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Summary
Technical debt management helps you control the hidden cost of shortcuts, outdated components, and structural weaknesses across your software landscape.Â
Technical debt is not just a development concern. In large organizations, it shows up across dozens or hundreds of systems that depend on each other and compete for the same budget.Â
When debt is visible and prioritized, teams can reduce maintenance overhead and improve productivity, helping them make smarter investment decisions.Â
The goal is not to remove every issue at once, but to manage technical debt deliberately so quality, risk, and business value stay in balance.Â
What technical debt management means
Technical debt management is the discipline of identifying, quantifying, prioritizing, and reducing software issues that make change slower, riskier, or more expensive over time.Â
That includes code-level problems such as duplication and high complexity, but also broader concerns like outdated dependencies, architectural bottlenecks, weak test coverage, and patterns that increase security or compliance risk.
Well-managed technical debt is treated as a portfolio decision, not just an engineering complaint. It gives IT leaders and software teams a shared way to decide which issues should be fixed now, which can be scheduled later, and which are acceptable trade-offs for business reasons.
Why technical debt becomes expensive
Deloitte’s 2026 Global Technology Leadership Study estimates that technical debt accounts for 21% to 40% of an organization’s IT spending.
Technical debt rarely hurts only one team. As it accumulates, even small changes start taking more effort because developers have to work around fragile code, inconsistent design decisions, or aging technology. That typically leads to slower releases, more rework, higher maintenance costs, and less capacity for innovation.
For enterprise environments, the impact often extends beyond developer productivity. Technical debt can increase operational risk, weaken security posture, complicate compliance efforts, and make portfolio planning less reliable.
When leaders lack visibility into where debt sits and what it costs, investment decisions become harder to defend, especially without understanding the cost of technical debt.
And this is exactly the challenge.
“technical debt is difficult to measure, unique to every organization” – DeloitteÂ
“Technical debt is like dark matter: you know it
exists, you can infer its impact, but you can’t see or measure it” – McKinseyÂ
“Running continually on a tech debt treadmill is not helping organisations to move forward.” – Forrester
But, fear not, technical debt can be measured — quantified as the cost of repairing maintainability issues to reach an acceptable quality level — and once it is visible, it can be managed deliberately, prioritized by impact, and reduced over time.
A little while ago, I sat down with Ger Cloudt in the SIGNAL podcast to talk about managing technical debt in large organizations. We discussed why technical debt is inevitable and (surprise) not always a bad thing. Indeed, it can be a necessary trade-off to move faster. Other times, paying it down can boost productivity and reduce long-term costs.
Does AI create more technical debt?
AI can help reduce technical debt through automated analysis, faster refactoring, and smarter prioritization.
But, that’s one specific use case focused on automating quality control. Meanwhile AI is also used to write code with the functional aspect in mind.Â
AI-assisted and agentic development helps teams produce software faster, but faster change does not increase quality. Recent research points that AI adoption in software development causes technical debt to increases 30-41%.
The debt that accumulates tends to be architectural: the accumulated cost of structural compromises in how software systems are designed and connected.
Unlike code-level debt — messy functions, duplicated logic — architectural debt affects how entire systems interact, how components depend on each other, and how a portfolio scales under pressure. It is also the hardest to fix retroactively.
Because it based on exactly the thing that AI is typically not great at: context.
This is where AI typically falls short. Writing a function is easy. Understanding how a change in one service ripples across dozens of downstream systems requires the kind of contextual judgment that AI tools do not yet reliably provide.
Common signs your organization needs stronger technical debt management
- Delivery slows down even when team size and demand stay relatively stable
- Bug fixing consumes roadmap capacity that should go to new features or modernization
- Changes have unpredictable impact because systems are tightly coupled or poorly understood
- Maintenance spend keeps rising without a clear improvement in software quality
- Security and compliance findings recur across the same applications or components
- Refactoring decisions are debated repeatedly because there is no shared prioritization model
- Leadership lacks portfolio-level visibility into software risk, quality, and remediation cost
- New debt is being added faster than existing debt is resolved, with no process in place to prevent it
What to include in a practical technical debt strategy
An effective technical debt strategy connects engineering reality to business priorities. Rather than relying on isolated clean-up efforts, it should create a repeatable way to assess debt, rank remediation opportunities, and track progress over time.Â
1. Establish a shared definition
Teams often label very different kinds of work as technical debt. Start by separating debt from routine defects, feature work, and optional technical improvements. A shared definition reduces backlog noise and makes prioritization more consistent across engineering, architecture, product, and leadership.
2. Make debt visible
You cannot manage what you cannot see. Debt should be documented through objective signals such as code quality and maintainability indicators, architecture issues, dependency health, security concerns, and recurring delivery friction. Visibility matters at both application and portfolio level.Â
3. Prioritize by impact, not volume
Not all technical debt needs to be resolved. Part of a good strategy is consciously deciding which debt to accept as a trade-off, and which to act on.
Focus on debt that affects business-critical systems, blocks delivery, raises risk, or drives avoidable maintenance effort. This is where technical debt management becomes valuable: it helps you choose the highest-ROI improvements instead of chasing the longest issue list.
4. Plan remediation as part of normal delivery
Technical debt reduction works best when it is built into planning and governance, not treated as an occasional rescue exercise. Teams may reserve capacity for structural improvements, combine refactoring with feature work, or schedule targeted remediation initiatives for high-risk areas.
5. Track outcomes over time
Good technical debt management does not stop at identifying tech debt. You also need to monitor whether remediation reduces risk, improves maintainability, and creates more development capacity. Without follow-through, debt tracking becomes reporting rather than control.
How to prioritize technical debt remediation
The most useful prioritization model balances technical severity with business consequences. A low-quality component in a rarely changed internal tool may deserve less attention than a moderate issue in a customer-facing platform that changes every sprint.
In practice, prioritization usually becomes clearer when you evaluate technical debt against a small set of factors:
- Business criticality – How important is the application or component to operations, revenue, or customer experience?
- Change frequency – How often does the team need to modify this part of the system?
- Risk exposure – Does the debt increase security, compliance, reliability, or operational risk?
- Maintenance burden – How much time is being lost to workarounds, defects, and difficult changes?
- Remediation effort – Can the issue be improved incrementally, or does it require larger architectural action?
- Test coverage — Is the component well-tested? Debt in frequently changed code with weak test coverage carries higher risk than the same issue in well-tested code.
This approach helps you avoid two common mistakes: fixing only what is easy, or postponing debt until modernization becomes far more expensive.
Measuring technical debt in a way that supports decisions
Technical debt is not just a code smell count. If the goal is better decisions, measurement should combine software quality signals with cost, risk, and business context. Useful measures often include maintainability indicators, complexity, dependency health, architecture findings, security weaknesses, and the effort associated with remediation.
At portfolio level, measurement becomes even more valuable when it helps answer practical questions such as:
- Which systems carry the highest concentration of technical risk?
- Where is debt most likely to slow delivery or increase maintenance cost?
- Which remediation actions offer the best return on investment?
- How is debt trending over time across the software portfolio?
This is why many enterprises combine software analysis with governance and expert review. Some code-analysis platforms can continuously monitor technical debt, software quality, and risk, highlighting high-impact issues, while consulting support can help organizations quantify and prioritize remediation in line with business goals using technical debt metrics.
Technical debt management in agile and product delivery
Technical debt management should support delivery, not compete with it. In agile environments, the strongest approach is usually to make debt visible in planning, include quality expectations in the definition of done, and prevent known structural issues from being pushed indefinitely into the future.
That does not mean every sprint needs a separate debt initiative. It means teams need a disciplined way to decide when debt should be addressed within stories, when it should be planned as explicit remediation work, and when it should be escalated because it threatens delivery or risk targets.
Where this breaks down, the pattern is familiar: feature pressure wins every planning discussion, debt stays under-documented, and software quality erodes until larger and more expensive interventions become unavoidable.
Examples of technical debt worth addressing early
- Outdated dependencies that increase security exposure or make upgrades harder
- Tightly coupled components that turn small changes into high-risk releases
- High-complexity modules that repeatedly generate defects or slow onboarding
- Weak automated test coverage in systems that change frequently
- Architecture issues that limit scalability, resilience, or maintainability
These issues matter because they create recurring cost. The earlier you identify and rank them, the easier it is to reduce debt in manageable increments.
Building a more controlled approach
If your organization manages a growing software portfolio, technical debt should be governed with the same discipline as other strategic IT risks. That means combining visibility, prioritization, and action rather than relying on periodic clean-up efforts alone.
Software Improvement Group supports this with Sigrid and expert consulting services that help organizations quantify technical debt, assess software quality, identify refactoring priorities, and build a clearer roadmap for reduction. For organizations involved in software-intensive acquisitions, the same analysis can also support due diligence through code analysis, benchmarking, architecture investigation, and cost modeling. Teams looking to reduce tech debt with Sigrid can use this approach to turn visibility into action.
About the author
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 management?
Technical debt management is the process of identifying, measuring, prioritizing, and reducing software issues that increase maintenance cost, delivery friction, or risk over time.
What are the 4 quadrants of technical debt?
The technical debt quadrant classifies debt along two dimensions: deliberate versus inadvertent, and prudent versus reckless. It helps teams distinguish between intentional trade-offs and debt created by poor decisions or lack of understanding.
What are the 4 types of technical debt?
Technical debt is often categorized in various ways (for example, code debt, architecture or design debt, infrastructure or platform debt, and process debt), but there is no single authoritative list of exactly four types.
What is an example of technical debt?
A common example is postponing dependency upgrades to meet a release deadline. The software ships faster in the short term, but later changes become harder, security risk can rise, and the eventual remediation effort becomes larger. If readers need a clearer foundation first, they can review what technical debt is.