintainability is between 70 and 80. Just like school grades: you don’t need perfection, but you can’t afford to fail. Whether your code comes from humans, AI, or both, this target remains the same.
Monitor your maintainability weekly. Set clear intervention thresholds. Make software quality part of your governance discussions, alongside compliance and financial risk.
Want to know your maintainability score? Our assessment shows exactly how your code measures up, where AI might be helping or hurting, and what needs attention. Facts, not opinions.
