7 sources of AI debt and how to avoid them

CIOs racing to experiment with AI models, test AI agents, and use . The pressure to accelerate proofs of concept (POCs) into production will likely drive teams to cut corners and leave known improvements as “to-dos” for future releases.

But speed isn’t the only factor that will create AI debt. Even with strong and .

First, not all technical debt carries the same risks or priorities for resolution. Understanding the source of technical debt can help rank the likelihood of the issue impacting business operations and its severity. . , manual steps in .

Second, prioritizing the work to address technical debt before they are fully tested. But as we are early in the AI era, CIOs should put significant focus on ways to avoid the different types of AI debt as part of defining governance and guardrails.

Here are seven AI debt sources to consider, along with ways to avoid them.

AI experiments without targeted outcomes

The pressure to get more employees learning AI and testing AI agents is needed but comes at a cost. In addition to people’s time and rising token costs, prioritizing work without defining objectives can increase AI debt without leaving a clear understanding of whether the AI capability is delivering value. 

“Outcome debt builds when organizations deploy AI without defining the specific, measurable business results they expect it to deliver,” says by creating an ideation process before committing resources and by tracking active AI initiatives. Require teams to define their targeted outcomes and establish a , gets amplified when used with AI models and agents. One key practice is to define a , co-founder and CEO at Relyance AI. “The best way to prevent it is to establish continuous data lineage and governance so teams can trace inputs, monitor transformations, and correct issues before they propagate into model behavior.”

A second practice is to extend by establishing reusable data products. Data products become shareable multi-purpose assets with their own release cycle, treating AI agents and other users as customers.

“Organizations often don’t recognize data quality issues until an AI agent acts on flawed data, amplifying errors at speed and without human judgment,” says that establish clear minimal criteria for using data in AI, including compliance requirements around data privacy. Strong or providing other contextual data to large language models.

“AI model debt builds when models drift or degrade without teams knowing why, leading to compounding performance and reliability issues,” says , including cataloging models, implementing observability, baselining training data statistics, and monitoring for outcome drift. Rathi recommends, “By continuously linking model outcomes to operating conditions, teams can act early by retraining, fixing data inputs, or rebalancing resources before issues accumulate into long-term model debt.”

Overly entitled AI agents

Should AI agents have the same data access rights as their users? One form of AI debt is when access permissions and the underlying entitlements for AI agents require revisiting and auditing. Worse is when over-permissioned AI agents expose confidential data or make erroneous decisions when accessing sensitive data.

“Enterprises are deploying AI agents that query databases, trigger workflows, and make decisions at machine speed, yet they’re granting these agents broad, static permissions modeled on how humans access data,” says , CEO of Appian. “You must give the AI agent a narrow range of possible outputs.”

Recommendation: To reduce the risk of AI debt from overly empowered AI agents, review the outcomes, decisions, and recommendations AI agents will be responsible for and apply a bottom-up review of the required data entitlements. “To avoid this debt, organizations need to treat AI agents as governed delivered significant ROI when applied to well-defined business processes. With AI agents, some have suggested that role- and task-based agents can perform the required work by providing sufficient context. But this assumes that existing business processes and the data they generate are accurate and reliable.

“Too many organizations are rushing to layer agentic automation on top of their existing processes and infrastructure,” says and how best to engage with customers across channels.

AI agent sprawl

AI agents are available across many platforms, and DevOps teams can use .

“Many companies already have more agents than employees, but lack lifecycle management, with no visibility into what agents exist, what data they access, or when they should be retired,” says , especially those with a history of .

Security lagging AI-generated code

The speed at which AI agents are developed, integrated with that AI pull requests produce 1.4 times as many critical issues as human-generated ones.

, CEO and co-founder at Operant AI, adds, “As organizations scale AI agents and multi-step AI workflows, they inherit a rapidly expanding attack surface where adversaries are actively exploiting new threat patterns, including zero-click attacks that require no user interaction to trigger data exfiltration, poisoning, or leakage mid-workflow.”

Recommendation: Organizations that use AI code generators, vibe coding, or spec-driven development methodologies should consider adding AI code review tools such as CodeRabbit, DeepCode, Qodo, SonarQube, or to ensure outputs comply with internal guidelines. In addition, Bhavsar recommends deploying adaptive, agentic-aware security controls that can detect and stop threats targeting AI agents in real-time.

will create new forms of AI debt for them to manage in the years to come.