CIOs racing to experiment with AI models, test AI agents, and use vibe coding to develop applications may find themselves dealing with a new form of technical debt: AI debt. 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 AI governance in place, advances in frontier models and new AI agents from SaaS platforms means that what gets POC’ed and moved to production today will require unanticipated changes in future releases.
Learnings from technical debt
Creating a framework for understanding, avoiding, and resolving AI debt should build on practices for reducing technical debt.
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. Seven types of technical debt include performance-limiting data management practices, open-source dependencies, architecture limitations, and cultural inhibitors. Data debt includes data quality issues, manual steps in data pipelines, and data management that lacks observability.
Second, prioritizing the work to address technical debt requires CIOs to communicate in terms that both technologists and business leaders comprehend. When there isn’t a shared understanding, business pressure to upgrade or deliver new capabilities may limit the work needed to address security vulnerabilities, fix defects, or improve operational resiliency.
AI debt can also be categorized by its sources. It also requires CIOs to communicate the risks of deploying AI capabilities 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 Rob Scudiere, CTO at Verint. “When teams chase experimentation or hype instead of outcomes, they accumulate systems that are technically impressive but operationally irrelevant. Anchoring every AI initiative to clear, verifiable results prevents this debt and ensures investments translate into stronger, faster, scalable impact.”
Recommendation: Avoid chaotic AI experiments by creating an ideation process before committing resources and by tracking active AI initiatives. Require teams to define their targeted outcomes and establish a risk registry covering any unknowns, concerns, and future fixes related to their AI implementations.
Feeding poor data quality to AI models
Poor data quality, a data risk CIOs should be paranoid about, gets amplified when used with AI models and agents. One key practice is to define a data trust score and prevent teams from allowing AI models and agents to use datasets that fall below targeted thresholds.
“Data quality debt is one of the most dangerous forms of AI debt because errors in training data and inputs cascade through models, pipelines, and downstream decisions,” says Abhi Sharma, 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 data catalogs and data fabrics 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 Mayank Mahajan, associate director of engineering at Xebia. “Unlike humans, agents cannot question inconsistencies or fill gaps, so they execute on whatever data is available. Package data as governed, well-documented products and implement observability early so issues like schema drift or stale data are caught before they scale.”
Recommendation: Communicate a set of data governance non-negotiables that establish clear minimal criteria for using data in AI, including compliance requirements around data privacy. Strong data governance practices help reduce the risk of data-related AI debt.
AI model drifts
One key concern when deploying machine learning models is recognizing when real-time inference data drifts from the model’s training data. Model drift is also an issue when using retrieval-augmented generation (RAG) 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 Amitkumar Rathi, chief product officer at Virtana. “Observability across models, data pipelines, and infrastructure helps identify whether issues stem from data drift, pipeline failures, or resource constraints like GPU contention.”
Recommendation: The key to avoiding AI model debt is through modelops practices, 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 Ganesh Kirti, CEO at TrustLogix. “Every agent running with over-provisioned access or without context-aware controls is quietly accumulating security, compliance, and data integrity risk that compounds over time.”
Part of the challenge is that more organizations are deploying AI agents in mission-critical business processes. Deploying wide-scoped AI agents with broad data access rights can lead to operational risks and AI debt to address them.
“You insert AI into the process with a specific job defined, not any job, and you make sure that the inputs match the job specification,” says Matt Calkins, 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 non-human identities with their own entitlement lifecycle, continuous, policy-driven authorization that adapts in real time, and not one-time role grants that nobody revisits,” Kirti recommends.
Teaching AI agents broken business processes
Robotic process automation (RPA) 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 Don Schuerman, CTO and VP of marketing and technology strategy at Pegasystems. “Anyone can now quickly create an app with gen AI, but they struggle when they try to deploy it to do the real work inside real enterprises with all their complexities and dependencies.”
Recommendation: Avoid the rush to deploy AI agents before conducting upfront audits of existing processes and discussing future needs with business owners. Before generating a single line of code or deploying an AI agent, Schuerman recommends reimagining how work could be optimally done and how best to engage with customers across channels.
AI agent sprawl
AI agents are available across many platforms, and DevOps teams can use spec-driven development practices to build them. One organization’s chief digital officer recently told me they have already deployed over 1,000 AI agents.
The question is whether CIOs will repeat past mistakes when deploying tools that make it easy for business users to create new assets that are challenging to manage.
Consider the debt created by sprawling behaviors:
- Spreadsheets were great for analytics until business users created too many of them, and there were few tools to manage the underlying data practices and change lifecycles.
- Data visualizations were an upgrade, but top CIOs realized that governance practices were needed to manage citizen data science programs.
“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 Saket Srivastava, CIO at Asana. “Each unmanaged agent compounds risk, including security exposure, duplicated logic, and decisions no one can audit. CIOs should manage agents with the same rigor as employees, with clear ownership, role-based access to the right systems and data, and defined onboarding and retirement, before sprawl becomes a costly, hard-to-contain problem.”
Recommendation: CIOs of organizations with SaaS sprawl, especially those with a history of shadow IT, should define processes and controls for selecting, deploying, and managing AI agents.
Security lagging AI-generated code
The speed at which AI agents are developed, integrated with MCP servers, and deployed can compound security vulnerabilities. There are also questions about AI-generated code, with one study reporting that AI pull requests produce 1.4 times as many critical issues as human-generated ones.
Nikhil Mungel, head of AI R&D at Cribl, says, “AI-generated code debt can occur when teams use off-the-shelf tools that generate code that isn’t consistent with the company’s standards.”
Vrajesh Bhavsar, 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 open source options to their security programs. Mungel recommends embedding engineering best practices into skill frameworks, such as agents.md or skill.md, 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.
AI is only reshaping businesses and not transforming them yet. The question for CIOs is whether the pressure to move AI experiments into production and deliver business value will create new forms of AI debt for them to manage in the years to come.