What Is Technical Debt and Why It Matters

Technical debt is basically the accumulation of compromises made during rapid software development to meet tight deadlines and ship features faster. Over time, as systems grow and evolve, this debt builds up. Creating friction, inefficiencies, and increasing the cost of future changes.

A recent report by Stripe suggests technical debt costs companies $85 billion annually in lost developer productivity. But there is more. Technical debt leads to bugs and outages, slows development velocity, and contributes to developer burnout by forcing teams to handle repetitive tasks and constant code fixes.

Take our experience with one of our long-term partners, ItTour, the travel aggregator supporting 67 operators and 7,500 agents. When scaling the solution, we faced significant technical debt due to diverse, outdated, or missing APIs. To address this, the team implemented a standardized API layer. Combined with caching and sharding, these efforts helped ItTour scale seamlessly while keeping the system stable and performant.

How AI Can Fix Technical Debt?

Recent advancements in artificial intelligence (AI) for software development are revolutionizing our perspective toward technical debt management.

In one such real-life example, Amazon leveraged their AI assistant, Amazon Q, to update an application from Java 17 to Java in a matter of hours instead of days. They estimate this saved the equivalent of 4,500 developer-years of work, in addition to higher security and lower infrastructure costs.

AI isn’t a silver bullet, but a powerful tool to help developers write clean code. When used correctly, AI can surface hidden issues, automate tedious work, and help teams gain control over their technical debt.

7 Ways AI Helps Address Technical Debt

1. Automating Repetitive Tasks

AI speeds up the development process by automating repetitive tasks, writing CRUD operations, scaffolding tests, and generating code snippets based on context. 

For example, GitHub Copilot completes entire functions with minimal input, so that developers can skip typing out boring boilerplate code.

Similarly, Amazon CodeWhisperer offers suggestions for secure API calls, speeding the implementation and increasing consistency across various teams.

By taking over the tedious parts, AI enables engineers to focus on domain-specific problems and scalable architecture, lowering the risk of technical debt caused by rushed or copy-pasted code.

2. Accelerated Refactoring with AI Assistance

AI tools can point out duplicated code, outdated patterns, or high-complexity functions, all the common sources of technical debt. 

Tools like Sourcegraph Cody or DeepCode scan large codebases for repeated structures, legacy constructs, or overly nested logic that indicates the need for refactoring.

Such tools can draft proposals for improvements,  but human oversight is critical to ensure they fit with the codebase’s long-term vision. Scalability and maintainability are context-dependent, and AI can’t reliably make architectural decisions like this yet.

3. Context-Aware Design Suggestions

Beyond syntax fixes, AI tools can suggest higher-level improvements, such as design pattern changes, optimized libraries, or even framework upgrades.

For example, Amazon’s CodeGuru Reviewer might recommend switching from an in-memory cache to a managed service if usage patterns indicate scaling concerns. Similarly, tools integrated with design systems can propose updated best practices and newer, more efficient libraries.

Still, developers should apply these suggestions selectively with real-world constraints in mind to avoid overengineering.

4. Proactive Code Health Monitoring

AI tools can continuously monitor and flag outdated dependencies, security vulnerabilities, and architectural drift. Snyk and CodeScene also offer proactive alerts to help development teams catch and address issues early on.

This helps teams tackle debt incrementally, preventing small cracks from becoming major faults later on.

5. Documentation & Knowledge Management

To reduce knowledge debt, AI-powered tools can auto-generate and maintain documentation. For instance, Mintlify and Swimm generate and maintain up-to-date documentation by pulling insights from code comments, commit history, and context.

So, when a developer refactors a function, these tools can automatically update related documentation or even notify teammates of changes.

That said, while useful for filling gaps, these tools need developer input to ensure nuance and context aren’t lost. Especially for legacy systems or nuanced business logic.

6. Prioritizing High-Impact Debt

Not all technical debt is created equal. Tools like Stepsize, Codacy, and CodeScene analyze code changes over time, correlating them with bugs, code churn, and team velocity to spotlight high-risk or high-cost areas.

Stepsize, for example, integrates with Jira to let developers tag technical debt directly in the code editor and link it to backlog items. CodeScene can visualize hotspots, files with high churn and complexity, so teams can focus on areas that will deliver the greatest ROI when refactored.

7. Improving Testing and Debugging

To address testing debt and boost test coverage, AI tools could be used to write unit tests, identify flaky tests, and catch untested edge cases. 

For example, Diffblue Cover can analyze Java code and generate human-readable JUnit tests that include edge cases and error handling. AI-based debugging tools can also trace bugs to their root cause by analyzing logs and suggesting breakpoints, cutting resolution time and reducing rework.

However, developer input is still required to ensure that tests reflect real-world use cases and business logic.

When AI Can Make Tech Debt Worse

AI systems are not yet good at figuring out whether code is maintainable or scalable and using these tools to manage technical debt can backfire in several scenarios:

• Poorly defined project goals: AI lacks the intuition to interpret unclear requirements.
• Undocumented business logic: Leads to incorrect assumptions and hard-to-maintain code.
• Highly customized or legacy systems: Niche frameworks or outdated languages can confuse AI models trained on modern codebases.
• Blind trust in suggestions: Over-reliance on AI without human oversight can lead to decisions that increase long-term complexity.

Final Thoughts: AI Is a Partner, Not a Shortcut

AI tools are transforming how developers manage technical debt, from documentation and refactoring to prioritization and testing. But they aren’t magic. Their effectiveness depends on how they’re integrated into existing workflows and validated by human expertise.

When used wisely, AI can be a powerful partner in building sustainable, maintainable software systems, and helping development teams stay ahead of technical debt, not buried beneath it.

Next Steps

Get in touch to tackle your tech debt!

At Itera Research, we combine cutting-edge AI tools with 20+ years of software development expertise to build scalable, efficient solutions, without disrupting your existing systems.

Ready to eliminate technical debt and future-proof your product? Book a consultation with our experts today to start your digital transformation journey.