Technical debt in low-code: hidden costs for enterprises

They are accumulated obligations sitting inside enterprise software portfolios, growing quietly while marketing decks celebrate “10x faster delivery.”

For organizations using low-code platforms to escape that trap, the uncomfortable reality is that visual development can accelerate debt accumulation as readily as it can reduce initial delivery effort.

Gartner expects 75% of new enterprise applications to be built on low-code platforms by 2026, up from under 25% in 2020. That adoption curve is real. What often disappears from the velocity narrative is the second-order cost: Deloitte’s 2026 Global Technology Leadership Study finds that technical debt consumes between 21% and 40% of total enterprise IT spending. McKinsey’s narrower estimate places the burden at 10% to 20% of the IT budget, rising as high as 60% of IT spending in organizations already carrying heavy debt.

The question is no longer whether low-code creates technical debt. It does. The more useful questions are who is tracking it, who owns it after launch, and how long an organization can keep adding applications before the hidden maintenance bill reaches the operating budget.

The Economic Reality of Visual Development Debt

Technical debt is not just a metaphor for untidy software. It is deferred maintenance, architectural shortcuts, undocumented dependencies, and temporary decisions that become permanent because nobody has budgeted time to revisit them. The interest is paid through future rework, incident response, security remediation, integration work, and features that never ship because the team is occupied keeping old systems functional.

In hand-coded environments, engineers at least have access to source-level visibility, version-control history, automated testing frameworks, and refactoring toolchains. Those tools do not prevent poor decisions, but they make the structure of the system easier to inspect. A developer can usually trace a dependency, compare revisions, search for references, and identify where a behavior was introduced.

In low-code environments, the debt manifests differently. It appears as orphaned workflows, duplicated business logic across disconnected applications, undocumented data relationships, environment-specific configurations, and platform automations that cannot be moved without significant reconstruction. The logic may be visible on a canvas, but visibility is not the same as understanding. A flow can be easy to draw and difficult to reason about once it has accumulated exceptions, integrations, permissions, and business rules.

The CISQ’s $1.52 trillion figure covers enterprise software debt broadly, not low-code exclusively. It should not be presented as a low-code measurement. But low-code is expanding the number of applications that enterprises can create, and that changes the shape of the portfolio that IT teams must govern.

When a non-developer assembles an application in three weeks that an internal engineering team might otherwise have built in four months, the initial time saving can be genuine. The problem begins when the application becomes operationally important. It may need to integrate with a regulated system, support a much larger user base, meet retention requirements, survive a platform upgrade, or remain available after its original builder moves to another role.

None of those obligations necessarily appear in the build-phase ROI calculation.

Low-code platforms compress development timelines. They do not compress the lifecycle. Maintenance, governance, and retirement costs still arrive after launch.

Three structural drivers explain why low-code debt becomes difficult to control as adoption expands.

• Visual abstractions hide coupling. A clean drag-and-drop interface can contain dependencies between data objects, permissions, connectors, scheduled jobs, approval paths, and external services. Changing one component may affect several others in ways that are not obvious from the screen where the change is made.
• Citizen development does not automatically include architectural literacy. That is not a criticism of business users. Workflow efficiency and system design are separate disciplines. Someone can understand a business process in detail while still lacking the tools to assess data ownership, failure modes, access boundaries, or long-term portability.
• Governance often trails adoption. Procurement may approve a platform faster than IT can establish application inventories, review thresholds, ownership rules, and retirement procedures. The result is a portfolio that grows in departments faster than it becomes visible to the people responsible for risk.

The pattern is familiar. The first year produces speed and enthusiasm. The second brings integration questions, permission exceptions, and competing versions of the same process. Later, the organization discovers that several applications depend on a builder who has left, a connector whose behavior has changed, or a data model that no longer matches the business.

That is not an argument against low-code. It is an argument against treating delivery speed as the main economic variable.

Shadow IT and the Governance Gap in Low-Code Adoption

Gartner estimates that shadow IT accounts for 30% to 40% of IT spending in large enterprises. Low-code platforms make this spending easier to create and harder to see. When a marketing operations lead, regional manager, or finance team can create an application without involving central IT, the spending may never appear in the centralized technology budget. The application becomes visible later, usually during a security audit, a data-residency review, an integration failure, or a request to expand access.

The governance gap is not primarily a citizen-developer problem. Internal IT departments create technical debt too. Under deadline pressure, professional engineering teams also ship minimum viable architectures, defer documentation, and accept workarounds that later harden into permanent infrastructure. The difference is that low-code lowers the barrier to creating production-like systems. More people can assemble workflows, connect data sources, and automate decisions without passing through the traditional architecture process.

That broader participation is useful when it remains within a clear operating model. It becomes risky when the organization confuses the ability to create an application with the ability to operate one safely over time.

The recurring governance failures are practical rather than theoretical:

• There is no centralized inventory of low-code applications running in production or handling business data.
• No one has defined ownership for an application after its original builder leaves the company, changes roles, or stops supporting the process.
• Applications handling sensitive or regulated data do not pass through a security and architecture review appropriate to their risk.
• The same business logic is implemented in several departmental applications, each with slightly different assumptions.
• Development, test, and production environments are not separated clearly enough to make changes auditable.
• Platform licenses, connectors, and execution environments are purchased independently, making the real portfolio cost difficult to calculate.
• No one has agreed on the conditions under which an application must be rebuilt, consolidated, or retired.

A workflow containing personally identifiable information does not become low risk because it was assembled visually. If it sits outside the security perimeter, lacks a named owner, or depends on a personal account, its risk is operationally real even when it has no line item in the IT budget.

The same applies to business continuity. A low-code application may be supporting customer onboarding, employee access requests, invoice approvals, or regulatory reporting. If the original builder is the only person who understands its exceptions and connectors, the organization has created a single point of failure. The application may appear simple to its users while being difficult to reproduce or repair.

Shadow IT therefore needs a more precise response than a blanket ban. A central IT team that blocks all departmental automation will encourage workarounds. A team that approves every request without establishing ownership will create an unmanaged application estate. The workable position is to make the safe path easier: approved platforms, known data connectors, visible ownership, proportionate review, and a clear route for moving an application into a more controlled architecture when its role expands.

Why Maintenance Cycles Consume 38% of Developer Capacity

Software developers spend approximately 33% to 38% of their working time maintaining legacy systems, fixing bugs, and managing technical debt rather than building new features. The figure predates the current low-code expansion, and it should not be treated as a universal measurement for every platform or organization. It is nevertheless a useful way to frame the opportunity cost. Maintenance is not only the time spent fixing a visible failure. It also includes investigation, regression testing, deployment coordination, documentation recovery, access reviews, and the work required to understand systems that were never designed as a coherent portfolio.

Low-code applications frequently demand a different kind of maintenance from equivalent hand-coded systems. The work is not necessarily larger in every case, but it can be harder to predict and more dependent on platform-specific knowledge.

Platform changes create recurring validation work

Visual platforms update frequently. A release can change component behavior, deprecate a feature, alter an API contract, modify permission handling, or introduce a new limitation in a connector. Even when the vendor maintains backward compatibility, the organization still needs to validate critical workflows.

This is vendor-driven technical debt: the enterprise carries maintenance obligations created by a roadmap it does not control. The work may include reviewing release notes, testing integrations, checking authentication behavior, validating calculated fields, and confirming that scheduled jobs still run under the expected identity.

For a small departmental workflow, that may be manageable. For a portfolio of applications connected to finance, customer operations, human resources, or regulated data, the validation burden becomes part of the operating model. Every additional application adds another surface that must be considered during a platform change.

Debugging is often less transparent

A low-code application can be visually readable while remaining difficult to diagnose. A failed workflow may pass through nested conditions, asynchronous jobs, several data sources, and platform-managed automations before producing an error. The person investigating the incident may need to reconstruct the sequence from run histories, connector logs, environment settings, and user permissions.

In a conventional application, engineers can often query logs, inspect stack traces, search the codebase, and reproduce a failing path locally. Low-code platforms may offer equivalent tools, but the depth and consistency of that observability vary. Some failures are exposed as a generic connector error. Others depend on a permission inherited from a parent object or a configuration that differs between environments.

The practical result is not that low-code systems cannot be debugged. It is that the debugging process can be more dependent on platform familiarity and on the quality of the original documentation. If the application was built quickly and never recorded its assumptions, the incident response team must rediscover those assumptions under pressure.

Refactoring is not always incremental

In hand-coded systems, a senior engineer can often restructure a module while preserving its external behavior. The work may still be complex, but the system can be changed in small, reviewable steps. Tests, interfaces, and version history provide a framework for controlling the change.

In low-code systems, restructuring can be more constrained. Business logic may be spread across screens, workflows, expressions, configuration tables, permissions, and platform-specific automations. A seemingly local change may depend on behavior that was never documented. The platform may not provide a clean way to extract, compare, or reuse that logic.

As a result, refactoring can become partial or total reconstruction. The organization may need to duplicate the application, rebuild selected workflows, migrate data, retest integrations, and run both versions during a transition period. The initial build estimate rarely includes that later work because it is not visible at the moment the application is created.

The asymmetry is operational: low-code is often faster to build, but that does not mean it is faster to evolve. It can also be more difficult to retire when the underlying business process, platform, or vendor relationship changes.

That distinction matters for portfolio planning. A fast build creates a new maintenance obligation sooner. If the organization measures only the time from request to launch, it rewards the easiest part of the lifecycle and leaves the harder parts unpriced.

Architectural Oversight: Moving Beyond Rapid Prototyping

The answer to compounding low-code debt is not simply to slow development. It is to apply structured governance before a prototype becomes production infrastructure.

Low-code is well suited to experimentation, departmental automation, and processes with a clear owner and limited integration surface. It becomes a different class of engineering problem when it handles regulated data, becomes a system of record, connects to core enterprise applications, or supports a process that cannot easily stop.

The organization needs a way to recognize that transition. Three controls provide a practical starting point.

Maintain a centralized application registry

Every low-code application in production should be recorded in a system accessible to IT, security, risk, and procurement. The registry does not need to turn every small automation into a major project. It does need to make the portfolio visible.

At a minimum, the record should identify:

• the business owner and technical contact;
• the business process supported by the application;
• the data classification and important data sources;
• the systems and connectors it depends on;
• the environments in which it runs;
• the platform version and license requirements;
• the expected lifecycle and review date;
• the conditions that would trigger migration or retirement.

An inventory changes the conversation. Without one, an organization cannot answer basic questions about how many applications exist, which ones process sensitive data, which ones have no active owner, or what would be affected by a platform or licensing change.

Set review thresholds based on risk

Not every application needs the same level of architecture review. A personal productivity automation and a customer-facing workflow should not move through an identical approval process.

A review threshold is appropriate when an application:

• handles regulated or sensitive data;
• integrates with core financial, customer, identity, or operational systems;
• creates or approves transactions;
• serves a large or external user population;
• becomes a dependency for another production application;
• requires elevated permissions or service accounts;
• has no straightforward manual fallback.

The purpose of the threshold is not bureaucratic friction. It is to distinguish a useful pilot from a production liability before the distinction becomes expensive. Review should examine data ownership, access control, error handling, observability, continuity, environment separation, and the likely exit path.

Define ownership beyond the original builder

A business owner is not always the same as the person who built the application. The business owner understands why the application exists and what happens if it fails. A technical owner understands how it is configured, how it connects to other systems, and how it can be changed safely. Both roles need to be explicit.

Ownership should also survive personnel changes. An application whose only documentation is inside one employee’s memory is not properly owned. The organization needs a transfer process covering credentials, connectors, deployment procedures, known exceptions, and support contacts.

Plan the exit before it is urgent

Every low-code application does not need a detailed migration plan on its first day. Every important application does need a credible answer to the question of how it will be decommissioned, rebuilt, or moved if circumstances change.

Vendor pricing can change. Platforms can be acquired or repositioned. Compliance requirements can shift. A business process can become too important for its original implementation. An application that was appropriate for a small team may become a critical dependency without any formal architecture decision.

An exit strategy does not predict failure. It limits the number of decisions that must be made during a crisis. The plan may specify a replacement system, an export method, a data-retention approach, or simply the conditions under which the application must be reviewed. What matters is that the organization has considered the path before the platform or the business need forces the issue.

Technical debt becomes manageable when the organization can see where it lives, name its owner, and budget for the work required to reduce it.

These controls do not eliminate technical debt. No delivery model does. They make the debt visible, which makes it possible to compare, prioritize, and fund. That is the difference between a known maintenance obligation and a surprise migration project.

Quantifying the Cost of Brittle Low-Code Ecosystems

The CISQ’s $2.41 trillion annual cost figure includes operational failures, security breaches, and rework across enterprise software generally. Low-code’s separate contribution is not quantified in the public research cited here. Standardized measures for visual development debt remain underdeveloped, and a platform’s number of applications or workflows is not enough to describe their real risk.

A small application with a clean owner and limited data access may carry little debt. A visually simple workflow connected to identity, payments, customer records, and multiple approval systems may carry a much larger operational obligation. Counting applications alone hides that difference.

What can be observed are the patterns that create cost:

• Platform migrations that require rebuilding workflows under compressed timelines.
• Audit findings against ungoverned visual applications handling regulated data.
• Integration debt created when low-code applications proliferate without a shared data model.
• License inflation as organizations add seats, environments, connectors, and execution capacity for applications that were never centrally tracked.
• Operational failures caused by personal accounts, undocumented credentials, or missing ownership.
• Duplicate applications that implement similar processes with different rules and produce inconsistent business data.
• Retirement work when a department no longer needs an application but cannot safely turn it off because other processes depend on it.

The financial impact is easiest to understand when technical debt is connected to the existing IT budget. For a mid-sized enterprise with $50 million in annual IT spending, a technical debt load at the lower end of Deloitte’s range, 21%, represents roughly $10.5 million associated with deferred work, rework, and operational drag. At the upper end, 40%, the corresponding amount is $20 million. McKinsey’s ceiling of 60% for heavily debt-loaded organizations would represent $30 million.

These calculations describe enterprise technical debt broadly, not low-code debt alone. They should not be used to claim that a particular low-code platform creates a specific dollar amount of liability. Their value is different: they show the scale of the budget that can be consumed when maintenance and remediation are treated as exceptions rather than as part of application ownership.

The economic mechanics are straightforward. Low-code reduces the upfront effort required to build certain applications. It does not remove the operating cost of running them. An application still needs access reviews, incident handling, platform validation, user support, documentation, data management, and eventual retirement.

The more applications an organization creates, the more important portfolio discipline becomes. A low-code platform can reduce the cost of producing an individual application while increasing the number of applications that require support. If governance, lifecycle management, and ownership do not expand with that portfolio, the apparent savings at the beginning are converted into recurring operating work.

That is where the sales narrative and the operating reality diverge. Faster delivery is a useful result. It is not, by itself, proof of lower total cost of ownership.

The Economic Reality of Enterprise Low-Code Debt

Enterprise low-code technical debt management starts with a change in how applications are evaluated. The relevant question is not only whether a team can build the requested workflow quickly. It is whether the organization can operate, change, secure, and eventually retire the resulting system.

A workable low-code platform maintenance strategy should therefore connect four decisions that are often separated:

1. Selection: Is low-code appropriate for this process, or does the process require a more controlled engineering approach from the beginning?

2. Ownership: Who is accountable for the application’s business outcomes, technical operation, data access, and continuity?

3. Lifecycle: How will the application be tested, monitored, reviewed, and changed as the platform and business requirements evolve?

4. Exit: What happens when the application reaches the limits of the platform, loses its owner, or no longer justifies its operating cost?

This is also the practical foundation for managing technical debt in low-code. The goal is not to prevent every shortcut. Some shortcuts are reasonable in a prototype or a short-lived internal tool. The goal is to prevent an undocumented shortcut from quietly becoming a permanent dependency.

Refactoring low-code business systems is most effective before the system becomes politically or operationally untouchable. That means reviewing repeated logic, consolidating duplicated data, documenting integrations, separating environments, and removing unused components while the application is still understood. Once the original builder has moved on and several departments depend on the application, even simple changes become negotiations across the organization.

The same principle applies to enterprise application lifecycle debt more broadly. Debt is cheaper to address when it is visible and still connected to a known decision. It becomes expensive when nobody can explain why the application works, what would break if it changed, or who has the authority to replace it.

Low-code does not make those questions less important. It makes them easier to postpone.

Low-code platforms deliver on their core promise: faster initial delivery. They do not deliver on the unspoken promise that faster delivery automatically translates into lower total cost of ownership. In some environments, the opposite can happen. Technical debt accumulates according to the volume of applications shipped, the complexity of their dependencies, the rigor of architectural review, and the maturity of governance—not simply according to how quickly the first version was assembled.

Organizations treating low-code as a tactical productivity tool for one-off automations, departmental utilities, and short-lifecycle applications can manage the debt. Organizations treating it as an ungoverned replacement for engineered enterprise systems risk recreating the broader software-quality problem inside a faster-moving visual portfolio.

The discipline is not in choosing low-code or rejecting it. The discipline is in knowing what is being built, who owns it after launch, which systems and data it touches, what it costs to maintain, and how it will be retired when the platform or the business need changes.

Without that discipline, every drag-and-drop shortcut is an obligation. With it, low-code can remain what it is best at: a way to move quickly without pretending that the rest of the application lifecycle has disappeared.