AI Can Eviscerate Technical Debt, But Only If You Let It

If you’re serious about scaling AI across your product stack, you have to face one increasingly troublesome problem: chiefly, that your technical debt will either eviscerate your AI, or your AI will finally help you burn that debt down.

The global cost of tech debt has already surpassed $1.5 trillion, and Forrester predicts that 75% of technology decision-makers will see their technical debt rise to a moderate or high level of severity by 2026, due to the rapid development of AI solutions adding complexity to IT landscapes. That’s before you factor in the messy sprawl of AI-written code and security gaps piling up in the rush to deploy.

And that’s before you add the new flavors of debt unleashed by AI, including pipelines built on brittle code, half-trained models running on shaky infra, lapsed security, and “quick fixes” that become permanent fixtures no one dares to touch.

The question isn’t just how to keep up with AI. It’s whether your foundation can survive it. And it’s a necessity for CIOs,CTOs and other tech leaders to find a solution before it's too late. 

The New Anatomy of Technical Debt in the Age of AI

Traditional technical debt was mostly about messy code and deferred maintenance. Today's AI-driven organizations face a more complex debt portfolio:

• Code debt has evolved beyond simple spaghetti code. AI-generated code often lacks context, creates dependencies that developers don't understand, and introduces patterns that work in isolation but fail at scale. Quick AI fixes become permanent fixtures that no one dares to touch because understanding the original intent becomes impossible.
• Data debt represents the most insidious new category. Broken pipelines multiply across model retraining cycles, data lineage gets lost in complex ML workflows, and model drift goes undetected until it causes downstream failures. When your models are only as good as your data, dirty pipelines become business-critical vulnerabilities.
• Infrastructure debt emerges as legacy systems buckle under the computational demands of modern AI. Systems designed for traditional workloads struggle with the memory, processing, and storage requirements of large language models and complex AI pipelines.
• Model debt creates an entirely new category of technical liability. Half-trained models running in production, unmonitored algorithms making decisions without human oversight, and AI systems that degrade over time without proper maintenance represent hidden time bombs in your technology stack.

Your definition of technical debt must expand far beyond "bad code" to encompass the entire AI lifecycle.

Why the Old Rules Don't Work Anymore

The traditional software development tradeoff, of "ship fast now, fix later”, fundamentally breaks when AI enters the equation.:

• Interest compounds faster: In traditional software, a bug might slow down a single feature. In AI systems, a pipeline bug multiplies across every model retrain, every data refresh, every automated decision. Small cracks become existential failures overnight.
• Dependencies become opaque: When AI generates code or makes architectural decisions, the logical connections that human developers would document get lost. Teams inherit systems they can't fully understand, making future changes exponentially more risky and expensive.
• Scale amplifies consequences: An AI model making thousands of decisions per second can turn a small logic error into massive business impact in hours, not months. The feedback loops that once gave teams time to course-correct now operate in real-time.

If a data pipeline bug introduces a small bias in training data in traditional software, this might affect just a single report. But in an AI system, this bias gets baked into every model retrain, influencing thousands of automated decisions before anyone notices the pattern. By the time the issue surfaces, the cost to fix it includes retraining models, auditing historical decisions, and potentially rebuilding entire systems.

Where AI Becomes the Solution

The same technology creating new forms of debt can also fight existing debt more effectively than traditional approaches:

1. Automated refactoring and test coverage powered by AI can tackle legacy codebases at unprecedented speed. AI agents can analyze complex systems, identify refactoring opportunities, and generate comprehensive test suites that would take human developers months to create.
2. Data observability and pipeline monitoring get supercharged by AI systems that can detect anomalies, trace data lineage, and predict pipeline failures before they impact downstream systems. These tools can surface data quality issues that human teams might miss for quarters.
3. DevOps automation and agent-driven remediation loops create self-healing infrastructure that can identify, diagnose, and fix common issues without human intervention. When properly configured, these systems can resolve technical debt as it accumulates rather than allowing it to compound.

AI debt-fighting tools work at machine scale and speed, processing vast amounts of system data to surface problems that would be invisible to human observation until they become critical.

The Human Factor: Judgment Still Matters

AI can surface issues with unprecedented speed and accuracy, but it cannot make the strategic trade-offs that define effective debt management. Human judgment remains essential for several critical decisions:

• System-level consequences require technologists who understand how changes ripple through complex architectures. An AI might identify that a piece of legacy code should be refactored, but only experienced engineers can assess whether that refactoring might break integration patterns or stall performance elsewhere in the system.
• Trade-off decisions between technical perfection and business velocity require context that AI cannot fully grasp. Sometimes technical debt is the right choice, for example when it enables faster iteration, competitive advantage, or strategic flexibility. Knowing when to accept debt versus when to pay it down requires business understanding and technical judgment.
• Governance and architectural vision ensure that AI-assisted remediation aligns with long-term system goals. Without human oversight, automated fixes might solve immediate problems while creating larger architectural inconsistencies.

The leadership challenge becomes clear: build AI-literate teams who understand both the capabilities and limitations of automated tools. The most effective organizations combine AI-powered debt detection with human-driven debt strategy.

The Playbook for Tech Leaders

Transform technical debt from a liability into a manageable cost of innovation with this practical framework:

1. Map your debt landscape across all dimensions, including legacy code, data pipelines, infrastructure bottlenecks, and model lifecycle gaps. Use AI-powered analysis tools to discover hidden debt that manual audits miss, but ensure human experts validate and prioritize findings.
2. Distinguish between debt types. Innovation debt - intentional shortcuts taken to test hypotheses or meet strategic deadlines - differs fundamentally from toxic debt that accumulates through neglect or poor practices. Track both categories separately and budget for their remediation differently.
3. Automate where AI adds leverage. Deploy automated refactoring tools for well-understood code patterns, use AI-driven monitoring for data pipeline health, and implement agent-based remediation for routine infrastructure issues. Focus human effort on strategic decisions and complex system interactions.
4. Build governance around AI-generated changes. Establish review processes for AI-assisted code modifications, implement testing requirements for automated refactoring, and create audit trails for AI-driven system changes. The goal is acceleration with accountability, not blind automation.
5. Hire for the AI era. Embed engineers who are fluent in both AI tools and systems thinking. These professionals should understand how to prompt AI effectively, interpret AI-generated solutions critically, and make architectural decisions that leverage AI capabilities while maintaining system integrity.

The Real Question

Sadly technical debt won't vanish, as it's an inherent cost of innovation and competitive speed. The fundamental question facing every technology leader is whether AI multiplies your debt burden or helps you reduce it in real-time.

As CIO.com notes, “AI alone can’t make the hard calls about technical debt trade-offs. That still requires judgment, experience, and deep system knowledge.”

The organizations that thrive will be those that use AI not as a replacement for good engineering practices, but as an amplifier of human judgment and technical expertise. They'll build systems that accumulate intentional, manageable debt while automatically paying down toxic debt before it compounds.

AI won't erase technical debt. But with the right systems, governance, and talent, it might finally stop debt from erasing you.

Looking to hire AI-native engineers who know how to work with Copilot, integrate agents into DevOps pipelines, and reduce technical debt with every sprint?

Andela connects global organizations with AI-expert developers who understand that moving fast is only smart when you're also building wisely. Talk to an Andela expert today.