Is AI Bloating Your Technical Debt? What You Need to Know

To understand what technical debt means, look no further than the rapid expansion of Artificial Intelligence (AI). AI adoption is predicted to enjoy a 37.3% annual growth rate from 2023 and 2030, with the number of organizations adopting AI increasing significantly since just last year—a 17% increase overall, according to McKinsey, and a 32% increase in the use of generative AI. But in the rush toward AI-driven adoption, many companies remain unaware of the risk posed by such rapid adoption: technical debt. Innovation has to be balanced with maintenance; for leaders, understanding the implications of technical debt in the context of AI models is crucial. As they innovate, it is vital to strategize on how to manage existing debt to keep systems efficient and future-ready.

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Settling for short-term gains while ignoring technical debt can lead to serious long-term consequences. Here, we detail proactive steps organizations can take to integrate best practices for AI model management, ensuring that innovation does not come at the cost of future flexibility and performance.

Key Takeaways

• While rapid AI adoption can increase technical debt through hasty implementation and ongoing maintenance needs, but organizations can mitigate these risks by implementing regular maintenance and observability tools.
• Effective management of AI-related technical debt requires a balanced approach, including careful vendor selection, maintaining flexibility, rigorous planning, adopting a tiered implementation strategy, and investing in staff upskilling and observability tools.
• AI technologies, particularly Large Language Models (LLMs), can help reduce technical debt by streamlining code management, improving efficiency, and enhancing overall software quality.

What Is Technical Debt?

Technical debt refers to the future cost incurred when choosing an easy solution now instead of a better approach that would take longer. Or, less egregiously—allowing technical issues or data hygiene tasks to go unaddressed for too long. This concept is common in software development and AI implementations—a CompTIA survey of 523 IT professionals found that 74% of organizations are challenged by technical debt.

As Ward Cunningham, one of the authors of the Agile Manifesto, said: “A little debt speeds development so long as it is paid back promptly with refactoring. The danger occurs when the debt is not repaid.”

An effective approach might involve balancing new feature development with paying down technical debt or allocating a percentage of a team’s weekly budget to address it.

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How Technical Debt Manifests in AI: Model Drift and More

Technical debt in AI is usually evidenced by model drift. Training an AI model requires a large and diverse dataset to ensure accuracy, so if the data is biased or incomplete—or the input data changes over time—the model's performance may suffer.

A prime example: is fraud detection systems. As fraudulent behaviors evolve, fraudsters develop new strategies to evade detection, leading to gradual model drift. So a machine learning (ML) model trained on historical fraudulent transaction data may become less effective over time as the nature of fraud changes. 

Another example is the impact of the COVID-19 pandemic, which caused sudden model drift by drastically altering consumer behavior patterns, such as increased spending on home fitness equipment and decreased spending on transportation services. ML models trained on pre-pandemic data could therefore be dangerously inaccurate.

Here are some of the other ways AI technical debt can hinder an organization:

• Rushed Adoption and Customization: Generative AI implementation often leads to hasty integration. Additionally, many organizations find that off-the-shelf AI models don't fully meet their specific needs, requiring customization. However, this customization process can introduce complexity and potential incompatibilities, further contributing to technical debt.

• Ongoing Maintenance: This includes monitoring for bias, addressing model drift as real-world conditions change, and adapting to shifting business goals and consumer preferences. The need for continuous management introduces a new layer of complexity to an organization's technical infrastructure, potentially increasing technical debt if not handled properly.

• Rapid Changes and Unsupported Tools: The rapid evolution of AI also means that some companies and products will inevitably fail or become obsolete. When this happens, organizations may rely on unsupported tools or outdated AI models, creating a form of technical debt that must be addressed by migrating to new solutions.

• Accelerated Development: While AI can significantly speed up the development process, it can also result in less time for proper planning, testing, and consideration of long-term maintainability. This rapid pace could lead to an increase in technical debt if proper safeguards and quality control measures are not put in place.

Reducing AI Technical Debt

By effectively managing maintenance needs, budgeting appropriately, consciously designing teams, and more, organizations can ensure that their systems remain robust and capable, allowing innovation to flourish without disruptions. Here are ten strategies for reducing AI technical debt via a more calculated approach to AI implementation:

1. Conduct a Maintenance Assessment: Maintenance is key on this list—continuous QA, data maintenance, and human oversight of AI programs. Organizations must regularly review their systems to identify areas requiring updates or repairs. This includes checking software performance, security patches, and system reliability.
   
   

Keys for an effective maintenance assessment:

1. Regular audits: Conduct periodic reviews of AI-driven software and infrastructure.
2. Feedback loops: Gather input from stakeholders to understand potential pain points.
3. Prioritize fixes: Address the most critical issues that could impact business operations.

2. Invest in Observability Tools: These tools should be capable of identifying both traditional architectural issues and AI-specific challenges such as model drift. Investing in best practices for maintaining clean and adaptable application architectures is crucial. This proactive approach ensures that systems remain flexible and ready for future developments in AI technology, reducing the likelihood of accruing technical debt due to outdated or inflexible architectures.

3. Budget for Technical Debt: Organizations should factor technical debt into the equation when planning projects. The amount of resources allocated to addressing it can vary considerably based on the application—a general AI solution that scans text requires fewer quality checks than a transportation-based solution like self-driving cars or railway systems.

4. Embrace Proof of Concepts and Iteration: This approach allows teams to test the waters, identify potential issues, and gain valuable insights before committing to a full-scale implementation. By iterating on these smaller projects, companies can refine their approach and minimize the risk of accruing significant technical debt.

5. Form Agile Teams: Agile teams should be focused on quickly learning about AI technologies and their applications within the company's specific context. They can then disseminate their learnings throughout the organization, ensuring best practices are adopted widely.

6. Be Choosy: Look beyond flashy presentations and marketing hype to carefully evaluate the technical merits of each solution, as well as the business viability of the companies providing them. This helps avoid reliance on a tools that may become unsupported in the future, thus preventing a major source of technical debt.

7. Maintain flexibility: Avoid over-commitment to a single platform or provider; instead, build systems that can adapt to changes in the AI landscape to reduce the risk of being locked into outdated or suboptimal solutions.

8. Plan Rigorously: Generative AI should be deployed at the same level of planning and preparation as traditional software development. This includes establishing clear guidelines for AI usage, setting up quality control measures, and creating processes for reviewing and refining AI-generated outputs. Human oversight must be balanced.

9. Adopt a Tiered Approach: Start with less critical areas, review the results, and gradually expand to more crucial systems. At each stage, the organization can compare notes across development teams, review outcomes against established standards, and make necessary adjustments.

10. Upskill and Hire: Integrating AI while minimizing technical debt requires a workforce with the right skills and knowledge. Organizations should invest in upskilling their existing teams, providing training on AI technologies and best practices, and consider hiring new talent with specialized AI expertise.

Can AI Decrease Technical Debt?

While the ability for AI to update itself remains a topic of debate and research, AI also offers powerful tools for reducing and managing technical debt. AI can streamline code management, improve efficiency, and enhance overall software quality when implemented thoughtfully. Here are a few ways in which AI technologies—particularly Large Language Models (LLMs) and associated tools—can help organizations decrease their technical debt and create more maintainable, efficient software systems:

• LLMs, Frameworks, and APIs: Large Language Models (LLMs), frameworks, and APIs can help reduce technical debt. These technologies can automate many code management and optimization aspects, identifying inefficiencies and suggesting improvements. 

• AI-Assisted Code Discovery and Reuse: AI technologies, particularly LLMs, can analyze vast codebases, identifying similar or duplicate code snippets across different projects or modules. By facilitating better code reuse, AI helps reduce redundancy and inconsistencies, which are common sources of technical debt.

• Identification and Consolidation of Duplicative Code: LLMs can scan through entire codebases, recognizing patterns and similarities that might be missed by human developers. Once identified, these duplicate sections can be refactored into more efficient, centralized functions or libraries.

• Automated Documentation Generation: LLMs can analyze code and automatically produce clear, comprehensive documentation. This saves developers time and ensures that documentation remains current as code evolves, significantly improving code discoverability and reducing the likelihood of duplicative efforts.

• Ensuring Adherence to Company Coding Standards: AI models can be trained on a company's specific coding standards and best practices. As developers write code, these AI tools can provide real-time suggestions and corrections to ensure adherence to these standards.
• Enhancing Automation: AI tools can significantly improve automation across various software development and maintenance processes. This includes automating testing procedures, code reviews, and even certain deployment aspects.

• Accelerating the Transformation of Old Codebases: AI tools can analyze old code, understand its functionality, and suggest or implement updates to align with modern standards and practices. This can significantly reduce the burden of maintaining outdated systems, a significant source of technical debt for many organizations.

• Empowering Non-Coders: AI tools can empower non-technical staff to participate more actively in software development and process improvement. These tools can translate business requirements into code or pseudo-code, allowing non-coders to prototype ideas or implement simple automation. This broader participation can lead to more efficient business processes and reduce the backlog of requests that often contribute to technical debt.

AI in Technical Debt: Challenge and Opportunity

Rapid AI technology adoption presents challenges and opportunities for managing technical debt. While hasty implementation and the need for ongoing maintenance can increase technical debt, organizations can mitigate these risks through strategic approaches. Organizations can decrease their technical debt by offering AI solutions to reduce technical debt through code management, efficiency improvements, and enhanced software quality.

Technical debt is a useful rubric for evaluating AI as a whole: a balancing act of innovation and thoughtful implementation.

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