In my article on AI Debt (to be published later this week), I wrote about seven sources of concern for CIOs. These included AI code-generated quality issues, overly entitled AI agents, and AI agent sprawl.

I left one area out – deliberately – because it’s a category by itself – AI cost debt.

The speed of AI experimentation has some similarities with how IT pursued the cloud in its early days. Speed often trumped building resilient architectures, robust security, and making cost tradeoff decisions. While these are factors impacting fast AI rollouts, it’s AI’s rapid technological evolution, coupled with frontier model pricing changes, that make AI cost debt a real concern.

The WSJ reported this week that corporations are beginning to ration AI amid skyrocketing costs. I would suggest it’s a good time for CIOs to elevate AI FinOps to a higher priority, review where AI cost debt is a growing risk, and begin remediation plans.

8 AI cost debt issues that can be avoided

Cloud cost management required tooling, but had straightforward causes and remediations. We had a body of knowledge from infrastructure, data center, and ITSM to draw on to drive improvements. Managing AI cost debt is primarily about avoidance, especially as more AI agents are deployed to production.

Here are eight AI cost debts and avoidance recommendations.

1. Deploying AI over poor data quality

“The most dangerous form of AI debt is a lack of trust that stems from companies deploying AI on top of fragmented, unverified data, which is why only 28% of leaders actually trust AI for decision-making,” says Andy MacMillan, CEO at Alteryx.

Macmillan is quoting Alteryx’s recent report, From AI Pilots to Enterprise Impact. According to their report, only 23% of companies are successfully scaling AI, and 78% agree that their organizations adhere to their data governance policies.

Recommendation: MacMillan says the fix is not more models, but a stronger foundation built on data readiness and modern governance, with observability, validation, and humans in the loop to define what right looks like.

2. Underperforming AI model code quality

Sonar publishes a code quality and security leaderboard highlighting vulnerability, reliability, and maintainability of AI models. 

“Accumulating AI model debt occurs when LLMs and agents are chosen based on general performance rather than specific code generation profiles,” says Joe Tyler, AI researcher at Sonar. “In our benchmarking of 1000s of Java tasks, we found ‘top’ models vary wildly: GPT-5.3 Codex generated ~25% more code than its predecessor, also increasing in complexity and bug density. This creates issues for agents, making it harder to navigate and maintain the codebase, and leading to expensive iterations with many tokens burnt.”

Recommendation:  Tyler recommends choosing models with low complexity and bug density, and integrating real-time feedback loops via MCP or CLI tools so agents can self-correct during generation.

3. Compounding AI tools from low-value use cases

Experimentation is good, but the number of experiments that lead to production deployments needs improvement in many organizations. The challenge arises when experiments compound the number of AI tools, and there are no/limited governance disciplines to select them or manage their lifecycle.

“One form of AI debt organizations face is early vendor lock-in from broad, enterprise-wide AI deployments,” says Chris Andersen, CFO at Flexera. “The challenge comes when teams still push for additional AI tools, and as CFO, I look to avoid compounding that AI debt. Ideally, an organization would require any new AI investments to start with narrowly defined pilots, clear use cases, and measurable ROI.”

Mike Freedman, cofounder and CTO at Tiger Data,  adds, “The AI debt I see most often is infrastructure sprawl, because teams keep bolting on specialized tools for each new AI requirement. A vector database here, a streaming pipeline there, an object store over there, and each one adds failure modes, maintenance burden, and integration complexity that only gets worse over time.”

Recommendation: Freedman recommends that organizations avoid the trap of accumulating tool sprawl by consolidating on a single foundation early. Andersen says that to avoid AI debt, the goal should be to scale only what demonstrates long-term business value, ensuring that every AI tool introduced into an organization serves a defined purpose.

4. Exploding token costs

Some vibe coding and AI agent development platforms offer their tools for free, with pricing based on outcomes once apps and AI agents are deployed to production. Review this vibe coding tool and this development studio for examples.

But these are exceptions, and tokens are expensive. CIOs with exploding costs need tools for tracking usage that offer optimization recommendations.

“FinOps tackles [AI cost debt] by analyzing the token-to-cost ratio across different LLMs, ensuring that applications use smaller, cheaper models or cached tokens for repetitive prompts rather than expensive frontier models,” says Robin Roacho, lead cloud economics engineer at Insight. “This approach also involves optimizing prompt engineering and chunking strategies, directly reducing the volume of processed tokens to prevent compounding API costs.”

Recommendation: CIOs should start by defining AI vision statements and expressing business value as part of their AI strategies. Organizations with large AI programs should assign FinOps responsibilities, select tools that provide recommendations rather than just insights, and hold teams accountable for their costs.

5. Accelerating AI without protections, not just policies

Consider data protection implementation realities.

• Easy: Generating and updating data protection policies.
• Harder: Communicating policies to employees in ways they understand and increase compliance.  
• Critical: Leveraging technology that implements, monitors, or enforces data protection policies.

“The AI debt most enterprises don’t see coming is trust debt — the gap between what they assume their AI stack protects and what they can actually prove,” says Aaron Fulkerson, CEO at OPAQUE Systems. “Every agent handoff, every RAG query against proprietary data is a potential leak, and most organizations manage that exposure with policies and prayers rather than hardware-enforced, cryptographically verifiable guarantees.”

Recommendation: Data privacy requires leadership accountability, not just updating and communicating policies. CIOs must partner with CISOs and their data governance leaders to protect sensitive data and prevent its leakage from their organizations.

6. Deploying AI agents over outdated business processes

Can CIOs learn the mistakes of deploying Robotic Process Automations (RPAs) on top of broken processes? Or will they deploy AI agents by first implementing critical process management, understanding outcomes, and establishing target metrics?

“Urban planners often talk about ‘desire paths,’ or the dirt trails that appear where people ignore the paved walkway and cut through the grass,” says Tomás Dostal Freire, CIO and head of business transformation at Miro. “In the same way, AI agent debt forms when leaders design elegant stone pathways while employees are already working differently, layering agents onto workflows that don’t reflect reality and scaling complexity instead of value.”

Recommendation: Dostal says organizations can avoid this debt by first observing how work truly flows, clarifying data ownership and decision boundaries, and then paving the paths people have already proven effective.

7. Building AI agents without lifecycle management

Like developing applications and APIs, building your own AI agents may be the easy part. Managing lifecycles is often the greater challenge, and understanding and reducing AI debt are emerging practices.  

“While managing prompts in software has been addressed by libraries like DSPy and Mellea, the lifecycle management of skills remains a problem,” says Nikolaos Vasiloglou, VP of research ML at RelationalAI. “Traditional CI/CD tools are not suitable, so stale and buggy skills, not to mention insecure/vulnerable ones, can be part of the production code.”

Recommendation: Until best practices and tools for AI agent lifecycle management are established, DevOps teams will need to identify tailored approaches for their architectures. Unpacking the lifecycle management of AI agents likely requires steps that cover the underlying infrastructure, platforms, models, development tools, AI-generated code, MCP integrations, prompts, skills, and data.

8. Using an unoptimized AI inference architecture

Developers and data scientists need an architecture built for speed and flexibility when testing AI models and agents. But for production deployments, leading organizations define their inference architecture optimized for cost, performance, security, and resiliency. 

“Many organizations started their AI journey in the public cloud, but this can create financial AI debt down the line when they fully operationalize and scale AI initiatives,” says Michael Byrne, VP of datacenter solution architecture at Presidio. “Emerging physical AI use cases occur where the data is created and where decisions need to be made in real-time, necessitating an extension of the AI factory to the edge.”

Recommendation: Byrne says this form of AI debt can be avoided by fully understanding enterprise data models and by designing hybrid AI environments that reduce dependency on proprietary tools and enable workload portability to deliver the desired business outcomes.

Improve AI governance to avoid costly AI debt

If you’re struggling with these issues, consider my workshop on AI Strategy and Governance – a leadership workshop bringing together business, data/AI, IT, security, and risk management leaders. Additionally, FinOps and tech debt management are covered in my workshops on World-Class IT in the AI Era.