Article Summary: AI-assisted development is generating architectural technical debt faster than it resolves it— creating systemic instability for unprepared organizations. Adopting the right technical debt management tool to meet an organization’s requirements is imperative. EvolveWare has been recognized as a Representative Vendor in the 2026 Gartner® Market Guide for Technical Debt Management Tools. The company’s Intellisys platform helps organizations get ahead of this risk by providing deep visibility into existing systems across legacy and modern codebases.
According to the 2026 Gartner® Market Guide for Technical Debt Management Tools (accessible to Gartner subscribers only), “By 2028, AI will create more technical debt on architecture levels than it solves.”1 As organizations such as Microsoft and Anthropic roll out tools to address fixing code level technical debt, the remaining technical debt will be in architecture, system design, and dependencies. According to the Market Guide, that debt “creates systemic instability and long-term fragility.”1 This report is not the only one that mentions the potential pitfalls of AI-assisted development. In a report on the state of AI generated code, Sonar found that 61% of developers agree that AI often produces code that seems correct but isn’t reliable. They also found that 90% of issues associated with AI generated code are considered code smells or harder to pinpoint maintainability challenges. This means that a lot of potential time savings from using AI code generators are getting eaten up by oversight and verification that is required later. Or worse, if that verification is not performed, potential system failures may occur.
That’s why one of the report’s key recommendations is to “Prioritize the adoption of a technical debt management tool that addresses your most pressing need: architectural technical debt reduction, code quality improvement, application portfolio prioritization, or preparation for large scale system modernization.”1 EvolveWare is proud to be recognized as a Representative Vendor in this report. The company’s Intellisys Platform uses AI and automation to speed up and de-risk key processes involved in maintaining and modernizing applications, including providing robust documentation, diagrams, and reports of the current state of applications. Organizations can start simply by understanding their current systems and then use that information for future portfolio prioritization or modernization initiatives. For those who choose to rationalize or modernize their systems, the platform extracts unparalleled context from source systems and allows users to use that information to refactor first to power successful system migrations.
This is even more important for those with legacy systems or systems written in older technologies. With legacy systems there is the existing technical debt and then there is the new technical debt that is introduced when trying to adopt these newer AI technologies. If an organization is not careful, the compound of all of this can create major security gaps, budget overruns or complete shutdowns. Since the Intellisys platform supports reverse-engineering of applications written in 20+ languages including both legacy and modern ones, large organizations are able to assess entire portfolios written across multiple years as opposed to only being able to concentrate on a few. A State’s Department of Transportation used the tool to create an enterprise repository for consumable inventory applications written in COBOL, C#.NET with T-SQL, PL/SQL, and VB .NET prior to moving off the mainframe.
Organizations are also able to continue to assess the state of their code as changes are made. Intellisys supports agile documentation capabilities where newer versions of the source code can be processed and reports are generated identifying where changes to the code have been made and the impact. While working on a modernization initiative for their large IES system, the State of New York used this feature to identify where changes in the production code were made and how they impacted modernization work that had been performed prior to those changes. This ensured that the modernized version of the application would be in sync with any updates made to the production version of the code, as the project involved 31 million lines of legacy code and took 3 years to complete.
The relationship between AI adoption and technical debt is neither simple nor one-directional. AI offers productivity gains, but those gains come with architectural and maintainability costs that are only beginning to be fully understood. As the evidence from Gartner, Sonar, and others makes clear, the organizations best positioned to benefit from AI-assisted development are those that approach it with discipline — maintaining visibility into their systems, enforcing architectural standards, and actively managing the debt that accumulates over time.
For organizations carrying legacy systems into this new era, the stakes are particularly high. The compounding effect of pre-existing technical debt and the new debt introduced by AI adoption is not a theoretical risk — it is an operational one. The good news is that the tools and frameworks needed to manage this challenge are maturing alongside the AI technologies creating it. As AI continues to reshape how software is built and maintained, organizations that invest in understanding what they have today using a robust technical debt management tool will be far better equipped to make sound decisions about where they are going tomorrow.
To learn more about EvolveWare’s Intellisys Platform technical debt management tool capabilities, contact us.
1 Gartner, Market Guide for Technical Debt Management Tools, Tigran Egiazarov, Howard Dodd,Aaron Harrison, 13 April 2026
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