Technical Debt Is Business Debt: Why Modernisation Can’t Wait

As enterprises in Singapore race toward digital transformation, it’s clear that technical debt has become a major obstacle to growth, compliance, and competitiveness. What begins as deferred updates and workarounds eventually manifests in slower decision-making, escalating cyber and regulatory risks, rising costs, and a diminished capacity to innovate.

The scale of the challenge is growing rapidly. Forrester predicts that by 2026, 75% of decision-makers will face technical debt at moderate or high levels of severity — up from just over 50% in 2025.

As regulatory expectations tighten and AI continues to reshape work, these numbers prove that organisations can no longer afford to delay modernisation or treat it as a problem for IT teams to address in isolation.

The True Cost of Technical Debt

Like AI drift, technical debt often occurs and accumulates quietly. Organisations retain legacy platforms because change feels disruptive and manual processes “still work.” Yet these compromises gradually restrict both the effectiveness and efficiency of daily operations.

The real cost of technical debt extends well beyond technology teams:

• Slower innovation and time‑to‑market. Legacy systems and brittle integrations delay the rollout of new capabilities, limiting the organisation’s ability to respond to market change.
• Increased downtime and operational inefficiencies. Manual workarounds raise the likelihood of outages and errors, diverting resources from higher‑value initiatives.
• Higher cybersecurity exposure. Fragmented and outdated environments create more attack surfaces, increasing both the likelihood and impact of security incidents.
• Poor employee and customer experience. Slow systems, inconsistent data, and inefficient processes frustrate users and reduce productivity, trust, and satisfaction.

Beyond efficiency, the disruption stemming from technical debt is a compounding liability rather than a static issue. Eventually, it extends to compliance and security: precisely where legacy environments create visibility gaps and control weaknesses.

The Risk Multiplier: Regulatory Compliance and Security Exposure

Technical debt from ageing platforms and poorly managed data environments does more than slow down operations; it also multiplies risk across compliance and security. As organisations in Singapore’s regulated sectors increasingly adopt AI, these legacy systems can create blind spots, slow response times, and increase the likelihood of regulatory breaches.

PDPA Obligations and Breach Notification

Data sprawl and legacy systems make it more challenging to fulfil Personal Data Protection Act (PDPA) obligations. Organisations may struggle to meet obligations such as timely notification to the Personal Data Protection Commission (PDPC) and affected individuals during a notifiable breach, increasing both operational risk and potential penalties.

MAS TRM and FSM-N05 Expectations

For financial institutions, the Technology Risk Management (TRM) guidelines and the legally binding Notice FSM-N05 Technology Risk Management issued by the Monetary Authority of Singapore (MAS) set clear thresholds that legacy environments often struggle to meet — such as clearly identifying critical systems, maintaining service availability during disruptions, and notifying MAS promptly when major incidents occur. Defence-in-depth and board-level oversight further raise the bar, turning technical gaps into regulatory risks and remediation costs.

Cybersecurity Act and CCoP for Critical Information Infrastructure

In critical sectors such as energy, healthcare, and water, fragmented digital environments undermine recurring requirements — such as risk assessments, vulnerability assessments, penetration testing, and red team exercises as mandated under the Cybersecurity Code of Practice for Critical Information Infrastructure (CCoP) issued by the Cyber Security Agency (CSA). Left unchecked, these environments raise the likelihood and attack surface of incidents.

Public Sector and Vendors to the Singapore Government

Projects integrated with Singapore government systems must meet risk-based security controls and undergo pre-go-live compliance testing to ensure systems are designed, operated, and secured effectively. Public‑sector organisations and government vendors that rely on legacy architectures often find that those same technologies fall short during evidence‑based audits.

What Modernisation Means for AI Adoption

AI can’t thrive on fragmented, low-quality data or legacy platforms. To realise AI value safely, at scale, and in alignment with local AI governance efforts, modernisation must come first. This means focusing on:

• Clean, governed, accessible data. Consolidated, well‑classified data with clear lineage and retention policies improves AI accuracy, reduces hallucinations, and supports compliant use.
• Secure collaboration environments. Modern identity, access, and information protection controls (e.g., sensitivity labels, data loss prevention, eDiscovery) enable AI to operate within defined guardrails.
• Confident AI without compliance blind spots. Centralised governance and auditability ensure AI outputs can be trusted, traced, and defended — critical for regulated industries.
• Scalable foundations. Standardised platforms, integrated data, and automation reduce operational drag, making it faster to deploy AI use cases across functions.

Modernisation isn’t just about cost optimisation. It’s the enabler of safe, scalable AI and a prerequisite for unlocking enterprise-grade outcomes.

From Technology Decision to Business Priority

For organisations to execute modernisation effectively, they must avoid treating it as a technical exercise. With technical debt increasingly affecting risk, compliance, and AI adoption success, paying it down must become a business responsibility, not just an IT challenge.

For leaders, the shift is one of perspective. Technical debt should be managed like financial debt: assessed, prioritised, and reduced based on its impact on the organisation’s resilience and future growth. Left unchecked, it compounds — making every regulatory change harder to absorb and every AI initiative riskier to deploy.

This is why modernisation efforts must be driven at the executive level, with clear ownership and alignment across business, risk, and technology teams. When leaders frame modernisation around business outcomes – such as reduced regulatory exposure, improved operational resilience, and faster AI-driven innovation – it becomes easier to secure investment and maintain momentum.

Most importantly, treating modernisation as a business priority ensures that today’s decisions support tomorrow’s capabilities. Organisations that proactively address technical debt are better positioned to adopt AI responsibly and compete in an increasingly digital economy.

Act Now: Turn Modernisation Into Momentum

Technical debt is business debt: It compounds over time, increases the risks of regulatory and security exposure, and slows innovation just when AI is transforming competition. The decision facing many organisations in Singapore is how quickly they can pay this debt and convert it to a sustainable capability.

Modernisation is the key to this conversion. By retiring legacy platforms, consolidating and governing data, and strengthening controls, organisations reduce the future interest on debt – the hidden costs of outages, audit findings, and remediation – while building foundations for trusted, scalable AI.

With regulatory expectations rising and AI innovation accelerating, the cost of delay increases, both in risk and missed opportunity. The AI Accelerator ECI Funding Programme enables decisive movement, offering up to S$105,000 in funding to offset the foundational work that pays down technical debt and prepares digital workplaces for responsible AI.

Explore how AvePoint can help you turn technical debt into an AI-ready foundation under the ECI programme, ensuring your models are accurate, compliant, and future-ready.