Technical Debt in the AI Age: Reasons and Remediation Tips

The growing AI adoption helps businesses automate and enhance many workflows, but it also has a downside. By accelerating software development lifecycles, AI can increase technical debt.

In fact, in 2024, at the height of the AI development boom, companies were allocating about 15% of their IT budgets to remediate technical debt, especially for new IT projects.

But how does this tech debt appear, and what is the role of AI adoption in it? And how can you reduce it?

In this article, we will explain the most common causes of tech debt in the age of AI and provide tips and best remediation practices for it.

Technical debt is a long-term cost incurred when organizations choose shortcuts over more sustainable software development approaches. It typically occurs when time or budget constraints make teams sacrifice code quality, architecture, documentation, or testing.

Just like financial debt, over time, technical debt generates “interest” in the form of higher maintenance costs, slower innovation, and challenges with system management and maintenance.

Without a proper response, all such factors limit an organization’s flexibility and affect its revenue directly. In particular, companies that actively manage and reduce tech debt report projected revenue growth of 5.3% for 2024–2026, compared to 4.4% among companies with higher debt levels. The problem of technical debt in the global IT industry applies to businesses of different sizes. For instance, the total cost of technical debt among Global 2000 companies is almost reaching $2 trillion.

Another significant problem with technical debt is that it hinders technology adoption over time. The greater the technical debt, the more your organization falls behind in adopting new technology trends.

All these figures highlight the value of disciplined engineering practices and sustainable technology strategies for long-term business growth and cost-effectiveness.

However, in the conditions of the AI race, the problem of tech debt is more relevant than ever. Many businesses are eager to adopt AI as quickly as possible and sometimes simply label their products as “AI-powered,” regardless of the actual value or cost. With such an approach, problems like technical debt accumulate over time.

So, how does AI impact technical debt in practice? Below, we provide an overview of common cases where fast adoption of artificial intelligence contributes to organizational tech debt.

Growing AI adoption accelerates development cycles

In 2024, 62% of AI users were relying on AI-powered tools for coding assistance. Such an approach has evident benefits because the use of AI can boost task completion by 7.7% across industries. However, this acceleration often comes at the cost of shortcuts in architecture design, documentation, and testing. For instance, the StackOverflow research on bugs in AI coding reveals that artificial intelligence created 1.7 times as many bugs as humans.

AI-generated code may appear functional but still contain hidden inefficiencies, security vulnerabilities, or architectural inconsistencies. Over time, such problems can grow, and the system becomes opaque, harder to debug, extend, and maintain. This lack of clarity can compound the AI code tech debt that reduces overall system resilience and productivity.

Organizations face complexities while integrating new AI components

New AI models appear and update at a solid pace. Businesses may be tempted by the possibility of integrating such models and features they offer into their products as soon as possible. However, the introduction of new AI models, data pipelines, and external APIs adds new layers to system architecture. If multiple integrations are performed fast, this may lead to a disorganized infrastructure, causing significant challenges with monitoring, versioning, and lifecycle management.

Moreover, by integrating AI into legacy systems, businesses can expose structural weaknesses and create additional dependencies that can contribute to the growing technical debt.

Data infrastructures become increasingly complex

Properly organized data of good quality is fundamental to any AI system. In particular, 42% of businesses cite the lack of proprietary data for training the models as one of the major AI adoption challenges. In an attempt to compensate for the lack of proprietary data, some companies turn to synthetic datasets or quickly assemble unreliable data pipelines. While this may help them launch AI features faster, it often results in disorganized and excessively complex data infrastructures.

Poor data management, fragmented storage, inconsistent schemas, and weak compliance controls can quickly turn into structural liabilities. Model drifts can also create additional regulatory compliance challenges. Attempts to build data infrastructure to power AI models as quickly as possible often leave underlying issues unresolved. Over time, as technical debt accumulates, companies are forced to address these problems anyway.

While AI has great potential for enhancing productivity and innovation, its rapid and unstructured adoption can significantly increase technical debt. To avoid such a problem, the companies should adhere to clear standards, apply disciplined engineering practices, and establish strong AI oversight.

If you are just planning to build an AI-powered product, always start with a strong business case. Not all software systems need AI components, especially complex ones. Very basic AI model integrations without heavy customization will fit when you need to enhance simple and predictable workflows, such as email classification or text extraction.

Hiring an AI development team to integrate complex models because competitors are doing it is a poor strategic choice. Embrace a full-fledged AI transformation only if this brings you tangible and specific business value.

And if you want AI to accelerate your product rather than destabilize it, technical debt remediation is crucial. Here are five crucial practices that we at Leobit suggest you follow while developing your AI solution in order to minimize the tech debt.

Let’s explore these approaches and workflows in more detail.

1. Build your software with future code refactoring in mind

Assume refactoring is inevitable right when you are building a solution. Today’s “working” can become tomorrow’s opaque dependency chain. Design modules so they can be updated or replaced without ruining your entire operations. The most efficient approaches include:

• Defining clear boundaries between business logic and AI components
• Featuring toggles for experimental AI features
• Using customizable APIs to keep all infrastructure components working together properly

Remember that AI models change and APIs evolve, which means that you should build your solution with replacements in mind.

2. Modernize your software before integrating AI components

Legacy systems and AI integrations do not mix well. If your core platform depends on an obsolete architectural approach, outdated frameworks, or fragile data pipelines, AI integration may be inefficient. You can try to fix the problem by modernizing AI touchpoints — for example, by customizing the APIs. However, without full-fledged system modernization, such practices can work as mere shortcuts that escalate your technical debt.

Therefore, focus on modernizing your architecture before integrating artificial intelligence into your solution. Move towards a more flexible architecture, such as a modular monolith or microservices. Clean up data models, eliminate redundant integrations, and ensure continuous monitoring of your infrastructure’s performance.

AI relies heavily on your system’s elasticity. Without it, artificial intelligence can quickly add to your technical debt and ultimately undermine your profitability.

3. Write clear documentation

While developing an AI-powered solution or integrating AI into the existing app, make sure to document each step. Well-organized documentation adds clarity and simplifies further enhancement, optimization, and other workflows that are vital for tech debt reduction.

For instance, document the following:

• Reasons a particular model or integration technique was chosen
• All data flows feed the model
• Data tuning techniques applied and the logic behind them
• Output validation techniques
• Model limitations and bias risks
• Cost implications per request

Without documentation, your AI features become “black boxes” that are hard to refactor and secure. Write documentation with future major upgrades, architectural shifts, and model changes in mind.

4. Test early and test often

AI introduces probabilistic behavior into deterministic systems. That’s why traditional system and code quality assurance practices may not work. In addition to traditional unit and integration tests, consider:

• Prompt regression testing
• Output consistency thresholds
• Bias and fairness evaluation
• Performance and latency stress testing
• Cost monitoring under scale

This will help you validate both the efficiency of your AI models and the performance of your infrastructure. Regular testing also allows you to identify system weaknesses that may accumulate over time and increase technical debt.

5. Establish regular code and architecture reviews

This practice applies to the cases when you have already developed a software solution and have a (possibly growing) technical debt.

Whether your software includes AI-generated code or short-term architectural decisions and AI touchpoints built with shortcuts, code and architecture reviews are essential. Focus on the following issues that are really common:

• Security vulnerabilities
• Redundant abstractions
• Inefficient queries
• Hallucinated methods or dependencies
• AI component isolation
• Model versioning strategy
• Fallback logic when AI services fail
• Vendor lock-in risks

In addition to regular code and software architecture reviews, run a comprehensive technical audit from time to time. It helps you identify the key vulnerabilities of the existing codebase or architecture.

As a result, you will understand the main causes of the problem and come up with efficient approaches to technical debt management.

Remediating an AI technical debt without the necessary expertise and hands-on understanding of common causes might be a very challenging task. Most of the practices aimed at remediating the technical debt that we have mentioned above should be applied throughout the software development or AI integration lifecycle. We at Leobit have significant experience in building AI-powered solutions for different industries, which has granted us the status of Microsoft Solutions Partner for Data & AI. While building AI-powered software, we apply best practices to make sure that you won’t be struggling with a growing technical debt after the release.

Our skilled specialists can also help you deal with the existing technical debt by running a technical audit and helping you identify the problems with AI integrations that keep your technical debt growing.

We can also help you modernize your software to prepare it for AI integrations and eliminate obsolete, resource-consuming parts of the infrastructure.

So, whether you want to build a custom AI software from scratch, integrate artificial intelligence into your existing solution, or deal with a technical debt in an existing AI-powered system that is already running, we are ready to help.

Technical debt is a problem that causes businesses many issues when it comes to software maintenance costs, technology adoption, or operational overhead. As more and more companies accelerate development cycles, run fast and complex integrations, and build excessively complex data infrastructures in pursuit of AI adoption, this issue becomes even more relevant.

Some of the best ways for remediating technical debt in the AI age include:

• Building AI software with future refactoring in mind
• Modernizing systems before AI integration
• Writing clear documentation
• Applying regular testing practices
• Reviewing the system on a regular basis and running comprehensive technical audits from time to time

Leobit is ready to help you follow these practices and build AI-powered software that won’t become your long-term burden. We can also help you modernize an existing solution to remediate an AI technical debt that already exists. Contact us to find out how we can help you maximize your AI adoption value without hidden challenges.