The rise of AI is as transformative as the invention of the electric motor. Just as motors found their way into almost every household appliance, AI is being rapidly integrated into all kinds of other technology.
But not every application of AI will result in added value. As the Gartner Hype Cycle reminds us, after the initial excitement, disillusionment often follows before true value is realized.
That’s why in software development, governance for AI is essential for mitigating serious risks to your code.
How AI impacts software development
AI is influencing software development in two key ways:
1. AI-powered products: AI is fast becoming a core component of software portfolios, offering enhancements such as automation, personalization, and decision-making. While beneficial, this introduces unpredictability into software behavior and raises questions over long-term maintainability.
2. AI-assisted development: GenAI tools can accelerate coding activities. However, over-reliance on AI-generated code can lead to a decline in technical expertise within your development teams. This raises the question of who can fix things when they go wrong.
Stronger governance around AI tools
AI systems in general are prone to behaving in unforeseen ways, making AI-generated code less predictable and not as reliable as hand-coded systems. On top of that, the overall effectiveness of AI tools depends on the quality of data provided.
If code is inconsistently structured or poorly governed, software architectures quickly become a patchwork of styles, reducing maintainability and scalability. Without proper governance, both of these factors introduce risks that can lead to a fragmented and unstable codebase.
It’s also not a given that AI-driven tools lead to higher productivity. While it’s undeniably possible, the potential gains from AI-powered development can very quickly and easily backfire if the correct governance isn’t in place.
At the same time, increased reliance on AI-generated code might impact developers’ abilities to critically assess, debug, and refactor software, resulting in long-term technical debt. More code does not necessarily correlate with higher productivity; quality is a vital factor.
Measuring code quality to bolster governance
To mitigate the risks, organizations must implement strong governance frameworks that prioritize code quality and prevent architectural erosion. Here at BonCode, we provide specialized tooling to detect excessive complexity and deviations from best practices. Here’s how:
- Measure and visualize code quality: Monitoring code quality ensures that AI-generated code aligns with organizational standards.
- Detect excessive AI usage: Over-reliance on AI-generated code can introduce inconsistencies within development teams. Governance tools help track where and how AI is being used within development pipelines. This helps you maintain consistency and control.
- Maintain software integrity: A well-structured software architecture leads to greater efficiency and sustainability. By preventing AI from creating an unmanageable patchwork of code, organizations safeguard long-term maintainability.
Strategic governance over AI
Implementing robust governance mechanisms isn’t just about technical cleanliness – it’s a strategic advantage. Used correctly, AI can be a powerful addition to software products and development. But without the right oversight, it can create more problems than it solves.
High-quality, well-structured code reduces maintenance costs, accelerates innovation, and ensures software remains adaptable to future advancements. At BonCode, we specialize in measuring and improving software quality. Our independent experts help organizations gain full visibility into their codebases, identifying risks and ensuring AI is a force for productivity rather than a source of technical debt.
Speak to a BonCode expert today and take back control of your AI-driven software products and AI-powered development.
