Of course, when it comes to tech debt, the best prevention is avoiding it in the first place. Justin Ramos, CEO of Compai, says that AI tools are “super helpful at easily creating unit tests, which prevents the buildup of tech debt. This used to be an arduous yet valuable task, but tools like Claude are making that easier.”
In fact, AI tools can help improve testing coverage in specialized scenarios that had previously resisted testing altogether. “Testing has historically been a challenge for ML/AI models due to their nondeterministic outputs, often leading teams to undertest complex systems,” says Jarrod Vawdrey, field chief data scientist at Domino Data Lab. “AI is changing this by automatically generating comprehensive test suites that account for the probabilistic behavior of models and can validate outputs across a spectrum of scenarios.”
Turning debt into strategy
Many companies are starting to use AI tools as an infrastructural ecosystem that supports quantifying and correcting tech debt. Dev Nag, CEO and founder of QueryPal, says that AI can go beyond just surfacing code smells to creating entire dashboards, “with hotspots, churn rates, entropy metrics, and even predicted cost-of-change per module. That makes technical debt legible to the business.”
