According to some estimates, tech debt, or the costs incurred when having to constantly fix aging or clunky software systems, has ballooned to more than $1.52 trillion in the U.S. alone. With technology like agentic AI being heavily embedded on top of companies’ aging technology systems and operations, this rising cost of tech debt makes sense.

Many organizations are quickly implementing new technologies without addressing underlying systems first. These haphazard tech foundations are starting to pile up and tip over, causing huge financial costs, heightened vulnerability, and long-term consequences and business issues. 

It’s time to shine a light on tech debt, the monster lurking in the shadows of many organizations’ digital landscapes, and discuss how we can tame it.

Gen AI – a double-edged sword

It’s no secret that gen AI is changing the technological landscape, requiring companies to move faster at adopting and implementing technology that impacts huge portions of their businesses. 

According to Accenture’s 2025 Pulse of Change report, 27% of organizations are already investing in AI agents across multiple parts of their enterprises, signifying the real value AI is creating. Within just the cybersecurity space, AI is helping companies accelerate code remediation, cut down on defect backlogs faster and improve business resiliency. 

While its benefits are clearly immense, what happens when gen AI grows too quickly on top of an already complex tech foundation? 

If not handled properly, AI can contribute to tech debt in major ways. The rapid evolution of gen AI models is leading to new layers of complexity and issues, especially if these models are integrated into sub-optimally designed systems. This ad hoc ecosystem of technology is creating a vicious cycle where the very technology meant to solve problems ends up creating more.

The cybersecurity ramifications alone are enough to bring concern. Tech debt can increase security vulnerabilities by causing systems to perform poorly or even break entirely. This breakage can create new vectors and opportunities for hackers, who are already regrouping for more high-profile attacks, to exploit.   

The good news is that there are several steps organizations can take to both mitigate the complexity AI is introducing and effectively tackle tech debt. 

Three actions to curb tech debt

So long as technology is improving and evolving, tech debt will always be an issue. But the gravity of its impact on a business can be managed. Here are three steps to manage tech debt. 

First, categorize tech debt into principal, interest, liabilities and opportunity cost. This will help your organization prioritize remediation efforts and focus on principal costs that directly impact current operations. Second, create a tech debt inventory and a prioritization model to trace debt to its source. For example, you could use the PAID model, which helps IT leaders prioritize and sequence tech debt remediation efforts based on business value and urgency. Third, use metrics like tech debt density to measure the issue. 

By focusing on the principal cost of tech debt and addressing the most critical areas first, organizations can effectively manage their tech debt and drive business growth.

Don’t wait for the monster to come knocking

Successful organizations treat tech debt like financial debt, managing it proactively with a strong digital core, agility and a culture of continuous improvement. However, if left unmanaged, the complex patchwork of technology and software comprising the digital foundation of many companies’ risks failing, leading to real and significant impacts. 

Take a moment and think about how you can navigate constant technological change. It’s a lot to juggle, but by being intentional with how you stay strategic through these changes, you can address tech debt head-on and use technology like AI to your business advantage.

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