Indonesia is accelerating the adoption of Artificial Intelligence (AI) across various sectors, from public services to financial services and manufacturing. Amid the optimism surrounding this digital transformation, however, lies a structural risk that receives far less attention: the growing accumulation of technical debt within our software systems.
What is Technical Debt?
In finance, debt is not inherently bad. Companies often borrow capital to accelerate expansion, build new facilities, or acquire advanced technologies. Every debt, however, comes with interest. When managed responsibly, debt can fuel growth. When ignored, it becomes a burden that gradually erodes an organization's health.
The technology industry has a similar concept known as technical debt. The term was first introduced by Ward Cunningham in the early 1990s to describe the consequences of making shortcuts during software development. Writing code quickly to launch a product sooner is a legitimate business decision, provided organizations recognize that the "interest" must eventually be paid through refactoring and system improvements. Problems arise when this debt is never documented, never measured, and never repaid.
From a Technical Issue to an Organizational Risk
Initially, technical debt was viewed as an internal concern for software development teams. Over the past three decades, however, the scale and complexity of digital systems have changed dramatically. Organizations now manage hundreds of interconnected applications running on cloud infrastructure, integrated with partners and customers, while processing enormous volumes of data.
Numerous studies illustrate the magnitude of this challenge. A 2020 McKinsey survey found that global CIOs estimated their technical debt to represent approximately 20–40% of the total value of their technology assets. Meanwhile, the 2022 CISQ report, The Cost of Poor Software Quality in the US estimated that accumulated technical debt in the United States had reached approximately US$1.52 trillion.
At the operational level, software developers spend an average of 13.5 hours out of a 41.1-hour work week—roughly 33% of their time—addressing technical debt. In other words, one-third of an organization's innovation capacity is consumed by paying the "interest" on past decisions rather than creating new value.
Ironically, as automation advances and AI systems become capable of generating code almost instantly, the importance of managing technical debt has only increased. AI-assisted software development significantly accelerates coding, but it does not automatically produce robust and well-designed system architectures. Without proper governance, this acceleration can actually introduce new layers of complexity. If technical debt already poses substantial risks in traditional systems, its impact in the AI era could become exponential. AI can accelerate value creation—but it can also accelerate debt accumulation.
When AI Amplifies Risk
One of today's most significant changes is the increasing deployment of autonomous AI systems that support human activities at scale. AI is being used to detect financial fraud, assist medical diagnosis, optimize logistics, and manage industrial and energy operations. Many of these systems are safety-critical, meaning their failures can directly affect human lives.
AI systems depend on massive datasets, continuously evolving models, and extensive integration with other systems. When built on fragile foundations, the risks become multi-layered. System performance may deteriorate without detection. Data bias may produce unfair or discriminatory decisions. Weak integration may expose security vulnerabilities.
For systems supporting financial decisions, medical services, or public safety, these failures are not merely technical disruptions. They can escalate into reputational damage, legal liabilities, and even humanitarian consequences.
AI does not simply add new capabilities—it amplifies potential risks. If technical debt once slowed innovation, it now has the potential to undermine the stability of systems that support economic and social activities.
A Strategic Imperative for Indonesia
Indonesia is currently experiencing rapid digital transformation. The government is digitizing public services, the financial sector is adopting advanced analytics, industries are embracing intelligent automation, and startups are racing to integrate AI into their products. Yet amid this wave of innovation, a fundamental question is often overlooked:
Are our digital foundations strong enough?
Technical debt is no longer merely an operational issue. It has become a strategic risk that can affect national competitiveness and digital resilience. Three initial actions should become a shared agenda.
First, measure technical debt systematically.
What cannot be measured cannot be managed. Organizations need clear indicators to assess system quality and risks within critical systems, including data quality and model stability for AI-based applications. These measurements should become part of enterprise risk evaluation.
Second, establish effective governance.
Managing technical debt requires policies that define when risks are acceptable and when remediation is mandatory. Boards of directors and executive management should view investments in system modernization as long-term strategic investments rather than additional costs.
Third, embed governance into organizational processes.
Technical debt management should not be treated as a one-time initiative but should be integrated into software development, technology procurement, and performance evaluation processes. Continuous monitoring mechanisms and periodic system reviews are essential, particularly for mission-critical systems.
Protecting the Foundation Amid the Wave of Innovation
We cannot—and should not—slow the adoption of AI. The technology offers tremendous potential to improve productivity and service quality. However, much like constructing a high-rise building, the taller the structure we aim to build, the stronger the foundation it requires.
Technical debt is a natural consequence of innovation. The real danger is not its existence, but our lack of awareness and our failure to manage it. Without disciplined governance, AI will not become an accelerator of progress but an accelerator of systemic vulnerability. The digital transformation we celebrate could end up standing on fragile foundations.
In the AI era, where software systems are becoming increasingly autonomous and support ever more critical functions, managing technical debt is no longer simply a matter of good software engineering practice. It is a strategic responsibility—one that safeguards organizational stability, protects the public, and ensures that Indonesia's digital transformation is built upon a solid and sustainable foundation.