Key Takeaways
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In 2025, technical debt isn’t just a software issue. It’s a business risk. For manufacturing firms still relying on legacy applications and aging infrastructure, technical debt accumulates silently in the background: in every hard-coded rule, outdated integration, or unsupported platform dependency. And it’s costing you more than just IT overhead.
According to McKinsey, CIOs estimate that 10% to 20% of their technical budget dedicated to new products is diverted to resolving issues related to tech debt. Worse, 60% say technical debt has increased materially over the past three years, especially as organizations race to modernize for AI, analytics, and automation demands.
For application owners in manufacturing, this creates a dangerous bottleneck. Aging ERP or MES systems running on IBM i, on-prem databases, or custom-coded logic are increasingly brittle…and expensive. They limit your ability to scale, integrate, and secure operations in a globally distributed, supply chain–intensive environment.
And here’s the kicker: sources predict that by 2027, 75% of organizations will face systemic failures due to unmanaged technical debt.
If you’re still running legacy workloads without a strategy for modernization, you’re not just falling behind; you’re risking system instability, compliance gaps, and millions in avoidable costs.
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Managing technical debt starts with recognizing its origin and symptoms, especially in legacy environments common in manufacturing.
According to Gartner’s Reduce and Manage Technical Debt (May 2025), around 40% of infrastructure systems across asset classes already carry significant technical debt burden. This burden often accumulates through compromises in architecture, shortcuts in integrations, or deferred updates.
Another Gartner-backed insight, highlighted in a report on technical debt measurement, is that poorly designed software architectures are among the primary root causes of long-term technical debt, producing ripple effects that degrade quality, increase defect rates, and slow feature delivery.
And this isn’t just a niche issue: in a survey shared via Gartner Peer Community, 93% of development teams reported currently experiencing technical debt, with architecture debt being the most frequently cited form Gartner. This suggests that technical debt is pervasive, even in modern development environments.
So what should application owners look for when identifying technical debt in their systems?
These signs are especially risky in manufacturing systems, where data flows, ERP logic, and operational consistency are mission-critical.
Letting technical debt pile up is no longer just a development concern. It’s a C-suite issue, especially in industries like manufacturing where uptime, consistency, and compliance are non-negotiable.
According to Gartner, organizations that ignore technical debt spend up to 40% more on maintenance than peers who address it early. This means more money spent patching, rewriting, and troubleshooting, rather than innovating or accelerating digital transformation.
In manufacturing, the ripple effects of unresolved debt are even more disruptive:
A report by McKinsey notes that companies with fragmented or legacy systems were 30% more likely to experience AI implementation delays due to the inability to integrate with modern data platforms. In other words, technical debt is now a barrier to AI adoption, not just a nuisance in IT.
And there’s an opportunity cost too. According to IDC, enterprise organizations that proactively reduce technical debt realize a 20–30% faster time to market on new digital initiatives.
With mounting pressure to modernize and adopt cloud-native architectures, failing to manage technical debt is like driving with the parking brake on. For application owners, the message is clear: what you tolerate today may determine your system’s agility, or fragility, tomorrow.
Technical debt isn’t just a software engineering nuisance. It’s a serious financial liability. Yet despite its impact, most organizations still manage it in a loosely defined, reactive way.
According to IDC, only 24% of organizations report having a confident, consistent way of tracking and managing technical debt across projects and business units. The rest? They’re relying on spreadsheets, gut feeling, or ad hoc team assessments. That kind of uncertainty can quickly snowball into spiraling remediation costs, slower delivery timelines, and stunted innovation.
The first step to managing technical debt is recognizing just how much it’s costing you. Research estimates that a codebase with around 1 million lines of code incurs approximately $306,000 per year in technical debt remediation costs. And that’s without factoring in downtime, missed market opportunities, or compliance risk.
Enterprise architects need a portfolio-wide view: dashboards, KPIs, and health metrics that show where technical debt is concentrated, which workloads are reaching critical thresholds, and which areas offer the most strategic upside if modernized.
Managing technical debt must become part of your operating model, not an afterthought. This means setting architectural standards, using automated code analysis, and incorporating debt tracking into sprints and QA. For many organizations, shifting to CI/CD pipelines and integrating observability into DevSecOps practices helps surface “silent” debt before it compounds.
IDC’s 2025 research shows that 47% of IT leaders cite technical debt as a major contributor to overspending on cloud and digital infrastructure. Tackling it proactively isn’t just best practice. It’s budget protection.
Not all legacy systems are equally harmful. Some may simply need containerization or decoupling, while others are so entangled with outdated dependencies that they hold back everything else. Focus first on high-debt, high-value systems; those that either directly affect customer experience or are at odds with your security and scalability goals.
By combining architectural assessments with technical debt scoring and business impact mapping, you can make informed modernization decisions that align with both your budget and your roadmap.
Managing technical debt becomes exponentially more challenging when it’s embedded in the very structure of legacy systems. Architectural debt – such as rigid, monolithic structures – not only constrains modernization but also inflates risk and cost with each new deployment or integration attempt.
According to McKinsey, “technical debt may represent up to 40% of the technology estate in large enterprises,” and in many cases, it accumulates silently across years of patch fixes, outdated patterns, and brittle dependencies.
Architectural debt is often compounded by tooling debt – a lag in keeping development pipelines, testing frameworks, and monitoring solutions up to modern standards. Gartner notes that I&O leaders that prioritize technical debt reduction report improved service delivery speeds and better stakeholder satisfaction.
Beyond technology, cultural inertia creates the third wall of resistance. Teams grow accustomed to working around limitations rather than addressing them. Knowledge silos around legacy codebases deepen over time. A longitudinal study found that architectural debt persisted even through multiple release cycles, leading to code that’s harder to test, understand, and maintain.
These factors create an interlocked challenge: an inflexible foundation (architecture), outdated mechanisms (tooling), and resistant mindsets (culture). Addressing one without the others often leads to partial wins. That’s why organizations modernizing on platforms like OCI are emphasizing holistic debt audits, combining business-driven assessments (e.g. VFunction’s modernization scorecards) with technical remediation roadmaps.
The payoff is measurable. In McKinsey’s research on AI-enabled IT modernization, enterprises that tackled debt in tandem with migration efforts reported 30–50% reductions in operational overhead and significantly faster development cycles.
In short, treating architectural, tooling, and cultural debt as a unified modernization priority, rather than isolated issues, gives organizations a faster path to resilient, AI-ready digital platforms.
Managing technical debt isn’t a one-and-done effort. It’s an ongoing discipline. As enterprises grow and adopt new technologies, the risk of accumulating additional debt remains constant. That’s why CIOs, CTOs, and enterprise architects are increasingly embracing continuous modernization as a sustainable and strategic approach.
Instead of waiting for the perfect time to execute massive IT overhauls, modern organizations are continuously upgrading systems, refactoring code, and aligning architecture with business goals. This “low-and-slow” approach avoids disruptive downtime and allows for tactical reengineering that delivers faster ROI.
DevOps is a strategy to reduce technical debt over time. Automated testing, integration, and delivery pipelines help detect architectural regressions, security vulnerabilities, and integration issues early in the cycle. Combined with proper observability tooling, DevOps teams can flag “hidden” debt, like outdated libraries or monolithic modules, that silently accumulate over time.
One of the most impactful ways to manage legacy technical debt is through cloud-native architectural patterns: microservices, containers, and serverless. These allow organizations to modularize functionality, isolate problematic components, and gradually rebuild rather than rip-and-replace.
Modern hyperscale platforms like Oracle Cloud Infrastructure (OCI) support advanced developer services like Oracle Functions, API Gateway, and DevOps service pipelines, empowering organizations to incrementally refactor applications while still maintaining operational continuity.
