Draining 20% to 40% of technology resources, technical debt can accumulate quickly, increasing inefficiencies and maintenance costs, while hindering innovation. Proactive tech debt management is essential for maintaining a clean, scalable, and efficient codebase. By identifying and addressing tech debt early, organisations can prevent it from spiraling into a bigger problem. However, this requires significant effort, and the business benefits can be hard to quantify.
This byline explores the importance of actively managing technical debt and how, with Artificial Intelligence (AI) technologies, tech debt remediation can be largely automated, reducing long-term costs, and enabling organisations to stay agile and competitive.
What tech debt does to the IT landscape
Tech debt accumulation is inevitable, but organisations cannot let it be. Failure to address tech debt can have a far-reaching impact on performance and competitiveness. For example, as technical debt increases, so does the cost and complexity of systems maintenance. Tech debt can hinder the productivity of IT teams, who are forced to spend more time and effort fixing issues. Systems become fragile, and more prone to failure and breach. Technical debt compromises scalability and adaptability, and therefore the ability of systems to handle new demands or disruptions.
Tech debt comes in many forms, including code debt, design flaws, outdated infrastructure, poor testing, security gaps, and inefficient processes. Understanding the exact nature of tech debt enables organisations to prioritise fixesand maintain long-term efficiency while balancing speed and quality in software development.
While periodic and proactive tech debt management is critical, it doesn’t always get the attention it deserves. Why is that?
Several factors contribute to the neglect of technical debt. Because tech debt reduction produces indirect, hard to quantify benefits, it does not get the required attention of decision makers. Also, lowering tech debt is wrongly viewed as routine IT maintenance, rather than a strategic activity that can improve system performance, scalability, and innovation potential. Other reasons why organisations fail to address technical debt early include a preference for investing resources in more visible, high-impact IT projects, limited IT budgets already straining to meet the needs of operational maintenance and innovation, and a short-term view that prioritises quick wins over the long-term strategic value of tech debt reduction.
An approach for managing tech debt
Zero technical debt is impossible to achieve. But organisations don’t even need to, because after a point, tech debt reduction efforts yield diminishing returns. A gradual repayment approach that pegs tech debt at a manageable level instead of trying to eliminate it, is preferable. Here, organisations need to identify what part of their technical debt must be eliminated or addressed on priority, and what can be shouldered for some more time.
There’s no single way to attack technical debt. Enterprises need to contextualize their approach to the nature and root cause of the debt being targeted.
The first step is to categorise tech debt to identify and prioritise the different categories. Now, organisations have to compare the cost of addressing tech debt with the cost of maintaining it – think lost opportunity, incremental maintenance and operational cost, and potential loss of business or reputation.
Next, technical debt needs to be correctly and uniformly measured across the IT landscape and reported to the right stakeholders. By further measuring and tracking the remediation process, enterprises can set up structural quality improvement programs and benchmarking.
Development teams should track debt within their applications, focusing their limited resources on high-priority items to maximise impact. Finally, enterprises should foster a culture of continuous improvement and implement different approaches that eliminate or minimise the creation of technical debt across the organisation.
Harnessing AI for proactive tech debt management
The emergence of Artificial Intelligence and more recently, Generative AI, is facilitating technical debt management in a number of ways. GenAI helps identify tech debt easily, provides meaningful suggestions, and even automaticcode remediations, to fix it.
Code assistants enable developers to use GenAI for eliminating/addressing technical debt while doing their regular jobs, rather than attending to it separately; advanced plug-ins highlight instances of tech debt within the Integrated Development Environment, followed by quick fixes or remediation suggestions. Integrating tech debt assessment tools into the build process enables early identification and timely remediation.
Other AI advances, for example, Autonomous or Agentic AI, may also be used to automate tech debt management. An agentic framework can be employed to build agents for detecting, remediating and validating remediation of technical debt, reducing both manual intervention and the probability of tech debt piling up in the future. An ideal AI agent for reducing technical debt is one that can address security vulnerabilities in code, support code simplificationand documentation, and perform regression testing.
That being said, AI technology is not a silver bullet for resolving technical debt. More than tools, enterprises need the right approach to tech debt reduction. Proactive maintenance is key because it prevents unresolved issues from piling up, resulting in a healthier codebase and lowering the likelihood of large-scale remediation in the future. In the same vein, the evergreening of software – implementing small, iterative, continuous improvements rather than infrequent big fixes – keeps applications fresh while minimising disruption. AI can support all these strategies to keep technical debt under control.
