AI has potential to help IT departments reduce technical debt by cleaning legacy code and streamlining overbuilt software; for example, Microsoft recently launched autonomous AI agents to modernize older Java and .NET applications. However, many IT professionals caution that the rise of numerous AI pilot projects and reliance on complex AI models can actually increase technical debt, especially when projects lack clear roadmaps or fail to scale beyond proof-of-concept stages. This leads to legacy tools that become obsolete and costly to maintain.
Excessive experimentation with AI without proper governance, as described by experts, causes “pilot paralysis” where resources are drained on multiple small, uncoordinated initiatives. AI-generated code often produces superfluous software that requires rigorous code reviews and continuous integration to prevent quality degradation. Furthermore, decentralized AI development across business units without senior IT oversight risks creating fragmented systems and disconnects between AI agents and core business processes.
Leaders emphasize the importance of governance tools that track data security, compliance, spending, and project metrics, ideally under CIO supervision, to maintain trust in AI outputs. Continuous evaluation and readiness to quickly fail and adapt are required as AI agents need ongoing updates aligned with evolving business goals. Without these measures, companies risk accumulating expensive technological liabilities and eroding user confidence in AI solutions.
👉 Pročitaj original: CIO Magazine
