Technical Debt is Eating 21–40% of Your IT Budget: Here’s How to Find Your Number

Hem Kant · 30 Jun, 2026

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Most engineering teams already know the problem. Deployments that should take hours take days. A simple feature request touches five systems that were never designed to talk to each other. New engineers spend their first two months not building, just learning what not to touch.

What most teams do not have is a number. Not a vague sense that maintenance work is expensive, but an actual figure they can bring into a budget conversation and say: this is what our technical debt is costing us per year, and here is the calculation behind it.

That gap, between knowing the problem exists and being able to quantify it, is exactly what keeps remediation perpetually deprioritized. 

You can not compete for budget against a $400K infrastructure proposal with “we should really clean this up.”

This article walks through the benchmarks, the calculation methods, and the framework for turning a qualitative problem into a number your CFO will take seriously.

1. How Bad Is It, Industry-Wide?

The numbers are not small. 

Deloitte’s 2026 Global Technology Leadership Study puts technical debt at 21% to 40% of total IT spending. 

That is not the cost of a bad quarter or an underfunded sprint; that is a structural drain built into most organizations’ operating budgets, whether they recognize it or not.

Protiviti’s global research puts the average at 30% of IT budgets spent managing technical debt, with organizations in transportation and logistics reaching 39% and UK firms hitting 38%. 

The US figure is lower in headline surveys, but that is partly because American firms are better at misclassifying debt as normal operating costs rather than actually carrying less of it.

There is also a talent dimension that rarely makes it into budget discussions. 

Stripe’s Developer Coefficient study found developers spend an average of 33% of their working week on technical debt, not features, not architecture, not learning. 

Workarounds, maintenance, bug cycles, and navigating systems nobody fully understands. For a 10-person team at $120K average salary, that is $396,000 per year in hidden overhead before a single line of new code is written.

2. What “Technical Debt” Actually Covers

The term gets used loosely. Before you calculate your number, it helps to understand what you are actually measuring. 

There are four distinct categories, and they carry very different remediation costs.

1. Code-level debt

The most visible kind: duplicated logic, undocumented functions, missing test coverage, outdated dependencies. 

Static analysis tools like SonarQube surface this automatically. It is real and meaningful, but it is also the smallest portion of the overall problem.

2. Architectural debt

This is where most of the cost lives. Circular dependencies, monolithic systems that were never decomposed, integrations held together with custom middleware written by engineers who left three years ago.

3. Data debt

Particularly relevant for any organization trying to adopt AI. Incomplete data models, inconsistent schema across systems, and pipelines that were built once and never properly maintained. 

If your data infrastructure is fragile, your AI initiatives will be too. We explored this relationship in detail in our piece on the AI technical debt crisis and how to fix it.

3. Knowledge debt

Systems that only one or two people understand. Documentation that is years out of date. Tribal knowledge that leaves when engineers do. 

This is the hardest kind to quantify and often the most expensive to remediate, because you are paying for archaeology before you can do any engineering.

3. Three Ways to Calculate Your Number

There is no single right method. 

The best approach depends on your team’s size, the tooling you already have, and whether you need an order-of-magnitude estimate or something precise enough to defend in a board meeting. 

Here are three methods, ordered from fastest to most rigorous.

Method 1: The Stripe Salary Model (30 minutes)

This gives you a defensible floor estimate using publicly available research. It is not precise, but it is fast and credible enough to start a conversation.

Method 2: Technical Debt Ratio (1–2 days with tooling)

TDR is the standard metric for quantifying debt as a proportion of your codebase’s development cost. 

It answers a different question than the salary model: not what you are spending on maintenance, but how far gone your codebase actually is.

Method 3: Maintenance vs. Feature Ratio (ongoing tracking)

The most operationally grounded method. 

Pull your sprint data from Jira or GitHub and categorize tickets by type: net-new features vs. bug fixes, rework, workarounds, and maintenance. 

The ratio of maintenance to total engineering time is your debt interest rate, and it is harder to argue with than any estimate because it comes directly from your own records.

4. What Technical Debt Actually Blocks

The cost of technical debt is not just maintenance time. It compounds in ways that are harder to put in a spreadsheet but matter just as much to the business.

It delays every roadmap item

McKinsey’s research found that 30% of CIOs report that more than 20% of their budget for new products gets redirected to debt-related issues before a single line of new code is written. That is not a rounding error. That is one-fifth of your innovation budget silently consumed by the past.

It poisons AI adoption

This is the dynamic most organizations are hitting right now. 

AI integrations need clean APIs, reliable data pipelines, and maintainable codebases to run properly. 

High architectural debt means you cannot build on top of what you have. You are not ready to adopt AI. You are waiting for a foundation that was never finished. 

Our analysis of why AI makes platforms more expensive to maintain covers exactly this compounding effect.

It drives out senior engineers

Developer experience research consistently shows that technical debt is one of the top reasons senior engineers leave. They do not mind complex problems. They mind systems so entangled that solving one problem guarantees breaking three others. 

When a senior engineer leaves, you lose not just salary but the system knowledge that makes the codebase navigable for everyone else. 

Replacement cost runs 50–70% of annual salary when you account for recruiting, onboarding, and the ramp period before a new hire becomes productive.

5. Where to Start

Most remediation efforts fail not because the engineering work is wrong, but because they start in the wrong place. 

Refactoring the module that your team finds annoying is not a strategy. Prioritizing by business impact is.

1. Inventory your systems

List every application, integration point, and data pipeline. For each, note its age, its owner, and whether it has documentation that a new engineer could actually follow. 

The systems with no clear owner and no documentation are your highest-risk items, not necessarily your messiest code.

2. Measure maintenance hours

Pull the last two quarters of sprint data and categorize by type. If you do not have that granularity, do a two-week self-reporting exercise. The number will be higher than your team expects.

3. Run TDR on your highest-traffic codebases

You do not need to audit everything at once. Start with the systems that touch the most users, generate the most revenue, or are on the critical path for AI initiatives. SonarQube or similar tooling gives you a baseline within days.

4. Score by impact, not severity

A 40% TDR in a module your team barely touches is less urgent than a 15% TDR in the API layer every downstream service depends on. Prioritize by business impact, change frequency, and failure consequence, not just technical severity scores.

5. Build the business case with your number

Take the three figures into a conversation with finance or leadership. The goal is not a one-time cleanup budget; it is a standing allocation, roughly 15% of your IT spend, treated as a recurring investment in delivery capacity.

Closing Thoughts: The Number Is the Beginning

Technical debt is not a technical problem. It is a visibility problem. Engineering teams live with its effects every day; finance and leadership teams do not see it on any report. The gap between those two realities is what keeps the conversation stuck in the abstract.

Getting your number, even a rough one, changes the conversation. It moves remediation from a vague aspiration to a line item with a return attached. It gives engineering a credible voice in budget discussions. 

And it creates accountability: once you have a baseline, you can track whether the debt is shrinking or growing, and whether the investments you are making are actually working.

If you are ready to go from intuition to evidence on your technical debt exposure, let’s talk through what it looks like for your platform. 

The conversation takes about 30 minutes. The visibility it produces is worth considerably more than that.

Author

Hem Kant

Content Strategy and Integrity Lead (Social+ Services)

Curious by nature, Hem Kant is a strategist and writer who grounds his work in quiet reflection. He draws inspiration from the stillness of winter, clean cityscapes, good books, and honest talk (Networking). He writes with a commitment to integrity and a sharp focus on essential detail, delivering work defined by substance and insight.