"Our core system has been running for over ten years. It gets the job done without major operational issues, but every time we request modifications, estimates inflate dramatically and delivery dates are unpredictable. The original developers left long ago, and right now no one inside the company fully understands how it works." Recently, we received this inquiry from an executive at a company with about 100 employees. The system is not broken. However, every attempt to modify it consumes ballooning time and budget, and no one possesses the full picture. It runs, but it actively drags down the business.
This phenomenon of a system that "runs, but no one dares touch" is the essence of technical debt. While it looks like an issue for on-the-ground engineers, it is genuinely an executive management risk that restricts company speed and strategic options. In 2026, the way we address this debt underwent a major transformation: AI agents began continuously visualizing technical debt and automatically remediating it. Taking this shift into account, this article explains the critical decisions management and procurement leaders must still retain, written from our perspective as a custom development partner.
Why technical debt is an executive business risk
First, let us examine why technical debt cannot be dismissed as merely an engineering concern.
Technical debt is a collective term for the accumulated difficulty of making changes, resulting from past development choices that prioritized simply getting things working. Outdated components whose maintenance has lapsed, architectures that were optimal back then but are obsolete today, missing documentation, and single-person dependencies. When these compound, adding even a single new feature requires immense time just to investigate ripple effects across the system.
This is where it hits executive management. Ballooning costs with every modification are a financial problem. The risk that no one understands the system when an employee resigns is a business continuity risk. An inability to patch discovered vulnerabilities in legacy dependencies is a security problem. And above all, entering a state where "every attempt to launch something new is held back by the weight of existing systems" robs the business of speed. While competitors launch new offerings in three months, your company takes a year due to legacy constraints. This goes beyond engineering productivity; it impacts competitive survival. Annual costs generated worldwide by technical debt are estimated in the trillions of dollars, and experts point out that the widespread adoption of AI coding can cause debt to accumulate even faster if left unmanaged.
The shift in 2026: AI continuously visualizes and remediates debt
Compounding this context is a monumental shift that arrived in 2026. Resolving technical debt historically required large overhaul projects undertaken once every few years. Today, AI agents are transforming that model into an approach of continuously fixing debt in small, incremental steps.
A prime example is "AWS Transform – continuous modernization," announced by AWS in June 2026. This system visualizes technical debt across thousands of repositories and autonomously detects and remediates common debt, such as end-of-life components, outdated frameworks, and known vulnerabilities. It shifts code refactoring from a massive multi-year project into ongoing background operations—effectively establishing Continuous Modernization (CM) beyond CI/CD (Continuous Integration and Continuous Delivery). Across AWS Transform as a whole, billions of lines of code have reportedly been processed, saving vast amounts of engineering effort.
This is welcome news. Technical debt that was previously considered too massive to touch has entered the realm of continuous, incremental remediation powered by AI. While the trend of modernizing legacy systems using AI assistance was covered in our article on AI-assisted legacy migration, the key novelty in 2026 is that migration can now run not as a one-time event, but as continuous, ongoing operations.
Decisions that still cannot be left entirely to AI
At this juncture, we must look at the situation calmly. It is tempting to think, "If AI fixes things automatically, why not outsource everything to it?" However, a critical pitfall lies here.
What AI excels at is detecting and remediating debt that can be evaluated mechanically: upgrading unmaintained dependencies, replacing clearly deprecated syntax, and patching known vulnerabilities. AI handles these tasks with unambiguous correct answers at astonishing speeds. On the other hand, there is something AI cannot determine: the prioritization of which debt to resolve, when, and to what degree.
Not all technical debt requires immediate fixing. In business domains that are rarely modified, aging code might cause negligible practical harm if left alone. Conversely, domains destined to become the company's primary business battleground should be refactored proactively, even if it requires substantial expenditure. This assessment of business criticality cannot be derived merely by looking at code; it can only be determined by cross-referencing company strategy. AI serves as a tool to visualize and fix debt, but deciding which visualized debts warrant capital allocation remains a human judgment rooted in business context. Delegating the task list of "what to fix" to AI while reserving the decision of "what to prioritize" for management and engineering leaders is essential. Reversing these roles risks having AI spend endless cycles modifying low-impact code while vital investments are bypassed. We explore the decision criteria for handling debt in our article on refactoring decisions in the AI era.
Complete overhaul vs. continuous modernization: Considerations for clients
With these points in mind, the strategic paths available when companies burdened with technical debt engage external partners become clear. Broadly, two avenues exist.
One path is a complete overhaul, rebuilding the legacy system from scratch all at once. This is chosen when debt is so severe that incremental repairs can no longer keep pace. While the impact is substantial, costs and timelines run high, and transition risks are elevated. The alternative path is continuous modernization leveraging AI agents. By keeping the current system operational, high-priority debts are resolved incrementally. While this allows progress without halting daily operations, achieving systemic optimization across the board takes time.
Which path is correct depends on the severity of the debt and business circumstances. What matters most for a client is first visualizing how much risk and cost the debt imposes on executive operations, and only then deciding between a complete overhaul and continuous modernization. If you skip visualization and simply place an order asking to "rebuild everything," you risk over-investing by replacing areas where continuous modernization would have sufficed. An overarching perspective on procuring system development in the AI era is detailed in our article on custom system development in the AI era.
Case study: A company that paused a total rebuild and began with visibility
Let us examine a concrete case. This involves the company mentioned earlier that struggled with a core system that no one dared touch (identity withheld). Their initial consultation was, "It's old, so we want to rebuild the entire system from scratch." However, when they gathered estimates, the project exceeded tens of millions of yen with an implementation timeline of over a year. The executive team hesitated, finding the hurdle too steep to greenlight.
We therefore proposed that before leaping into a total rebuild, they should start by visualizing the debt. We audited where debt existed across the system, what types were present, and how much had accumulated, analyzing what business risks each posed. This revealed that the system was not uniformly outdated. While debt was indeed critical around order management—an area undergoing frequent operational tweaks—legacy reporting features that were seldom touched caused virtually no practical harm if left alone.
The decision changed. The company abandoned the total rebuild, opting instead to prioritize modernizing the core order management domain while leaving low-impact modules running as-is for the time being. Mechanical tasks like updating unmaintained packages and patching vulnerabilities were expedited using AI, allowing human engineers to concentrate on prioritization and migration architecture. As a result, at a fraction of the original budget, they eliminated the bottleneck constraining business speed. The decisive factor was not rebuilding technology: it was pausing the urge to rebuild everything, visualizing the debt first, and setting clear priorities.
First, articulate "the losses this debt inflicts on your business"
If you are evaluating countermeasures for technical debt, clarify one thing before requesting quotes for a system rebuild: articulate in words the specific losses this debt inflicts on your business. Inflated modification fees, unpredictable delivery timelines, knowledge silos when staff leave, unpatched vulnerabilities, and sluggish speed when launching new initiatives. Only when these losses are visible can you determine where and how much to invest.
Even in an era where AI visualizes and remediates technical debt, the executive responsibility of deciding which debt to prioritize remains with humans. In fact, because AI accelerates execution, clients can focus more sharply on deciding what to fix. There is no need to rebuild everything overnight. By continuously resolving the costliest debts without stopping business operations and maintaining the correct sequence, technical debt can be reduced steadily without excessive capital expenditure.
If you are dealing with a system that runs but no one wants to touch, are frustrated by ever-inflating modification quotes, or need to determine whether a total rebuild or continuous modernization is the right call, please feel free to reach out through GleamHub's development, AI, and automation consultations. From debt visualization and business risk structuring to determining whether an overhaul or continuous modernization is appropriate, and designing efficient AI-enabled workflows, we partner with you to prevent over-investment.
Sources
- AWS launches a new continuous modernization capability in AWS Transform - AWS
- Proactively reduce tech debt autonomously with AWS Transform – continuous modernization (preview) - AWS
- Smash tech debt and get AI ready – AWS Transform
- AWS Transform and Agentic Modernisation: The End of Technical Debt - Devoteam
- One year. 4.5 billion lines of code. 1.6 million hours saved. - AWS Migration & Modernization
