AI debt reshapes enterprise risk, impacting projects

Enterprise AI systems are introducing new forms of technical debt beyond traditional code issues, including prompt, model dependency, retrieval, and evaluation debt. These complexities make AI systems harder to manage, less visible, and prone to subtle, non-linear failures. Studies indicate a high percentage of AI projects fail to reach production or deliver value due to these accumulating risks. Addressing this requires treating prompts as code with version control and testing, building continuous evaluation pipelines, and integrating explainability for auditability. Proactive mitigation from the design phase is crucial for sustainable AI platforms and long-term productivity.