Over the past two decades, technical debt has meant outdated architecture, messy code, and poorly maintained documentation. In the age of AI, where failure modes are more subtle and often non-linear, this definition is no longer sufficient. AI systems introduce new layers of technical debt that live between instructions, models, and data dependencies—making these layers less visible, harder to measure, and often more dangerous than traditional debt.
A crisis hiding in plain sight
The complexity of AI systems and their associated failures are well documented. An MIT study in 2025 found that 95% of AI projects fail achieve production or deliver value. A similar study by S&P Global Market Intelligence found that 42% of enterprises have canceled multiple AI initiatives In 2025 — a sharp increase of 17% compared to the previous year. Various reasons are given for these failures, but most point to poorly designed and implemented systems with multiple points of failure that are complex to manage and difficult to monitor, leading to the rapid accumulation of AI debt.
Traditional technical debt was localized in the code base, and bugs were usually easily reproducible. As a result, errors during testing can be easily identified and corrected by rebuilding the code base. However, AI debt is more distributed and manifests itself across instructions, models, data pipelines, and all associated infrastructure. It’s also more intermittent: Due to the probabilistic nature of AI, systems don’t always react in the same way, leading to intermittent failures. This makes it much more difficult to identify risks during testing, and also creates a need for more continuous monitoring, even after deployment, to avoid gradual drift and performance degradation.
New forms of AI debt
AI debt typically comes in four new forms, each of which comes with its own set of risks.
Urgent debt is the most prominent of these. A modern version of “spaghetti code”, this can include undocumented hotfixes, bundled “hotfix” hints that cause inconsistencies, versioning of hints released, and “quickfilling” (pushing extraneous data or context directly into AI hints). All of these combine to make instructions a form of unwritten, untested code without any version control, leading to increased fragility and vulnerability.
Model addiction debt AI is an increasingly common form of debt. Most enterprises now depend on a mix of external models developed by leading foundational model providers; applications and agents build on API calls to these models. Consequently, application logic now depends on models that are external to the underlying system and cannot be clearly controlled. As models are updated, performance changes and reproducibility is lost — instructions configured for one model may fail or perform poorly when switched to another model, whether from the same provider or an update from a different provider.
Most enterprise AI applications today use retrieval-augmented generation (RAG), which draws on context in addition to enterprise data stores. Apple debt the result of these repositories having mixed data, duplicate documents and outdated data. This causes the AI to return outdated and technically correct answers that are no longer relevant, leading to downstream failures. Unlike hallucinations, they are more difficult to detect because they may have been correct until recently and therefore appear correct to any tester.
Appraisal debt It reflects a lack of standardization in testing and monitoring for AI models and applications. Although AI benchmarks exist, they tend to focus on narrow tests and reflect results over time. Most enterprises lack consistent testing standards, ground-truth databases, and real-time monitoring of deployments; There is not yet an equivalent of continuous integration/continuous delivery (CI/CD) for instructions. As a result, CIOs and CTOs lack visibility into model performance and are unable to track improvements or deterioration of models.
All of this is in addition to the traditional forms of technical debt that still manifests in the tools and systems that AI applications and agents interact with, read, or write to. The rapid adoption of AI-generated code (often implemented without adequate testing) is exacerbating internal inconsistencies and the poor maintainability of traditional codebases.
New forms of AI debt are rapidly merging with these earlier forms of technical debt, creating far-reaching risks that could lead to the catastrophic failure of entire enterprise deployments. Addressing these risks is further complicated by the distributed nature of AI ownership—most systems span engineering, product, data, and business teams, leading to uncertain accountability in the event of misidentification.
As a result, these risks manifest themselves in the form of increased computational costs, inaccuracies in AI results, and an increase in exceptions that must be handled by humans – often causing projects to stall and fail due to uncertain return-on-investment stories and a lack of user trust.
How businesses can avoid AI debt
The AI debt will not be solved by “better” models – even though the models are already highly accurate, failure rates remain high. Addressing AI debt requires better system design, integration, controls, and changes in organizational culture.
First, instructions should be viewed as code. This includes careful version control, documentation, and rigorous testing both before and after deployment for all possible operational configurations. Best practices from the traditional coding world — such as using smaller blocks of operands instead of large operand-filled walls or reducing the use of hard-coded parameters — can also help reduce AI debt.
Second, evaluation must be built into the entire AI infrastructure stack. Continuous assessment pipelines should be established and should reflect a variety of metrics that measure both technical and business-aligned metrics. In addition, AI observation systems must be integrated to monitor output quality, failure rates, model drift, and data drift.
Third, to compensate for limited reproducibility, explainability should be included as standard in all AI results. The data line, models used and steps followed should be clearly traceable to allow verification of results and corrections in case of any system errors.
This requires open AI debt reduction programs and associated budgets similar to previous waves of investment in security or cloud modernization. These should be managed by key leaders at the CXO level to avoid costly rework later.
Result: A stitch in time
Enterprise AI deployments aren’t just static code; they are living systems that interact with the entire enterprise stack. Consequently, the defining challenge in the agent enterprise will not be building or deploying intelligent systems, but maintaining those systems to ensure continued reliability during real-world operations.
Enterprises that proactively identify and reduce AI debt from the design stage are more likely to build sustainable AI platforms that drive significant long-term productivity across the organization.
Vikram is a director at Cota Capital, which invests in early stage enterprise technology and deep technology companies.
