The rising cost of technical debt in IT service management

Date:
Fri, 01 May 2026 08:56:55 +0000

Description:
How technical debt quietly slows ITSM platforms and why governance determines innovation success.

FULL STORY ======================================================================Copy link Facebook X Whatsapp Reddit Pinterest Flipboard Threads Email Share this article 0 Join the conversation Follow us Add us as a preferred source on Google Newsletter Subscribe to our newsletter Over the past decade, IT
service management platforms have emerged as mission-critical tools that
power service delivery for IT organizations and beyond.

Many platforms, along with evolving customer needs, are expanding into other shared service areas such as HR, Finance, CRM , and Facilities. Further extending their enterprise value, some are even becoming foundational
toolsets that support critical security and risk management functions.
Article continues below You may like Bias is eating your AI budget AI governance under strain: what modern platforms mean for data privacy Want to improve ITSM workflows and efficiencies? Here are the top 5 AI features to look for The overall goal is to simplify business operations and create a unified, consistent approach to service delivery. Ron Browning Social Links Navigation

CEO and Co-Founder of Dyna Software. However, when organizations move quickly to meet business demands and expand into broader use cases without proper governance and architectural controls, they can introduce technical debt that makes it more difficult and expensive to innovate on their platform.

This tends to be true across most, if not all, service management platforms, including ServiceNow, Freshworks, Ivanti Neurons for ITSM, and others. Contributing to technical debt There are several factors that contribute to technical debt, and one of the most important things to understand is that
its seeds can be planted long before the platform even goes live. Are you a pro? Subscribe to our newsletter Sign up to the TechRadar Pro newsletter to get all the top news, opinion, features and guidance your business needs to succeed! Contact me with news and offers from other Future brands Receive email from us on behalf of our trusted partners or sponsors By submitting
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Many organizations begin implementation with years worth of assumptions about workflows and system design. In an effort to accelerate time to value, some teams attempt to recreate processes exactly as they exist in legacy systems.

In some cases, this may be appropriate. However, in many cases, it introduces unnecessary complexity and customization. Rather than critically evaluating existing workflows, organizations often reimplement outdated solutions as custom code.

The reasons teams take this approach are less about technical challenges and more about organizational pressure. No one wants to slow down teams that are measured on their ability to deliver business value. What to read next Why enterprise AI ambitions are outpacing legacy modernization AI & cost of
legacy systems in UK banking Best ITSM tool of 2026

Governance decisions about what should be migrated, redesigned, or retired
can be difficult and are often delayed. When platform configuration and development decisions lack proper oversight, teams continue building without
a clear understanding of what should be introduced into the environment.

Technical debt can also be introduced after a platform goes into production. While there may be multiple technical approaches to achieving a goal, not all are sustainable over time.

One of the biggest contributors is choosing to build custom code, system rules, or scripts when the same functionality could be achieved through configuration. The need for ongoing investment Developing custom code may
seem effective initially, as it helps organizations achieve short-term goals. However, it requires ongoing investment to maintain. For example, every
custom script, application, or plugin developed in ServiceNow must be continuously analyzed, tested, and validated with each platform upgrade.

As more custom code is introduced, it can significantly extend upgrade timelines and limit the platforms ability to adopt new capabilities. As a general rule, if an IT service management platform can be configured instead of customized, it should be.

Dependency is another factor that complicates technical debt. Most leading platforms have been refined over many years to support shared components across features and application suites. Modifying core elements of the platform can jeopardize both current and future functionality.

In many cases, even application-specific customizations can create similar risks. The true value of service management platforms lies in their interconnected nature, which enables a unified experience for both end users and the employees responsible for fulfilling requests and resolving issues.

A growing trend in large enterprises is the adoption of distributed or federated development models. In this approach, development resources are decentralized, and different business units are given direct development capabilities.

While this can increase agility, it also significantly raises the risk of technical debt and development conflicts if not governed through a consistent and managed framework.

Over time, technical debt tends to accumulate gradually rather than appearing all at once. For months or even years, systems may appear to function as expected, or issues may seem manageable. Eventually, however, IT management teams responsible for the platform begin to notice that upgrades take longer or that new features are blocked.

As customization increases, both the complexity of upgrades and the effort required to remediate issues grow. This often leads to hesitation in adopting new features, capabilities, or applications, especially when resources are already constrained.

In extreme cases, organizations may be forced to rebuild or re-platform their environments due to the overwhelming complexity caused by technical debt.
This is why technical debt is often compared to financial debt. If you do not address it early, it compounds into a much larger problem over time. Artificial Intelligence Meets Technical Debt Artificial intelligence is the latest development bringing technical debt into sharper focus. AI has the potential to introduce technical debt at scale, but it can also help reduce
it when applied correctly. On one hand, AI can significantly accelerate development and simplify many platform configuration tasks.

Developers can work faster, and processes that once took hours can now be automated. However, if organizations begin deploying AI-generated code or configurations without proper governance or architectural controls, they risk introducing complex dependencies and potential security vulnerabilities.

To prevent this, organizations must implement guardrails. Any form of automated development should be validated against platform best practices,
and AI systems should be trained on these standards before deployment.
Without these controls, AI may create more problems than it solves.

As more teams gain the ability to build their own workflows and business apps , governance and platform visibility become increasingly critical. Organizations must establish clear architectural standards and implement controls that provide appropriate oversight across the environment.

Without a well-defined governance model, teams may continue introducing quick fixes that solve immediate problems while creating long-term challenges.

Focusing on foundational practices remains essential. Code reviews, architectural governance, structured development processes, and adherence to best practices have helped organizations manage technical debt for decades.

Service management platforms provide organizations with the ability to move quickly and deliver business value. However, moving too fast without the
right controls can strain operations and impact long-term performance.

Organizations that succeed understand that technical debt is not something to avoid entirely in the short term. Instead, it must be actively managed over time through strong governance, sound architecture, and disciplined development practices. We've ranked the best software asset management (SAM) tools . This article was produced as part of TechRadar Pro Perspectives , our channel to feature the best and brightest minds in the technology industry today.

The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/pro/perspectives-how-to-submit



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