Technical Debt Is Killing Your Business – Here’s How to Fight Back
Technical debt explained: From hidden costs to strategic choices, uncover how it impacts your organization and the proven methods to address it effectively.
Welcome back to Tech Trendsetters, where we not only explore the intersection of technology, business, and society, but also try to bridge the gap between complex technical concepts and business leadership.
Today, we're tackling a challenge that's probably costing your organization millions, even though you might not see it on your balance sheets. What do a successfully planned city infrastructure and your IT systems have in common?Both can either thrive or collapse under the weight of mismanaged growth.
And let me be honest here: most business leaders tune out when their technical teams mention terms like "technical debt" or "legacy systems." They treat these issues as purely technical problems that can be solved by throwing more developers at them or postponed indefinitely. This mindset is exactly why companies end up with systems that cost fortunes to maintain and prevent any meaningful innovation.
Ward Cunningham, who coined the term "technical debt," compared it to financial debt:
“Shipping first time code is like going into debt. A little debt speeds development so long as it is paid back promptly with a rewrite... The danger occurs when the debt is not repaid. Every minute spent on not-quite-right code counts as interest on that debt. Entire engineering organizations can be brought to a stand-still under the debt load of an unconsolidated implementation.”
But unlike financial debt, technical debt doesn't show up on quarterly reports, making it easy for executives to ignore until systems start breaking down spectacularly.
The truth is, your organization is probably sitting on a mountain of technical debt right now. Those quick fixes from five years ago? They're still there, quietly multiplying like bacteria in a forgotten takeout container. That "temporary" solution your team implemented during the pandemic? It's now a critical system that nobody dares to touch. And that legacy system everyone's afraid to replace? It's not just slowing you down – it's actively preventing you from adopting AI and other innovative technologies that could give you a competitive edge.
In today's episode, I'll show you how these invisible technical decisions from years ago are sabotaging your digital advancements today. More importantly, we'll explore why this isn't just an IT problem – it's a business risk that needs to be understood and managed at the executive level. But let’s start from basics.
What is The Technical Debt
Let's cut through the technical jargon and get real about what technical debt actually means for your business.
Starbase – City of a Dream
Imagine you’re Elon Musk, building your own Starbase – a small town dedicated to designing, testing, and launching next-generation spacecraft. In the beginning, everything is carefully laid out:
Roads to transport heavy rocket parts;
Power grids capable of handling advanced manufacturing;
Residential areas for engineers who need to live on-site;
Launch facilities with cutting-edge safety and monitoring systems.
The Starbase is small but hyper-focused, so everything initially runs smoothly. Local infrastructure meets current demands, and the teams have enough runway (pun intended) to work on new rocket designs without too much friction.
The Rush to Scale
But suddenly, demand skyrockets for more launches. NASA, private clients, and other partners want frequent test flights. There’s pressure to expand rapidly:
You add more launch pads without fully integrating them into the existing control systems;
You throw up temporary housing for the influx of new engineers;
You hack together a makeshift system to monitor rocket telemetry, “just for now,” promising to refine it later;
It works, but only just. Over time, you’re left with a patchwork of code, hardware, solutions and processes. Each new problem you fix “for the moment” creates a technical debt that Starbase will eventually have to repay – through refactoring, upgrading, or overhauling entire systems.
Just like a city that outgrows its initial plan, Starbase starts to feel the strain.
Technical debt
Kent Beck, one of the pioneers of software engineering, puts it bluntly:
“The first step in paying down technical debt is acknowledging it exists.”
But here's the problem – most organizations don't just fail to acknowledge it; they actively create it while pretending everything's fine (true story).
Back to our Satrbase analogy. In your rush to accommodate new residents, you hastily extend roads on top of old ones, patching together power lines without a clear construction plan, and zoned new neighborhoods with temporary rules. You figure, "we'll fix it later." Sound familiar? That's exactly how technical debt creeps into your systems.
What I have to say: it definitely works... until it doesn't.
Just like that new shiny city, your organization is probably dealing with:
Systems that require bizarre workarounds that only "Dave from IT" understands;
Critical processes that run on outdated technology because "that's how we've always done it";
Temporary fixes from 2020 that have somehow become permanent;
Integration nightmares that make simple changes take months instead of days;
In 2018 Stripe collected some statistics on the same exact problem: developers spend up to 42% of their time dealing with technical debt, resulting in global losses exceeding $85 billion annually. That's not just inefficiency – it's a massive waste of your most expensive resource: engineering talent. Instead of building new features or improving your products, your team is essentially running around putting out fires in a city infrastructure that should have been upgraded years ago.
To finalize it, technical debt is like slapping temporary patches on critical hardware – quick, but costly. It’s the cost of taking shortcuts that lead to long-term inefficiencies.
When Technical Debt Is Actually Strategic (Yes, Sometimes It Is)
Now, here's where it gets interesting – and where I might surprise you. Technical debt isn't always bad. Sometimes, it's actually a smart strategic choice. Twitter, for instance, built their initial platform on a monolithic architecture – a decision that helped them launch quickly and prove their concept. Once they had millions of users, they gradually rebuilt their system into microservices.
Let me echo Reid Hoffman's (Co-Founder, LinkedIn & Inflection AI) famous insight:
“If you are not embarrassed by the first version of your product, you’ve launched too late”
This perfectly captures the value of moving fast, even if it means accepting technical compromises early on to capture market opportunities. In Twitter’s case, it was like zoning a new suburb quickly to test if people would move there – a short-term plan to prove the concept before investing in a full-scale infrastructure. The key difference is that Twitter knew (or, at least they realised it later on) they were taking on technical debt and eventually had a strategy to pay it back.
Planned vs. Unplanned Debt
Indeed, taking on a little technical debt is actually a strategic choice. This is what we call planned debt: we accept temporary shortcuts, but we document them carefully and budget time (or resources) to refactor or rewrite later. By contrast, unplanned debt is when those shortcuts accumulate without any awareness, documentation, or timeline for fixing them.
The difference is enormous. And here's where most businesses get it wrong. They:
Take on technical debt unconsciously or through ignorance;
Never document what corners they cut;
Have no plan (and/or budget) for fixing it later;
Pretend everything's fine until systems start failing catastrophically or the business completely loses its market niche;
In software engineering, Ward Cunningham himself warned:
“A little debt speeds development so long as it is paid back promptly with a rewrite”
That last part is key: a rewrite or refactoring must always be part of the plan. Without it, your organization becomes increasingly resistant to change. Much like trying to rebuild roads while the entire city is still using them 24/7. You can’t pause operations, but ignoring the problem only makes it worse.
Let’s add some perspective from Jez Humble , one of the minds behind DevOps:
“If the cost of repayment is low, taking on technical debt becomes a reversible business decision instead of an irreversible technical liability”
As you can see, smart people don't treat technical debt as something scary or nonexistent. Instead, they make it part of their daily conversation and strategic planning. They know it exists, they track it, and most importantly – they budget for it.
Successful architects don't just plan for new buildings; they also budget for road maintenance, utility updates, and occasional big overhauls. It's just part of doing business. The same goes for software development.
When Quality Actually Saves Money (But Nobody Believes It)
Here's where things get ironic – and where Martin Fowler's insights become particularly relevant. Despite what most business leaders assume, high-quality software is actually cheaper to produce. Yes, you read that right.
The same way a well-designed city with solid infrastructure ultimately allows more efficient transit and services, clean and well-structured code lets teams move faster and deliver more value. Simple! I think it's so simple, that it actually becomes hard.
Martin Fowler puts it perfectly when he explains that:
“The ‘cost’ of high internal quality software is negative.”
or
“The point isn't to avoid debt, but to be deliberate about it.“
This flies in the face of conventional wisdom, where quality and cost usually trade off against each other. When you buy, let’s say a construction material, better quality usually means higher prices. But software doesn't work that way.
Let me break down why this counter-intuitive trend gets ignored:
Short-term thinking dominates business decisions;
The cost of poor quality isn't visible until it's too late;
Most executives have never experienced the difference between working with high-quality and low-quality codebases;
The pressure to "ship fast" creates an illusion that cutting corners saves money;
And let me share my favorite explanation of these principles – a simple graph that perfectly captures what happens in almost every software project I've seen. The brown line shows the typical "move fast and break rules" approach. The blue line – what happens when you take time to do things right. You see, there's that initial dip, an investment in quality – like carefully laying out roads and utilities for future expansion. And yes, some managers at this point start getting twitchy, asking why everything's taking "so long".
But look where these lines cross – and this is the part that blows my mind every time – it happens in weeks, not months or years. WEEKS. After that point, the high-quality approach doesn't just win; it completely changes the game in terms of how fast you can deliver new features. But if you're stuck with the brown line, after that point, you actually start to pay the “interest” on this debt we talked about before.
And the most frustrating part – the longer you go, the wider the gap between what’s possible and what’s achievable.
The Legacy
I hope these city analogies have resonated with you. I chose Starbase, the real hub for SpaceX operations, as a real-life example because it perfectly illustrates the challenges of managing rapid growth alongside legacy infrastructure. Imagine inheriting not just one facility but an entire network of launch sites, each with its own unique layouts, operational quirks, and infrastructure built to meet the urgent demands of its time.
This is what legacy systems feel like in the tech world – a patchwork of components that, while functional, create challenges for scalability, modernization, and future growth.
But what exactly is a legacy system?
The term often conjures images of outdated technology, but it’s more nuanced than that. Gartner glossary defines a legacy system as:
“An information system that may be based on outdated technologies, but is critical to day-to-day operations”
Similarly, Martin Fowler, one of the most respected voices in software development, simplifies it further:
"Legacy code is simply code without tests."
While both definitions highlight key aspects, from my point of view legacy systems are not just about age, outdated technology or tests or anything like that. They’re about complexity. Their architecture reflects the challenges of the past, not the needs of the future. It's critical systems that keep your business running but are increasingly difficult to maintain, understand, or modify.
To be fair, it must be said, that legacy isn't always a bad thing, but sometimes (or even often) it's a result of technical debt that the previous project team decided not to pay off.
You may not love Elon Musk, but a leader who bridges the worlds of software, engineering, and business strategy, he is uniquely equipped to recognize how these hidden complexities can become barriers to innovation. While most people just see rockets launching into the sky, think of the underlying systems – much like technical debt and legacy systems in software.
The Real Cost of "Good Enough"
The numbers tell a shocking story.
According to recent Forrester research, almost 60% of CTOs admit their legacy technology stack is not just costly but fundamentally inadequate for modern applications.
Atera's research reveals that up to 80% of companies' IT budgets is spent merely keeping old systems afloat.
Think about that for a moment – four-fifths of your technology budget isn't driving innovation or creating competitive advantage; it's just maintaining the status quo.
Perhaps most telling is this: 40% of IT leaders regret their legacy technology purchases. That's a really big amount of buyer's remorse, representing billions in investments that organizations wish they could take back.
Why We Can't Just "Replace Everything"
Gene Kim, author of "The Phoenix Project," nails it when he says:
Unplanned work is the silent killer of IT organizations.
Legacy systems create a constant stream of unplanned work – unexpected failures, security patches, compatibility issues – that prevent teams from focusing on innovation and improvement.
Let's break down why legacy systems are so hard to replace:
Deep Integration
Years of custom integrations and dependencies;
Changing one component affects dozens of connected systems;
The risk of disruption keeps growing larger;
Critical business processes built around system limitations;
Knowledge Gaps
Original developers have left the company;
Documentation is outdated or non-existent;
Tribal knowledge becomes the only source of truth;
New team members require months to understand system complexities;
Business Continuity
24/7 operation requirements make replacement risky;
Each change risks breaking critical functionality;
The cost of failure increases with system age;
Regulatory compliance adds additional complexity;
The Modern Legacy
Here's where things get even more interesting. Andrew Ng (a pioneer in machine learning and online education) has a profound observation:
AI is the new electricity, and like electricity, it needs robust infrastructure to deliver its full potential.
This creates a painful paradox for many organizations: they need to modernize to remain competitive, but their legacy systems make modernization extremely risky.
Let me share a story that might sound familiar. Recently, I had the pleasure of consulting for a major retail chain that prided itself on its digital transformation initiatives. They had fancy mobile apps, sleek websites, and ambitious AI plans. But, as always, there was a little problem – their core inventory system was older than most of their developers. Every new feature required bizarre workarounds, which essentially prevented any new initiatives. If we had to sum up all these modern challenges, I would choose these:
Legacy systems often use outdated architectures that don't translate well to cloud environments;
Old systems weren't designed with modern security threats in mind;
Legacy databases often can't handle the volume and velocity of modern data requirements;
Modern APIs often can't interface directly with legacy systems;
Finding developers who can maintain older technologies becomes increasingly difficult and expensive;
The Four Paths Forward
Now, let's talk solutions. Based on my experience and the collective experience I've observed across hundreds of organizations, there are really only four ways to handle legacy systems:
The Ostrich Approach (Ignore It)
Sometimes, rarely, this actually makes sense. If your business model is stable and your systems work reliably, why fix what isn't broken? But let me be clear: this is like NASA relying on its aging space program for decades. While it worked for a time, the lack of modernization eventually left NASA reliant on costly, outdated systems – creating an opportunity for SpaceX to leap ahead with more innovative, efficient, and adaptable solutions.The Clean Slate (Complete Replacement)
This is the digital equivalent of demolishing of everything and starting fresh. It's expensive, risky, and sometimes absolutely unnecessary. I know one mid-sized company, took this approach after calculating that their legacy systems were costing them three new market opportunities every year.The Gradual Evolution (Component-Wise Replacement)
One step at a time approach. Think of this as renovating your rocket facilities one by one, and continuously integrating cutting-edge technologies to combine them into one unified network. It's less risky than a complete replacement but requires incredible discipline and clear architecture vision. As a proper example, Amazon famously used this approach to transform from a monolithic application to a microservices architecture.The Business-First Evolution (Process Reengineering)
This is perhaps the most radical, practical, and effective path forward – but don't be fooled by its apparent simplicity. You start by reengineering your business processes first, then build or modify software to support these improved processes. It's labor-intensive and, most critically, requires conscious, active support and awareness from management throughout the entire journey.
Also, a couple of words from my personal experience. After years of witnessing both spectacular failures and remarkable successes in legacy system modernization, I'm convinced that the fourth approach is the only one that consistently delivers lasting results. Here's why.
Your business isn't just a collection of software systems. It's not. Your business is your processes, your people, and your purpose. The technology should serve these elements, not dictate them.
What makes the fourth approach so powerful? It's comprehensive. Like a waterfall, it flows from top to bottom, naturally encompassing all 3 other solutions within its scope. When you start with business processes, you might discover that some systems can be ignored (approach 1), others need complete replacement (approach 2), and some can be gradually evolved (approach 3). But these decisions emerge from business needs, not technical preferences.
It’s really tempting to start modernizing your technical stack while the fundamental business processes remain stuck in the past. It just doesn't make sense.
Any technical modernization that doesn't start with business process analysis is just expensive window dressing. Period. It forces you to answer the most important question first: "What are we actually trying to achieve?" Only then do you figure out how technology can help you get there.
Thanks for joining me today on this journey through technical debt and legacy systems. I hope my simplistic explanation helped to demystify these complex engineering concepts – and if you've read this far, you're already ahead of most executives in understanding these critical challenges.
Every technical decision you make today is shaping your organization's future agility. Whether you're building the next Starbase or managing a traditional enterprise, the principles remain the same: acknowledge the problem, plan for it, and never lose sight of the business processes that technology should serve
I wish you the best strategic enhancements – may your technical debt be intentional and your legacy systems evolve gracefully! See you next time!
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