CIOs perennially deal with technical debt’s risks, costs, and complexities. While the impacts of legacy systems can be quantified, technical debt is also often embedded in subtler ways across the IT ecosystem, making it hard to account for the full list of issues and risks.
Forrester reports that 30% of IT leaders struggle with high or critical debt, while 49% more face moderate levels. Even in the case of moderate to low risk, technical debt impacts can change quickly as business needs evolve. After all, a low-risk annoyance in a key application can become a sizable boulder when the app requires modernization to support a digital transformation initiative.
Accenture reports that the top three sources of technical debt are enterprise applications, AI, and enterprise architecture. These areas are considerable issues, but what about data, security, culture, and addressing areas where past shortcuts are fast becoming today’s liabilities? Another question is: What separates out debt that’s fixed opportunistically versus critical debt that could cripple the business?
To address known and unknown factors that could derail their organizations from transformation, CIOs should consider the following seven types of technical debt, what makes them critical, and what they should do about it.
1. Data debt that undermines decision-making
In Digital Trailblazer, I share a story of a private company that reported a profitable year to the board, only to return after the holiday to find that data quality issues and calculation mistakes turned it into an unprofitable one.
CIOs who change the culture to be more data-driven and implement citizen data science are most impacted by data debt, as the wrong interpretation or calculation of a date, amount, or threshold can lead to the wrong business decisions. Types of data debt include dark data, duplicate records, and data that hasn’t been integrated with master data sources.
Using the company’s data in LLMs, AI agents, or other generative AI models creates more risk. Data biases, gaps in classifying data, and data sources with inadequate authorization policies can all lead to bad decisions, compliance risks, and customer-impacting issues. For this reason, organizations with significant data debt may find pursuing many gen AI opportunities more challenging and risky.
What CIOs can do: Avoid and reduce data debt by incorporating data governance and analytics responsibilities in agile data teams, implementing data observability, and developing data quality metrics.
2. Data management debt that throttles performance
Data management debt can happen in a flash, build up over time, result from a lack of automation, or be driven by incident response:
- A flash: IT departments that lifted and shifted large databases to the cloud without optimizing the data architecture may have created a steep step up in database management debt to operationalize over time.
- Build up: Databases that have grown in size, complexity, and usage build up the need to rearchitect the model and architecture to support that growth over time.
- Lack of automation: Database admins spend too much time on manual operating procedures that should be automated, including creating backups, administering privileges, syncing data across systems, or provisioning infrastructure.
- Incident response: Firefighting daily issues, responding to major incidents, or performing root cause analysis prevents database administrators from performing more proactive tasks.
“Even modest investments in database tooling and paying down some data management debt can relieve database administrators of the tedium of manual updates or reactive monitoring,” says Graham McMillan, CTO of Redgate. “This will free them to bring their skills and creativity to higher-value activities such as enhancing data security and delivering innovative solutions for customers.”
What CIOs can do: Measure the amount of time database administrators spend on manual operating procedures and incident response to gauge data management debt. Options to reduce data management debt include automating tasks, migrating to database as a service (DbaaS) offerings, and archiving older datasets.
3. Open source dependency debt that weighs down DevOps
As a software developer, writing code feels easier than reviewing someone else’s and understanding how to use it. Searching and integrating open source libraries and components can be even easier, as the weight of long-term support isn’t at the top of many developers’ minds when they are pressured to meet deadlines and deploy frequently.
“Many teams neglect dependency hygiene, letting outdated, redundant, or unsupported open-source components pile up,” says Mitchell Johnson, CPDO of Sonatype. “The average app contains 180 components, and failing to update them leads to bloated code, security gaps, and mounting technical debt. Just as no one wants to run mission-critical systems on decade-old hardware, modern SDLC and DevOps practices must treat software dependencies the same way — keep them updated, streamlined, and secure.”
According to the 2025 Open Source Security and Risk Analysis Report from Black Duck, 81% of risk-assessed codebases contained high- or critical-risk vulnerabilities, and 90% contained components more than 10 versions behind the most current version. CIOs should look for signs where open-source dependency debt is crippling DevOps productivity, including the frequency of disruptive code updates, increases in security alerts, or time spent on addressing dependency conflicts.
What CIOs can do: Educate DevOps teams on open source security risks, establish governance policies on evaluating and approving open-source packages, and use SAST tools to find code vulnerabilities.
4. AI debt that will require significant rework
Gen AI tools and capabilities are introducing new sources of technical debt. Even when CIOs have AI governance defined, rapidly changing gen AI models, regulations, and agentic AI capabilities will create AI debt issues.
“Technical debt in AI systems manifests differently than traditional architectural debt, as it’s not just about code maintainability, but about the entire data and model governance lifecycle,” says Eric Johnson, CIO of PagerDuty. “Companies rushing to build custom AI solutions today risk creating new forms of technical debt that could prove more costly and complex to unwind than the architectural challenges we’ve faced in the past. The key is establishing strong data governance and infrastructure foundations before diving into AI implementations.”
While many forms of technical debt drive ongoing maintenance issues, AI model drift is one example of incremental AI debt. But some AI debt may require CIOs to decommission and replace AI capabilities, for example, when new models have sizable accuracy, performance, or cost improvements, leaving behind obsolete models. Another concern is if regulations force holistic model retraining, forcing CIOs to switch to alternatives to remain compliant.
What CIOs can do: To make transitions to new AI capabilities less costly, invest in regression testing and change management practices around AI-enabled large-scale workflows.
5. Architecture debt that erodes to create legacy systems
Some forms of application architecture debt can be remedied through modernizations, migrating applications to new platforms, or using gen AI tools to document and explain legacy codebases. Some of the bigger sources of architectural debt include:
- Significant code customizations embedded in ERPs and other enterprise systems
- Point-to-point integrations between systems without using data fabrics or integration platforms
- Microservices and APIs deployed without security, testing, versioning, and observability standards
- Multicloud architectures configured for early deployment benefits that require significant cost, time, and expertise to maintain
CIOs with sprawling architectures should consider simplifications and one step to establish architectural observability practices. These include creating architecture and platform performance indicators by aggregating application-level monitoring, observability, code quality, total costs, DevOps cycle times, and incident metrics as a tool to evaluate where architecture impacts business operations.
“Without architectural observability and governance, AI-driven development can introduce microservices sprawl, accelerate architectural drift, and lead to hidden dependencies which compound architectural technical debt, the most damaging form of tech debt that impacts performance and scalability,” says Amir Rapson, co-founder & CTO of vFunction. “Engineering teams also risk drowning in tangled service interactions instead of delivering new features. Gen AI is a powerful enabler, but sustainable success depends on architectural observability for long-term innovation.”
What CIOs can do: Evolutions in technology create architecture debt that all CIOs have to address over time, otherwise, the debt becomes an unsupportable legacy system. One area CIOs can control is governing whether and how to implement customization to avoid business rule complexities wired into code. A second area is to rethink the architecture review board and define self-organizing standards, clearly indicating the decision authorities around architecture between agile development teams and enterprise architects.
6. Unexplainable security debt in AI implementations
Security debt comes in many forms, such as a lack of enforceable policies, inadequate end-user training, and failure to shift left security practices in DevOps. CISOs are in never-ending cycles of playing catch-up to these security gaps while addressing the latest threats.
Playing catch-up with AI models may not be that easy. While organizations can take steps to prevent confidential information from being used to train AI models, it’s hard to know what private information is in the model or whether there are options to remove it.
“Generative AI models can introduce new security risks, such as vulnerabilities in the model itself, data breaches, and adversarial attacks,” says Giovanni Lanzani, managing director of data at Xebia. Security debt can accumulate when these risks are not adequately addressed.
Lanzani shares an example of a bank’s customer-facing chatbot. “The instance would require a scaled gen AI framework that implements strong prompt injection guardrails to avoid giving financial advice or talking poorly about the bank. It also anonymizes all PII so the cloud-hosted chatbot can’t be fed private information.”
What CIOs can do: The security practices in DevSecOps lagged CI/CD automations, and businesses were fast implementing citizen data science, leaving many data governance practices as to-dos. Falling behind AI governance practices may yield unacceptable risks, especially as AI agents are deployed in enterprise and customer-facing applications.
7. Cultural debt that accelerates business disruption
The hardest part of digital transformation is gaining early adopters, driving change management, and addressing pushback from detractors. Gen AI adds more cultural debt as subject matter experts age out of the workforce, leaving little behind for employees with AI capabilities to take on new responsibilities.
Joe Byrne, field CTO of LaunchDarkly, says, “Cultural debt can have several negative impacts, but specific to AI, a lack of proper engineering practices, resistance to innovation, tribal knowledge gaps, and failure to adopt modern practices all create significant roadblocks to successfully leveraging AI.”
What CIOs can do: CIOs looking to use AI beyond a productivity driver and seek transformational outcomes should recognize how important it is to reduce job-loss fears and guide employees on using AI to augment, not just automate, their capabilities.
While CIOs are under pressure to accelerate delivering AI and other modernizations, leaving behind too much technical debt can become a drag force on innovation and transformation.
Read More from This Article: 7 types of tech debt that could cripple your business
Source: News
