6 Steps on How to Manage Technical Debt Without Sacrificing Innovation

In the era of rapid digital transformation, technical debt is an inevitable part of building innovative software products. Whether it's rushed timelines, the use of low-code/no-code, or the rapid gen AI adoption rates, nearly every team accumulates at least some tech debt. But while taking on debt can sometimes be a strategic move to achieve faster delivery, the real challenge lies in knowing how to manage technical debt before it begins to slow progress or stifle innovation.

That's why investing in legacy system modernisation has become so important in the digital age. CIOs are well aware that technical debt isn't just a budget issue, it's a business blocker. The financial strain of maintaining legacy code and patchwork systems is real, what keeps tech leaders up at night is missing innovation opportunities because of not knowing how to manage technical debt. As a result, innovation slows down and go-to-market timelines stretch.

In a world where adaptability is a new currency, technical debt quietly becomes one of the most expensive liabilities a company can carry. In this article, we'll break down what technical debt really is, explore why it matters beyond just cost, and share actionable strategies on how to manage technical debt effectively without sacrificing business innovation initiatives.

What is technical debt?

Technical debt refers to the cost of shortcuts in software development - decisions made to deliver quickly today, knowing they’ll require cleanup sometime in the future. Think of it like a business taking on a short-term loan: it can be a smart move if it enables faster progress, but if left unmanaged, the interest compounds and becomes increasingly expensive to repay.

Initially coined by Ward Cunningham, the term highlights the trade-offs between short-term gains and long-term codebase health.​ There are two main types of tech debt. Intentional debt is often strategic - such as launching a product quickly to seize market opportunity, with a clear plan to refactor later. Unintentional debt, on the other hand, creeps in through outdated legacy systems, inconsistent coding practices, or evolving business requirements. It’s this latter form that typically surprises executives, often surfacing as ballooning maintenance costs, slower release cycles, or fragile systems. 

Actually, there is a third and often overlooked type of technical debt - the environmental debt, which accumulates over time as your tech stack ages or falls behind modern development standards.

Types of technical debt:

• Intentional debt: In most cases, these are deliberate shortcuts taken to meet deadlines, with plans to refactor later.

Business example: Launching a product with minimal features to gain market traction quickly, with plans to optimise later.

• Unintentional debt: Results from lack of knowledge, poor design, or evolving requirements.

Business example: A development team unknowingly creates performance issues by scaling a microservices architecture without properly planning for inter-service data consistency.

• Environmental debt: Arises from outdated tools, frameworks, or infrastructure.​

Business example: Continuing to build on a monolithic application hosted on outdated infrastructure, which limits integration with cloud-native services and slows future development efforts.

Technical debt types by business impact

While it’s useful to categorise technical debt by source - intentional, unintentional, or environmental - it’s equally important to assess the quality of the decision that led to it. Martin Fowler’s quadrant-based model introduces another layer, classifying tech debt based on whether it was:

Deliberate or inadvertent - Were development teams aware of the technical debt or not?

Reckless or prudent - Was the decision made with due diligence or carelessness?

Each of these has a different risk profile. Reckless debt often requires the most urgent attention as it’s the kind that can break systems or erode trust. Prudent debt on the other hand, especially when deliberate, can be part of a sound innovation strategy - as long as it's tracked and repaid over time.

The key factor here is visibility. You can’t know how to manage technical debt, if you don’t know it exists, and you can’t mitigate risk if you’re not keeping track. By understanding not just where technical debt lives, but how and why it was created, executives can align teams around smarter, faster decisions.

Nevertheless, recognising the type of debt is just the beginning. To truly take control, we need to understand its broader business impact - the hidden costs that go far beyond code. Let’s take a look at them more closely.

What causes technical debt?

When determining how to manage technical debt effectively, it’s essential to be aware of what causes technical debt to accumulate in the first place. As illustrated in the table below, technical debt stems from multiple organisational dimensions.

Code-related technical debt

Ongoing maintenance burdens stem from various code-related issues, particularly outdated legacy systems and error-prone programming that require constant attention. At the same time, outdated programming languages limit modernisation efforts, their maintenance costs increase over time while causing tech debt to pile up over time. For example, some sectors like banking still run COBOL applications for core banking functions. Originally implemented in the 1970s, these mission-critical systems process millions of transactions daily but have accumulated substantial technical debt, making even minor changes risky and time-consuming.

Here, custom software development plays a crucial role in reducing technical debt during these modernisation efforts by creating purpose-built solutions that align precisely with business requirements rather than forcing business processes to conform to off-the-shelf software limitations.

Tech debt related to enterprise architecture

Enterprise architecture is another contributing factor to technological debt. In most cases, monolithic systems resist scaling and lack integration options, and complex IT portfolios that become increasingly difficult to untangle. Migrating applications to the cloud is an option here, although this strategic move needs careful planning. In most cases, however, the most effective approach is application modernisation alongside cloud migration. These might involve refactoring monoliths into microservices, implementing API integrations, or adopting cloud-native development practices.

Data-related technical debt

In terms of data, technical debt accumulates through isolated data silos, insufficient documentation, security vulnerabilities, and AI model drift that reduces accuracy over time. Let’s illustrate two of these with concrete examples. In the case of poor documentation, poor or missing records on data schemas, pipelines, and transformations can make systems hard to maintain or debug. For example, ​​if a data engineer leaves the company, and no one knows why a certain transformation was applied in an ETL job, this can lead to broken dashboards after a schema change. 

In the example with AI model drift, it is usual for AI and ML algorithms to lose predictive accuracy because the data distribution shifts away from the training set. Let’s say a recommendation engine trained on seasonal buying habits continues to suggest winter products in spring because it's not retrained frequently enough. A recommendation engine's failure to adapt to seasonal changes creates technical debt through accumulated workarounds, hard-coded exceptions, redundant systems, and undocumented fixes that ultimately make the entire system more brittle and resistant to improvement.

Tech debt related to talent and ways of working

Perhaps most overlooked are the talent and workflow factors causing tech debt. In this category are skills gaps, inadequate training, overspecialisation, outsourcing dependence, poor code-review culture, inefficient manual data entry processes, and rigid release cycles all contribute to technical debt buildup. For example, Google has come up with a "20% rule" allowing engineers to explore projects outside their primary expertise. This reduces overspecialisation debt by creating more versatile engineers who understand multiple system components. Similarly, we at Dreamix also invest in developing T-shaped skills for our software engineers, making them fit for highly dynamic Agile environments where adaptability and cross-functional collaboration are crucial success factors. 

The hidden costs of technical debt

As the Wall Street Journal puts it, technical debt has turned into an “invisible $1.52 trillion problem” for companies. A key finding from Accenture's digital core report reveals that industry-leading organiсations typically dedicate approximately 15% of their information technology budget specifically to technical debt reduction efforts. And accumulating tech debt can have significant side effects beyond just costs for maintenance:​

• Reduced agility: Complex codebases slow down development cycles. This burden extends beyond just slower coding - it increases onboarding time for new team members, complicates debugging processes, and multiplies the testing effort required before releases.
• Innovation at standstill: Unqork data reveals that 90% of respondents were burdened with some form of technical debt and that 80% faced delay or even cancellation of business critical projects as a direct consequence of it.
• Lower quality: Higher likelihood of defects and system failures. When software developers work within debt-laden codebases, they often introduce new bugs while implementing features or fixing existing issues, as the fragile, interconnected nature of compromised architecture creates a minefield of potential regressions. 
• Employee morale: Developers may experience frustration working with convoluted code. Talented developers thrive on solving meaningful challenges, not untangling spaghetti code or implementing awkward workarounds for architectural limitations.

Read next: Why good (clean) software architecture matters

Technical debt assessment strategy

The image below is a summary of the process of systematically addressing technical debt in organisations, indicating the continuous nature of technical debt management. 

The process begins with the first step, where cross-functional teams select applications for technical debt assessment analysis. Here, potential app candidates may be business-critical legacy systems with high maintenance costs, such as an aging CRM platform that handles core business operations but requires frequent patches.

In step two, teams define criteria by outlining clear measurements for impact and probability of risk for the identified applications. Clarifying impact and risk criteria is crucial for objectivity. It’s a good moment to align these with business KPIs such as time-to-market, system availability.

Step three involves individually scoring the technical debt items based on the established criteria. Scoring technical debt is necessary, but companies may consider utilising a standardised scoring model e.g. maintainability index, code complexity metrics to reduce subjectivity. 

Moving to step four, teams analyse the item scores and match them with business impact outcomes, costs, and urgency to prioritise remediation efforts. Linking scores to business outcomes is vital as it helps prioritise what to fix first based on ROI, not just technical severity. For high-priority items requiring significant remediation, custom software development provides the most effective approach. Unlike band-aid solutions or force-fitting commercial products, custom development can address the root causes of technical debt by redesigning problematic components with future maintainability in mind.

The final fifth step of the technical debt focuses on tracking, where remediation tasks are assigned to specific teams and progress is regularly monitored using dashboards. Effective implementation of this step is crucial for ensuring that technical debt reduction efforts translate into measurable business outcomes.

How to manage technical debt strategically

​Effectively managing technical debt requires a proactive, structured approach that balances immediate product development needs with long-term codebase health.

1. Trace and categorise technical debt

Before choosing how to manage technical debt, begin by conducting a thorough audit of your codebase to identify areas with high technical debt. Classify the debt into categories such as:​

• Critical blockers: Issues that hinder development or pose significant risks.
• Accelerating risks: Problems that, if left unaddressed, will escalate over time.
• Minor disruptors: Low-impact issues that can be scheduled for resolution some time in the future.​

2. Integrate debt management into development cycles

Allocating a consistent portion of each development cycle, such as 20%, specifically to address technical debt offers a strategic approach to its management. This dedicated time ensures continuous improvement of the codebase and underlying infrastructure without necessitating a complete halt to new feature development.

• "Debt-aware" definition of done

Make sure the development team's Definition of Done to include a mandatory "debt impact assessment" for every user story or feature. Allocate 20% of each sprint specifically to debt reduction tasks, prioritised collaboratively during sprint planning.

• Codebase quality gates with automated enforcement 

Configuring CI/CD pipelines to automatically reject code changes that don't meet predefined quality thresholds for complexity, test coverage, and duplication. These codebase quality gates would be complemented by team dashboards showing debt metrics alongside feature delivery metrics, creating transparency and accountability for both speed and quality.

• "Interest payment" budgeting

Assign quarterly debt reduction budgets to each product or system proportional to its estimated debt burden. Require teams to demonstrate specific refactoring outcomes and report progress to executives in financial terms, such as reduced maintenance costs or accelerated delivery timelines.

3. Translate technical debt into business impact

How to manage technical debt efficiently without knowing its business impact? Communicate the implications of technical debt in terms that resonate with stakeholders:​

• Market responsiveness: Delays in addressing debt can slow down the release of new features.
• Customer satisfaction: Persistent bugs or performance issues can erode user trust.
• Operational costs: Inefficient code may increase maintenance expenses.​

4. Leverage custom software development for critical systems

While commercial off-the-shelf solutions (COTS) can be expedient, they often introduce technical debt through unnecessary features or poor integration points. Custom software development allows organisations to build software systems precisely around their unique needs and business goals which minimises debt right from the outset.

5. Foster a culture of continuous improvement

Encourage practices on tech debt management that prevents the accumulation of new debt:

• Code reviews: Implement peer reviews to catch issues early.
• Refactoring: Promote regular code refactoring to improve structure and readability.
• Testing: Maintain comprehensive test suites to ensure code reliability.​

6. Craft a long-term debt management strategy

Develop a roadmap that outlines plans for addressing technical debt over time:​

• Regular assessments: Schedule periodic reviews to reassess and reprioritise debt items.
• Documentation: Maintain records of identified debts, their impact, and remediation plans.
• Metrics: Track progress using metrics such as reduced bug counts or improved performance benchmarks.​

Make sure your long-term strategy on how to manage technical debt remains aligned with overall business objectives.​ And speaking of metrics, let’s take a look at which metrics are vital when managing technical debt.

Key metrics to track technical debt levels

To maintain visibility on technical debt, executives should track a balanced scorecard of metrics:

Maintenance-to-Innovation ratio: That’s the proportion of development hours spent maintaining existing systems versus building new capabilities. When this ratio exceeds 50% maintenance, it signals excessive technical debt. Dev Pro Journal suggests that a good rule of thumb is to keep maintenance efforts under 40% of total development time. This indicates a healthy balance, allowing more focus on innovation.

Mean Time to Change (MTTC): How long it takes to implement a standardised change across different systems. Increasing MTTC indicates growing technical debt. The standard formulate to calculate this is:

MTTC = Sum of time required for all standard changes / Number of changes

Technical Debt Ratio: The financial relationship between remediation efforts and complete system replacement, calculated by dividing the projected cost of addressing all identified technical debt by the estimated expense of rebuilding the entire system from the ground up.

TDR = (Remediation Cost / Replacement Cost) × 100%

Here, remediation cost is the estimated cost to fix all known technical debt and the replacement cost is the estimated cost to rebuild the system from scratch.

Put in numbers: If your team estimates it would cost $200,000 to address all identified technical debt in a system but the replacement cost would be approximately $1,000,000, then:

TDR = ($350,000 / $1,750,000) × 100% = 20%

Deployment Frequency: This metric refers to how often new code can be safely deployed. Decreasing frequency often signals increasing technical complexity and debt.

Defect Escape Rate (DER): This is a metric DevOps teams use to measure the percentage of bugs discovered after code release rather than during testing. The higher the rates, the more degrading code quality gets in terms of accumulating technical debt. The DER formula is as follows:

DER = (Number of defects found in production / Total defects found) × 100%

Here, the "Number of defects found in production" refers to bugs discovered after code is released to production, while the "Total defects found" includes all defects (both those caught during testing and those that escaped to production). For example, if five bugs were discovered in production after release and 95 bugs were caught during testing before release, the total defects would be 100 (5 + 95), so DER would be 5%. 

Wrap up: Balancing tech debt and innovation

Innovation rarely happens in a perfectly clean codebase. In fact, some level of technical debt is not only inevitable, it’s often a necessary part of moving fast and staying competitive. The key is knowing how to manage technical debt so it becomes your strategic progress enabler. In many cases, taking on strategic debt - such as prioritising speed-to-market over long-term scalability - can be a smart move, as long as there’s a clear plan for refactoring later. 

Some of the most successful organisations recognise that how to manage technical debt effectively is about finding the right balance - not eliminating it entirely. By implementing structured assessment processes, establishing governance frameworks that span across code, architecture, data, and talent dimensions, and consistently allocating resources to debt reduction, companies can create a sustainable approach to technical health. 

Custom software development provides organisations with a strategic advantage in managing technical debt through tailored solutions that address specific technical challenges while aligning with business objectives. By partnering with experienced development teams, companies can implement targeted modernisation efforts that systematically reduce tech debt in legacy systems while introducing clean, maintainable architectures designed specifically to support long-term innovation goals.