What leaders get wrong about the latent cost of technical debt 

Estimated reading time: 9 minutes

Calculating the impact of tech debt and building a management strategy.

Accruing technical debt isn’t necessarily a negative issue, and usually starts with good intentions. For instance, you may have made the call to accept a tech debt trade-off with the view to finishing a feature or meeting a deadline. 

But what happens when this becomes the norm – an ingrained component of the culture? I have seen the process of teams normalizing broken tests, sluggish builds, and tribal knowledge to the point that no one asks questions about it anymore. 

The longer this debt remains unaddressed, the more it interferes with the velocity of development and cognitive load, demotivating team members. 

However, knowing how to quantify debt, strategically prioritizing it, and communicating it in a way that does not cause panic is the key. 

How tech debt can slowly infect your systems and teams

When debt manifests as operational instability

Technical debt is not always about messy code; it is the instability that sneaks into the operations. Even the slightest quality problem in a regulated industry can turn into an existential threat. For example, an FAA audit conducted on Boeing found that the airplane company failed 33 out of 89 quality audits of their 737 Max production, because they were not following approved manufacturing processes. 

What is the cause of most of these failures? Missing documentation, haphazard tooling, and a lack of process; technical debt at scale. Similar to the weak APIs and undocumented programs in our world, these were not merely bugs, but time bombs that had systemic implications.

The parallel fragility in software teams is that the mean time to recovery (MTTR) is slower, and more often, rollbacks or on-call escalations are required. 

You may not be building planes, but when a service fails and no one understands who owns the fallback logic or how it is tested, you are closer to the risk than you imagine.

The silencing effect of accumulated complexity

Systems are bound to become more complex as they evolve. However, when technical debt accumulates without oversight, such as a lack of documentation, unclear ownership, or postponed refactoring, it accelerates complexity with no structural checks in place. When I was working in a fintech company, we learned that more than 40% of their microservices lacked identifiable owners, alongside the fact that uneven test coverage was rampant. Although the engineering department had grown at a high rate, no one bothered to assess the structural debt being incurred; issues such as tightly coupled services, legacy monoliths with hardcoded integrations, or ownership gaps that made critical systems unmaintainable.

These findings illustrated how entrenched silence was in the team culture. Engineers cease to raise issues since this is how things are. New employees do not challenge inconsistencies since they presume it is deliberate. This normalization is what makes technical debt so dangerous; it becomes unseen, but highly influential.

Strategic cost isn’t just financial

In addition to operational anarchy, debt constrains strategic options. It traps teams in unstable architectures, makes experimentation less desirable, and change more expensive. In Equifax, a known Apache Struts vulnerability patch was missed in 2017, resulting in one of the most significant breaches of consumer data in history: 148 million individuals. The autopsy showed that although Homeland Security had warned Equifax, the company was unable to identify and patch the vulnerable systems. Why? Inefficient inventory maintenance and an architecture that was unclear and untransparent.

The moral is obvious: technical debt minimizes optionality. It denies organizations the flexibility to react to threats or innovate promptly when needed.

Building a debt inventory that works

Start with full-scope visibility

I’ve worked with teams that have uncovered hidden technical debt by combining several approaches: running engineering surveys, conducting service maturity audits, and analyzing operational metrics. These efforts often revealed debt artifacts like unowned scripts, deprecated libraries, or undocumented APIs – elements that rarely show up in standard project tracking tools. 

Without a structured inventory like this, teams often focus their efforts on the most obvious pain points, such as slow tests or deployment delays, rather than the most strategically important ones. Full-scope visibility means going beyond surface issues to identify and document what’s genuinely slowing down delivery, scaling, or incident response.

A more modern strategy for understanding the scope of your tech debt issue incorporates telemetry-driven scans. These will be able to surface broken pipelines and flaky tests. It’s also important to gather qualitative feedback: developer pain points, support tickets, and onboarding feedback. If new engineers repeatedly encounter setup failures or unclear integration steps with a specific legacy module during onboarding, that module is a visibility gap. It’s not just a one-time inconvenience; it reflects debt that directly affects developer experience and onboarding velocity. These recurring issues should be logged and scored, as they indicate systemic friction with measurable impact.

At one point, we ran a cross-team maturity assessment; a structured review of service ownership, monitoring, and test coverage, across all engineering squads. This helped us identify that nearly 20% of services lacked basic observability hooks and were failing silently in production. After prioritizing this visibility gap, we embedded logging, tracing, and service-level objective (SLO) dashboards. Within six weeks, incident response time dropped by 38%.

Score by impact, not just frustration

Not every debt is alike. An abandoned configuration file is an inconvenience to engineers, but a closely-coupled authentication system that drags every product update has significantly steeper consequences. I suggest a light scoring model that is determined by three factors:

• Severity: What is the downstream risk of this debt going unaddressed?
• Frequency: How frequently does it create issues?
• Strategic impact: Does the debt limit your ability to scale systems, like handling more users, data, or teams? Does it impede your ability to adapt your product direction, e.g., shift to a new architecture, integrate with new services, or launch a different feature? 

With a simple scoring system (e.g., 1-5), you will have a shared language to compare debts between teams and make decisions on what to work on first.

Using this model, one back-end team identified a legacy queue system that was adding 15–20 seconds of latency during peak usage. After replacing it with an event-driven architecture, latency dropped to sub-second levels, and support tickets for that flow were reduced by 80%.

Elsewhere, an e-commerce platform I consulted with recently used this scoring model to confront its tech debt. They found that three of their most requested customer features were blocked by just two architectural decisions made four years ago. Instead of refactoring everything, they reprioritized just those two items and unblocked months of roadmap work. The insight? Debt isn’t solved by scope; it’s solved by relevance.

Designing a sustainable debt management strategy

The 70/20/10 allocation model

A common issue leaders face when dealing with tech debt is the time it takes to tackle without compromising delivery. The 70/20/10 model has served our team well: 70% on roadmap delivery, 20% on medium-term technical health, and 10% on long-term cleanup or experiments. This brings predictability to the product stakeholders and provides engineers with breathing space to solve what is blocking them.

This allocation should be made clear by the leaders. Never hide paying off debt by calling it regular backlog work – it should be treated as top-quality work, reviewed and tracked just like features.

Choose the right fix: Refactor, replace, or bypass

Not all debt is meant to be repaid. Some should be sunset, documented, or left untouched until the cost outweighs the effort. A helpful triage method I’ve used:

• Refactor when the debt compounds daily costs; things like developer frustration, poor test coverage, or sluggish performance. For instance, in one of our back-end services, a shared utility function was frequently modified and regularly broke downstream dependencies. A simple refactor to isolate concerns reduced change failure rates by over 30% in just two sprints.
• Replace when you’re scaling past its original intent, e.g., hardcoded workflows or in-memory stores. At a previous role, our real-time analytics relied on an in-memory store that had no sharding or durability guarantees. It worked at launch, but as our usage scaled 10x, data loss and throttling became common. We replaced it with a distributed store designed for high throughput and persistence.
• Bypass when the effort-to-impact ratio is too high, fix only what’s necessary, and document the rest. One team I worked with had a legacy admin portal with hardcoded permissions logic. Rewriting it would have taken months, but it was rarely used. We documented its quirks, added a banner to warn users of limitations, and created a wrapper for the one feature it still supported.

The lesson: don’t assume all tech debt deserves your best engineering. Sometimes, clarity and containment are more valuable than cleanup.

Accountability is a team sport

The ownership of the debt cannot rest with tech leads alone. Teams require incentives and rituals, such as quarterly debt review, shared dashboards, and making ownership based on service health scores. An organization I consulted with tied debt scores directly to performance reviews at the top management level, not as a stick, but as an indication that quality was not a choice. Within two quarters, they saw a 25% increase in resolved debt items and a measurable drop in incident frequency across critical systems, indicating that visibility and ownership alone can drive behavior change.

Communicating technical debt to stakeholders

Explaining debt is a challenge for engineers because we default to using tech jargon. Instead, anchor your explanation to what leadership is already monitoring: time-to-market, uptime, and customer retention. One of the teams I was working with demonstrated that their flaky integration suite was the cause of 20% of deployment delays over a quarter. They didn’t want a second chance to rewrite tests; they wanted time to address underlying causes and minimize lead time.

Such measurements as MTTR, frequency of incidents, and success of deployment are more compelling than the statement that the “code is messy.” Talk in a language that is appreciated by your audience.

Graphs and dashboards go a long way in supporting your message and making abstract problems tangible. A simple burn-down chart of known debt items versus resolved ones, or an “incident proximity” heat map that highlights which systems are frequently tied to on-call pages, can be powerful. I’ve seen leadership teams green light refactor budgets after one well-made chart.

Just avoid vanity metrics. If the graph doesn’t influence a decision, it’s not worth presenting.

How not to induce panic

One of the most effective strategies I’ve used is framing debt as a risk mitigation effort, rather than a crisis. No executive wants to hear, “our system might collapse.” But they will listen to, “We’re seeing signals that our current architecture could slow feature velocity by Q4. Here’s our mitigation plan.”

This is where you can draw from external examples. The Equifax breach wasn’t just a cybersecurity issue; it was the result of brittle processes, slow patch cycles, and poor observability. The takeaway for your stakeholders? Ignored debt creates exposure. Proactive management is cheaper than damage control.

Final thoughts 

Debt is inevitable; neglect is optional. The best engineering managers that I have encountered do not view technical debt as a headache. They use it as a signal of operation, like a mirror where the system, team, or culture is heading. Make visibility part of your architecture. Rank and sort debt by impact. Allocate time for predictability. Talk the language of results. And most importantly, make quality conversation a norm.

When we do this correctly, technical debt maintenance will not be a special cleanup, but will be a habit, a way of thinking, and a strategic advantage.

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