In today’s ever-increasing digital world, the speed at which technology is changing and updating is significant. From cloud computing and AI to automation and data-driven decision-making, enterprises are constantly under pressure to innovate and modernize, Yet, many organizations find themselves weighed down by something invisible but critically impactful – technical debt.
Over a quarter (28 per cent) of UK businesses responding to Zoho’s Digital Health Study reported having ‘good’ digital health. Just as financial debt accumulates interest over time, so does technical debt. It refers to the future cost incurred when businesses choose quicker, short-term solutions rather than more sustainable, robust approaches to modernize their technology.
While these decisions may solve immediate problems, they often introduce inefficiencies and complications that escalate over time, and every enterprise, understanding and managing technical debt is not just a technical concern, it’s a strategic imperative.
Managing Director at Zoho UK.
The hidden cost of technical debt
Technical debt can manifest in several ways, from outdated codebases and legacy systems to inefficient workflows and poorly integrated applications. For large enterprises, the scale of technical debt can potentially be massive due to the complexity of systems and the years (or potentially decades) of patchwork development.
Small and medium businesses are not immune to this either and the pressure to scale often quickly leads to shortcuts and accumulate unseen technical liabilities.
But the concept extends beyond code towards architecture, to processes where manual workflows are error-prone and slow down operations, technology stacks that don’t support modern features or security standards, and skills gaps that prevent onboarding and collaboration.
Over time these issues create bottlenecks, increase operational costs and limit the organization’s ability to innovate. What begins as a minor inefficiency can snowball into a major barrier to growth.
The accumulation of technical debt is not always the result of poor planning or bad practices. In many cases, it’s a strategic decision, delivering functionality fast to gain a competitive advantage or to meet time-sensitive business requirements. However, when this debt is not tracked or acknowledged, it becomes a liability.
As systems grow older and more complex, the effort required to modify or integrate them increases, leading to greater maintenance costs and more frequent errors.
Legacy systems, yesterday’s strengths, today’s strain
Legacy systems were once the backbone of many successful operations but are now one of the most common sources of technical debt. Many enterprises rely on systems that were built decades ago and use outdated programs, databases and frameworks.
These infrastructures often lack modularity, making it difficult to isolate functions or update individual components. This rigidity complicates integration with modern technologies such as APIs, cloud platforms, and artificial intelligence tools, often leading to increased costs and complexity.
Their inflexible architectures and siloed data further hinder innovation as seamless data flow is essential for implementing technologies like AI, IoT, and advanced analytics. As IT automation and data-driven strategies become central to business performance, reliance on outdated systems can erode competitiveness and agility.
Beyond this, it can also expose organizations to security vulnerabilities, especially when outdated systems no longer adhere to modern compliance standards.
The case for phased modernization
Addressing technical debt is not about eliminating it entirely. Some level of debt is inevitable in any technology-driven organization, but the goal is to manage it strategically and systematically. Instead, enterprises should look at phased transformation, where legacy components are gradually replaced or integrated with modern tools.
One of the most efficient ways to enable this transformation is AI. By automating repetitive tasks such as data entry and report generation, organizations can reduce inefficiencies and improve accuracy. Automation also serves as a temporary bridge between old and new systems which allows teams to modernize individual components without a full system overhaul.
Data analysis plays a key role in identifying and prioritizing areas of technical debt. With the right analytics tools, businesses can assess the performance, usage, and maintenance costs of various systems and processes.
This enables informed decision-making, where the most problematic or expensive components are addressed first. Over time, this methodical approach helps reduce complexity and improve agility.
Low-code and no-code platforms can also offer significant advantages in managing technical debt by empowering non-developers to build and customize applications which can reduce the reliance on overburdened IT teams.
When governed effectively, these platforms enable rapid deployment of digital solutions without compromising control or compliance. They also support faster iteration and experimentation, making it easier to adapt to changing business requirements.
Importantly, addressing technical debt also involves cultural change. Leadership must recognize that digital modernization is not just an IT initiative but a business priority. Investing in training and fostering cross-functional collaboration is essential to creating an environment where technical debt is actively managed rather than ignored.
By adopting a phased, data-driven approach to modernization, supported by automation, low-code platforms, and modular architectures, businesses can reduce their technical debt while preserving continuity and accelerating digital transformation.
In doing so, they not only improve operational efficiency but also position themselves for long-term success in an increasingly dynamic and digital world.
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