AI tools and autonomous agents are becoming part of everyday work in the development of digital solutions at breakneck speed. This shift is fundamentally changing how teams operate and innovate. However, along the way, a new kind of hidden cost is starting to appear: AI debt.
Just like technical debt in traditional software projects, AI debt begins to build up when organizations adopt AI faster than they establish the processes, governance, and shared ways of working needed to manage it.
In the first part of this blog series, we explored how AI agents are changing the way we create digital services. In this post, we take a closer look at the AI debt that accumulates when AI adoption in software development outpaces the structures needed to support it.
What is AI debt and how does it form?
AI debt describes the hidden, long-term costs incurred when teams prioritize short-term gains in AI-related work over long-term sustainability. It builds up as teams make decisions that favor immediate results over the sustainable growth of their AI initiatives (a trend identified by Gartner in late 2025). Just like technical debt, AI debt can accumulate in many areas – code, processes, governance, and data management – often growing quietly in the background.
In agentic coding, AI generates far more code, documentation, and specs than a human can realistically process. This creates bottlenecks in the development lifecycle, particularly in code reviews, where human intervention is still needed. It’s possible to fall into a trap where code slips through without a clear understanding of how it actually works. This creates a cycle where the code returned from agentic workflows is not always aligned with the original intent.
The further AI-driven development progresses, the more rapidly AI debt accumulates. The ease of content generation makes it increasingly difficult for human teams to keep pace with the exponential growth of the codebase.
Why AI debt matters now
We have moved from the era of technical debt into the era of AI debt. While the phenomenon is new and often unrecognized, AI debt is here to stay, and every organization building digital solutions needs to address it.
AI-driven development alters the entire workflow. AI agents now execute the bulk of the work – from implementation to testing and even deployment – while humans provide oversight and strategic guidance. Consequently, the cognitive load for developers is significantly higher than in traditional methods. Humans must now oversee a much higher volume of autonomous output, leading to new operational challenges.
As autonomous workflows expand, unmanaged AI use can lead to sprawling systems. Without careful attention, trained developers, and clear governance, the speed and productivity gains AI provides can eventually translate into maintenance problems, security risks, and operational inefficiencies.
AI debt is inevitable, but manageable
Eventually, all debt must be paid. The difference with AI is that, given its speed and scale, the impact of the debt can become much larger, much faster. Here’s how to manage AI debt:
1. Use AI to check AI: Set up "guardrails" (such as strict system prompts) where one AI agent audits the work of another. Harness AI to evaluate its own output by running automated checks on quality, security, and efficiency.
2. Create rules for AI agents: Developers must shift from being "writers" to "architects." Agents need a shared blueprint or architectural rules to follow. This prevents a chaotic codebase where different agents solve the same problem in inconsistent ways.
3. Scale small: The best way to manage high speed is to work in tiny increments. Keep features small and tightly scoped to ensure they remain reviewable, and take manageable steps to ensure developers stay aligned with the project direction. Use short-lived feature branches to facilitate frequent integration and reduce the risk of massive, unmanageable pull requests.
4. Adopt a new operating model: Managing this requires a shift in thinking where AI is treated as a new operating model rather than just a tool. This includes updating roles, responsibilities, governance, and guidelines to reflect the agentic reality.
AI debt introduces a new dimension to adopting AI in digital solution development. However, when recognized and addressed systematically, the work becomes adaptive and efficient, with human teams and agents complementing each other in ways previously unimaginable. The key is to build operating models that remain maintainable over time.
As leading experts in this area, we’ve already navigated these challenges firsthand in our own projects and are prepared to help our customers tackle them. If you want to discuss how AI-driven development can benefit your next project without the burden of debt, contact us.
Simo Martomaa,
Director, Technology and Development
