Why prompt debt, retrieval debt, and evaluation debt are quietly reshaping enterprise AI risk
Over the past two decades, technical debt meant outdated architecture, messy code, and poorly maintained documentation. That definition is no longer sufficient in the AI era, where failure modes are more subtle and often non-linear. AI systems are introducing new layers of technical debt that live across prompts, models, and data dependencies — making these layers less visible, harder to measure, and often more dangerous than traditional debt.
A crisis hiding in plain sight
The complexities of AI systems and their associated failures have been well documented. A 2025 MIT study found that 95% of AI projects fail to reach production or deliver value. A similar study by S&P Global Market Intelligence found that 42% of businesses scrapped multiple AI initiativesin 2025 — a sharp increase from 17% the previous year. Various reasons are cited for these failures, but most of them point to poorly designed and implemented systems...
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