In this article
Summary
Technical debt represents software quality issues that slow down future development. It is the extra effort teams spend maintaining and changing software because earlier implementation choices were suboptimal.
Like financial debt, shortcuts can save time in the moment, but they create interest later through higher maintenance effort, slower delivery, and more risk.
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.
This article gives you a clear definition, the key types and consequences, and practical steps to find, prioritize and reduce debt in software development and Agile product delivery.
A practical definition of technical debt
Technical debt is the extra effort you incur in the future because of shortcuts or suboptimal choices you make today. Think of it as a useful but risky trade-off between time, quality and cost.
In Scrum and Agile, teams often accept debt to meet a deadline or validate a hypothesis. If you track it, add tests and refactor later, the interest stays low. If you ignore it, that interest compounds as slower change, more bugs, security exposure and higher maintenance costs.
Tech debt spans beyond code – it includes architecture drift, manual deployments, weak documentation, missing tests and the rise of AI doesn’t mean we’ll see less of it anytime soon.
Types of technical debt
According to Martin Fowler, “technical Debt is a metaphor, so the real question is whether or not the debt metaphor is helpful about thinking about how to deal with design problems, and how to communicate that thinking. A particular benefit of the debt metaphor is that it’s very handy for communicating to non-technical people”.
He also mentions that the more practical distinction is not debt versus non-debt, but prudent versus reckless debt. That helps teams explain which shortcuts were intentional, what risk they create, and what to address first.
Martin Fowler’s Technical Debt Quadrant
|
Quadrants (Fowler) |
Prudent | Reckless |
|---|---|---|
|
Deliberate |
Planned shortcut with a clear payback plan |
Knowingly cut corners without mitigation |
|
Inadvertent |
Learned better later – then improve |
Did not know and did not seek to learn |
These two perspectives can help you classify debt so you can manage it more effectively. At the end of the day, it’s all about perspective. Sometimes technical debt (or financial debt for that matter) is deliberate and helpful.
That said, there are many different types of technical debt:
- Architecture debt – tight coupling, missing boundaries, outdated frameworks that block scalability and change.
- Code debt – complexity, duplication, low cohesion, poor naming and lack of tests that slow developers down.
- DevOps and infrastructure debt – manual builds, fragile pipelines, snowflake servers, missing observability.
- Process debt – weak Definition of Done, skipped reviews, unclear requirements that create rework.
- Security debt – unpatched libraries, insecure defaults, missing threat models and secret management.
For an overview of key categories and blind spots, see Five types of technical debt that are often overlooked.
Causes and consequences when technical debt grows
Common causes include deadline pressure, sudden scope changes, inexperienced teams, legacy constraints, rushed vendor handovers, weak test automation, and lack of clear architecture principles. In product management, prioritizing features over quality for too long quietly accumulates debt. In DevOps, manual release steps and flaky tests create operational drag.
As technical debt increases, you will see:
- Slower time-to-market – more time spent understanding, fixing and retesting.
- Higher costs – maintenance effort rises and feature work stalls.
- More incidents – defects, outages and security vulnerabilities surface in production.
- Compliance risk – especially in regulated industries with audit and security obligations.
- Lower morale – engineers avoid fragile areas and onboarding takes longer.
Generally speaking though: the longer you wait, the higher the interest. Refactoring small slices early is far cheaper than large rewrites later.
For a business impact and ROI perspective, watch this Fireside Chat I had on stage with Cor van Herk, Director of IT at Keylane Life & Pension.
Managing technical debt
For core concepts, common causes and a practical approach, you should get familiar with Technical debt management.
Technical debt management is an outcome of good Continuous Software Portfolio Governance, not a separate discipline. When organizations govern the portfolio well, technical debt becomes visible, measurable, and manageable.
Managing technical debt requires shared metrics, clear ownership, and continuous measurement across the software portfolio.
Prioritize reduction within your roadmap
Resources are limited. Refactoring, retiring, or modernizing one system affects the rest of the portfolio. It’s important to invest where technical debt matters most.
If you don’t have access to a platform that can automate this, you can still do the following:
- Create a visible tech debt register with concise entries: location, impact, quadrant, estimated interest per sprint and a proposed fix.
- Prioritize by value at risk – what blocks strategic goals or creates operational risk.
- Mix debt items into every sprint, use refactoring tasks alongside features and schedule targeted debt sprints when needed.
- Tighten your Definition of Ready and Definition of Done so new work does not add fresh debt.
Track with metrics and governance
Use measurable quality indicators to monitor debt over time. Helpful metrics include complexity and duplication, test coverage, build and deployment frequency, lead time for changes, change failure rate and mean time to recover.
Organizations need a consistent way to see technical debt across applications, teams, and platforms, and to define what healthy looks like. With Sigrid®, teams can analyze software quality across the portfolio, prioritize improvements by impact and ROI, and continuously monitor risks that affect delivery, maintainability, and security.”.
Not all technical debt is equally urgent. Decisions to remediate should reflect business priorities. Resolving a seemingly unending list of findings and aiming for zero, is not the advised way forward.
Build the right engineering culture
Adopt the boy scout rule – leave the code a bit better each time. Encourage pair reviews and shared ownership. Invest in a solid test pyramid, reliable CI and repeatable deployments so improvements are safe and fast. Celebrate paying down high-interest debt the same way you celebrate shipping features.
Is technical debt always bad?
In a podcast episode, I spoke to Ger Cloudt, author, trainer, and Software Quality Manager at ASML. Together, we discussed how technical debt is inevitable and not always a bad thing. Sometimes, it’s a necessary trade-off to move fast. Other times, paying it down can boost productivity and reduce long-term costs.
Learn how to spot the technical debt that matters, what makes it manageable, and why context is everything, in just over 30 minutes.
Generative AI and technical debt
Generative AI can speed up delivery, but it can also create new debt if you accept code blindly.
GenAI’s error rates can reach 25% depending on the use case, yet Gartner finds that 84% of CIOs and IT leaders don’t have a formal process to track AI accuracy.
Here’s why: AI coding assistants make unexpected mistakes, lack full system and architectural context, and rely on pattern-based association rather than true understanding. A great breeding place for technical debt to grow.
To get the upside from AI assisted coding without the interest spike in tech debt, set guardrails: standard prompts, architectural guidelines, mandatory reviews and automated checks for security and quality. Use AI where it shines – scaffolding tests, writing docstrings, generating refactor candidates – and require developers to validate behavior with reliable tests. Keep the human in the loop, especially important with the rise of Agentic AI, where multiple agents are now able to create complex systems in a fraction of the time it would take humans to write it manually.
How Software Improvement Group helps
Software Improvement Group identifies, quantifies and manages technical debt across full software landscapes.
With the Sigrid platform and services such as Software Portfolio Scans, Software Risk Assessments and Technical Due Diligence, you get fact-based insights, benchmarks and an actionable roadmap to reduce risk, speed up delivery and reduce technical debt where it matters most for your organization.
If you want to get an up-to-date overview of technical debt (and highlight refactoring candidates with the highest ROI potential) in your organization, feel free to reach out. If you want to learn more about our approach to managing technical debt, watch this video:
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.
Helping organizations govern the software their business runs on by enabling control of software cost, risk & speed in the AI era| Business–IT alignment | Host of the SIGNAL podcast | Senior Director at Software Improvement Group.
FAQs
What is meant by technical debt?
Technical debt is the future extra effort you must spend because of shortcuts or suboptimal solutions you accept today. Just like financial debt, it can be a smart lever for speed if you track it and pay it down, or a costly drag if you ignore it.
What is an example of technical debt?
Shipping a feature without integration tests to hit a launch date is deliberate debt. You save time now but pay interest later as slower releases, more defects and added refactoring to add the missing tests and decouple modules. See more Technical debt examples and software failure cases.
Is technical debt good or bad?
It depends on intent and management. Prudent, deliberate debt aligned with a clear payback plan can be good. Reckless or ignored debt accumulates interest and becomes bad, harming velocity, quality and security.
What are the 4 quadrants of technical debt?
The quadrants combine intent and behavior: deliberate-prudent, deliberate-reckless, inadvertent-prudent and inadvertent-reckless. They help you judge risk and decide which debt to address first and which guardrails to add to prevent new debt.