In this article
Summary
The cost of technical debt is not limited to a future refactoring project. It shows up every day in slower delivery, rising maintenance effort, higher operational risk, and reduced room for innovation. For technology leaders, the real challenge is not simply knowing that debt exists, but understanding where it creates the greatest business impact and how to act on it with confidence.
On this page, technical debt is treated as a portfolio-level cost issue as much as a code-level one. That matters because technical debt can lurk in system architecture or processes rather than a single obvious code module, and it can absorb budget, delay change, or increase risk across critical systems.
The scale is larger than most teams assume. Deloitte’s 2026 Global Technology Leadership Study estimates that technical debt absorbs 21% to 40% of total IT spending. For every €100 an organization spends on IT, between €21 and €40 goes toward servicing debt rather than delivering new value.
What technical debt means in practical terms
Technical debt is the consequence of software and architecture decisions that are expedient in the short term but create extra cost or effort later. Sometimes that tradeoff is deliberate, for example to meet a market deadline. In many cases, it accumulates gradually through postponed upgrades, inconsistent coding practices, outdated dependencies, weak architecture choices, or years of incremental change in legacy systems.
The reason the debt metaphor works is that these choices create an ongoing repayment burden. Instead of financial interest, organizations pay through additional maintenance, more defects, slower changes, harder integrations, and a growing need for specialist knowledge to keep systems running. For readers looking for a foundation, this is essentially what technical debt is in practice.
Where the cost of technical debt appears
Technical debt costs often manifest as slowed releases and extra maintenance work across projects, rather than a distinct budget line. Instead, it accumulates in daily delivery friction.
Maintenance overhead – More time spent fixing, patching, testing, and working around fragile systems. State of Software 2026 puts a number on this. Moving a single system from a maintainability level that flags a delivery risk (2-star) to the recommended threshold (4-star) frees roughly 5.8 FTE, about €870,000 per system per year in recovered capacity. Across a portfolio the math compounds: a large enterprise running 10 systems at 2-star quality carries close to €9 million per year in avoidable maintenance overhead.
Lower development capacity
Teams spend less time on new features because existing complexity absorbs effort.
Longer time-to-market
Changes take more analysis, more coordination, and more regression testing. Architecture quality has a direct effect here. The State of Software 2026 report, shows that strong architecture cuts issue-resolution time by 30%.
Operational instability
Outages, performance issues, and production incidents become more likely and more expensive.
Security and compliance exposure
Outdated components and weak architecture increase risk and remediation effort. New statThe link is measurable: systems with 4-star maintainability carry a 72% higher security rating than 2-star systems, and 3-star systems a 36% higher rating.
Higher integration cost
High technical debt can complicate integrations and M&A, increasing integration risk and the effort required to connect new platforms or data flows.
Customer and revenue impact
Technical debt can hurt customers and revenue at the same time. When architectural debt in Southwest Airlines‘ 1990s-era crew-scheduling system buckled under peak holiday demand in 2022, it stranded more than 16,000 flights and led to roughly $600 million in refunds and over $140 million in penalties. For more cases, see our technical debt examples.
Seen this way, the cost of technical debt is both direct and indirect. Direct costs include higher maintenance spend and remediation work. Indirect costs include opportunity cost: delayed launches, missed efficiencies, and innovation that never happens because teams are tied up preserving the status quo. This financial pressure is closely related to the dynamics described in How technical debt affects IT budgets.
Why technical debt becomes more expensive over time
Technical debt compounds because as systems age and complexity grows, every change tends to touch more fragile dependencies, require more specialist knowledge, and trigger more testing and coordination. The result is a cycle that is difficult to break:
- Teams take shortcuts or postpone structural improvements.
- Code and architecture become harder to understand and change.
- More effort goes to fixes, workarounds, and support.
- Less capacity remains for preventative improvement.
- New change introduces even more debt.
This is one reason technical debt often becomes a business problem before it is recognized as one. Delivery slows gradually, maintenance budgets rise incrementally, and risk accumulates until a major transformation, migration, audit, or incident exposes the true cost. In many cases, this overlaps with insights from The cost of poor code quality.
How much does tech debt cost?
There is no universal number because the answer depends on the size of your portfolio, the business criticality of affected systems, and the nature of the debt. A heavily used legacy platform supporting core operations can create far greater cost exposure than a poorly structured internal tool with limited business impact.
The State of Software 2026 report makes the order of magnitude concrete.
Using an average loaded developer cost of €150,000 per year, the direct labor cost of poor maintainability runs to roughly €870,000 per system per year for a 2-star system, and close to €9 million per year across a 10-system portfolio held at that level.
These figures cover engineering time alone. They do not yet count the token cost of AI-assisted maintenance, which is becoming a second variable cost line.
In practice, organizations should think about cost in three layers:
- Current cost – the maintenance effort, incident handling, and inefficiency you are already paying for.
- Change cost – the extra time and complexity added whenever teams need to modify the software.
- Future risk cost – the financial exposure linked to failure, security issues, compliance gaps, or delayed modernization.
That is why a simple estimate based only on refactoring hours is usually too narrow. The true cost of technical debt includes the software’s drag on delivery speed, resilience, and strategic flexibility.
Why AI does not erase the cost
A common assumption is that AI removes the problem: if agents generate the code, they can regenerate a cleaner version on demand, so the debt is disposable.
At Software Improvement Group, we analyzed Cursor’s FastRender experiment. A swarm of AI agents built a working browser engine, more than 3 million lines of code, in a single week. It was estimated that 10-20 trillion tokens were consumed, which would have cost several million dollars.
When SIG ran the output through Sigrid®, it scored 1.1 out of 5 for maintainability and 2.2 out of 5 for architecture.
AI is cheap at fixing code-level debt such as duplication and overly complex functions, but it does not resolve architectural debt, which needs system-wide context an agent does not hold. Gartner predicts that architectural debt will account for 80% of all technical debt by 2027, and that by 2028 AI will create more architectural debt than it solves.
Token spend is becoming a maintenance cost line in its own right, alongside the FTE time that debt already locks up.
In a recent SIGNAL podcast episode, I sat down with Luc Brandts, CEO of Software Improvement Group to discuss AI tokenomics and the real cost of AI coding.
How to calculate technical debt
Calculating technical debt is best approached as an estimation exercise grounded in evidence, not a single formula. A useful model combines software quality findings with operational and business context.
1. Identify the debt
Start by locating the technical and architectural issues that create excess cost or risk. This can include maintainability weaknesses, outdated technologies, structural complexity, unsupported dependencies, security issues, and systems that are difficult to evolve.
2. Estimate remediation effort
For each issue or system, estimate the effort needed to bring it to an acceptable level. This may involve refactoring, replacement of components, modernization, improved test coverage, or architectural remediation.
3. Quantify the ongoing penalty
Then assess what the organization is currently paying because the debt remains in place. Typical indicators include increased maintenance effort, incident frequency, delayed releases, slow onboarding, and higher support load.
4. Add business criticality
Not all debt has equal importance. Debt in a revenue-generating platform, a regulated environment, or a system central to customer operations should be weighted more heavily than debt in low-impact internal software.
5. Prioritize by business impact
The most useful outcome is not a theoretical total, but a ranked view of where debt reduction will create the highest return. This supports practical decision-making about what to fix now, what to monitor, and what to accept temporarily.
For many enterprises, this requires visibility across the full software estate rather than isolated application reviews. That is also why portfolio governance matters: it helps separate expensive debt from merely untidy code. Teams often support this work with Technical debt metrics to quantify and track progress.
Get your Technical Debt Quick Scan
Common sources of high-cost debt
Not every form of technical debt creates the same financial pressure. The cost usually rises fastest when debt affects changeability, stability, or business-critical operations.
- Legacy platforms with outdated technologies – Harder to hire for, integrate, secure, and modernize.
- Poor architectural choices – Tight coupling and weak boundaries increase the impact of every change.
- Low code maintainability – Teams need more time to understand, test, and safely modify software.
- Deferred upgrades and dependencies – Security, compliance, and support risks increase as components age.
- Inconsistent engineering practices – Weak testing, limited standards, and insufficient review create recurring quality issues.
For a budget-focused perspective on maintainability, see How poor maintainability drains IT budgets.
These are not just technical concerns. They influence budget allocation, roadmap reliability, post-merger integration, and the ability to introduce AI, cloud, or other modernization initiatives without adding further complexity.
How to reduce the cost without slowing the business
Reducing technical debt does not mean pausing delivery until everything is clean. In most organizations, that is neither realistic nor necessary. The better approach is controlled remediation aligned with business priorities.
- Create visibility – Build a clear view of debt across systems rather than relying on anecdotal team feedback.
- Focus on the highest-impact areas – Prioritize systems where debt slows delivery, raises maintenance cost, or increases business risk.
- Track over time – Treat debt as a managed portfolio issue, not a one-time cleanup project.
- Integrate remediation into delivery – Combine feature work with structural improvement where it reduces future cost.
- Use evidence for investment decisions – Link technical findings to budget, risk, and strategic outcomes.
The return is measurable. A peer-reviewed study published in April 2026 found that systematic remediation of architectural debt delivers median returns of 437% over 24 months, with a break-even point of 6.2 months. That is consistent with what SIG sees across its client base: architectural improvement is one of the highest-return investments an IT organization can make.
For organizations that need this level of visibility, Software Improvement Group supports Systematic technical debt management with the Sigrid platform and expert services focused on portfolio insight, prioritization by business impact, and continuous tracking over time.
For a faster diagnostic starting point, the Technical Debt Quick Scan will give you everything you need to know about up to 5 systems.
Werner Heijstek
Werner Heijstek is the Senior Director at Software Improvement Group and host of the SIGNAL podcast, a monthly show where we turn complex IT topics into business clarity.
FAQ
What is technical debt in simple terms?
Technical debt is the extra effort and cost created by software shortcuts, outdated technology, or postponed improvements that make future changes harder.
What are the 5 types of technical debt?
SIG groups technical debt into five types: code-level debt, architectural debt, process-level debt, data-level debt, and legacy-level debt. Architectural debt, the kind that sits between systems rather than inside a single module, usually carries the highest business impact. For a breakdown of each, see the five types of technical debt that are often overlooked.
How do you know which technical debt to fix first?
Prioritize the debt that has the strongest effect on business-critical systems, delivery speed, maintenance cost, operational risk, or compliance exposure. High visibility and impact-based ranking are more useful than trying to fix every issue equally.
When debt cost needs to be quantified more rigorously
Some situations require more than a high-level estimate. If you are preparing for modernization, evaluating software-intensive acquisitions, or defending major remediation budgets, you may need a quantified view of software value and cost impact.
In those cases, software-focused due diligence can help assess how technical debt affects maintainability, risk, and future cost projections. This is especially important when hidden software issues could distort valuation or create post-transaction integration problems. For more practical guidance, you can explore how to manage technical debt in a systematic way.