For too long, enterprises have had to deal with the perils of technical debt. Cutting corners to release faster bought speed, but often led to a loss of productivity and higher costs down the line, as teams had to spend time on refactoring and fixing bugs. However, old as this challenge may be, it’s taking on a new and concerning shape. Now it’s tokens as much as time, but the real shift is bigger than just the bill. AI produces code faster than teams can govern it, turning what was once a development problem into a portfolio-level one.
Architectural debt overtakes code-level issues
First, the good news. AI coding tools are delivering real productivity gains. Routine tasks take less time. Code-level debt remediation—duplication, complexity, poorly structured functions—is increasingly within AI’s reach. Refactoring, documentation, and cleanup that once required significant engineering time can now be approached at a lower cost.
But technical debt has many heads, and there is one type of technical debt in particular that’s about to present its bill: Architectural debt.
Architectural debt manifests in the relationships between systems: it pertains to how components are linked, how teams rely on each other’s work, and whether the software structure aligns with the original design intent.
This is exactly where AI’s capabilities fall short; AI coding tools or agents have a limited context window and typically lack domain knowledge. This means that when making decisions, they do not consider your architecture and may attempt to fill in the gaps independently.
Gartner predicts that by 2027, architectural debt will account for 80% of all technical debt. For organisations where technical debt already consumes 21% to 40% of IT spending, this shift means the rising cost of tech debt will increasingly come from architectural problems—the gaps between systems—rather than code-level issues. Failing to manage this has clear consequences: slower delivery, higher maintenance costs, and systems that become harder to change.
AI exacerbates architectural debt
Although AI can be great for fixing code issues, it has a significant compounding effect on architectural debt. This is because AI coding tools and agents operate within a limited context window and lack domain knowledge of the broader system. This means that AI tools can generate code that introduces hidden dependencies, coupling between services, and architectural drift from intended designs.
What’s more, AI accelerates code production, increasing complexity across systems and making architectural debt harder to detect and control. Yes, AI enables developers to produce software changes at speed, but that speed alone is not enough to reduce technical debt.
Indeed, lacking visibility, shared standards, and continuous governance, AI can cause technical debt to accumulate faster and become much more difficult to contain across the portfolio. In this scenario, businesses can quickly find themselves saddled with a significant governance gap whereby they scale software development using AI faster than they can manage the structural risk building up across their portfolios.
A new approach to technical debt resolution
It’s increasingly clear that team-level tooling is not up to the task of managing architectural debt. This is because these tools are largely blind to the systemic debt that arises in the gaps and dependencies between software systems. As a result, IT teams are unable to quantify the system-wide issues affecting their portfolios’ performance.
To get to grips with architectural debt, developers should instead look to tools that deliver continuous, portfolio-wide visibility into software quality, risk, and dependencies. Indeed, portfolio-wide visibility is currently the only reliable way to detect, prioritise, and govern architectural debt before it destabilises the systems that the business depends on.
To achieve this visibility, CIOs and CTOs should pivot to technical debt management tools that incorporate wider capabilities such as software composition analysis, architecture observability, and application portfolio management. These tools enable new features including continuous architectural observability and greater software intelligence for contextualised reasoning and automated remediation.
Fast, effective, and prioritised remediation
Applied to reducing architectural debt, these tools make a huge difference. First, they enable granular visibility of the software portfolio and its associated risks. This visibility enables CIOs, CTOs, and their teams to prioritise remediation efforts on the points where technical debt has the greatest business impact.
Meanwhile, technical debt management tools can also provide IT leaders with the continuous monitoring needed to mitigate the challenges of AI-enhanced development. By tracking change from moment to moment, IT teams can monitor how technical debt shifts with every release and every AI-generated code change. The approach enables teams to measure remediation impacts and catch architectural drift early. As a result, the productivity benefits of AI can be fully leveraged without any associated quality risks.
Solving technical debt once and for all
With the dangers of architectural debt becoming more pronounced, IT leaders need to change the way they identify and remediate faults. Leveraging real-time, continuous monitoring, they can prioritiseinvestment across the portfolio and focus resources on where technical debt has the biggest impact. The approach also enables IT teams to keep pace with change and mitigate the consequences of AI. Technical debt has been a problem for far too long.
Start here: get visibility into your architecture before AI accelerates the debt you can’t see. That visibility is how you move faster responsibly.
