AI Is Accelerating Software Development. How Is Technical Debt Management Changing?

AI is changing the pace of software development. Tasks that once required more time, from generating sections of code to testing and documentation, can now be completed faster with the help of AI-powered tools.

For development teams, this creates opportunities for greater efficiency. But as the pace of development increases, another aspect of software engineering becomes even more important: maintaining the quality and long-term stability of the system.

This brings us back to a concept that has long been part of software development: Technical Debt.

AI changes the pace, not the principles of software engineering

Technical Debt is not tied to the use of AI. It exists in every software system and arises when decisions made during development require additional work in the future to improve, adapt, or maintain the system.

What changes with AI is the pace.

As teams are able to develop and iterate faster, architecture, coding standards, testing, and code review need to keep pace.

AI can assist in producing code, but decisions about how that code fits into a larger system still require technical context and engineering oversight.

From development speed to system stability

A feature should not be evaluated only by whether it works when it is released.

It is also important to consider how it fits into the existing architecture, how easily it can be maintained, how it affects other components, and how straightforward it will be to modify in the future.

This is why the use of AI in development does not reduce the importance of established software engineering practices.

On the contrary, automating part of the work gives teams more room to focus on decisions that require experience: architecture, security, integration, performance, and the long-term quality of the system.

Technical Debt should be managed, not simply avoided

Technical Debt is not necessarily the result of poor development. In real-world projects, certain decisions are made to meet deadlines, launch a feature, or adapt to changing requirements.

What matters is that these decisions remain visible and are actively managed.

This means understanding where Technical Debt exists, what impact it may have, and when it needs to be addressed. As the pace of development increases, this discipline becomes even more important.

AI as part of a controlled development process

For a software company, the question is not simply how much code can be produced with AI.

The question is how new technologies can be used within a development process that maintains standards for quality, security, and system architecture.

AI can accelerate parts of the process. But code review, testing, technical standards, and architectural decision-making remain essential to ensuring that speed translates into long-term value.

As Ermal Beqiri, founder of Soft & Solution Group, says:

“AI is increasing the speed at which software can be developed, but quality continues to rely on the same principles: sound architecture, clear standards, testing, and technical oversight. Technology can accelerate the process, while engineering ensures that the system remains stable over time.”

AI is changing the way software is developed. The challenge is not to choose between speed and quality, but to build processes where the two work together.