Confident and Wrong: The Hidden Technical Debt Accumulating Inside Your Enterprise AI Deployments
Language models occupy an unusual position in enterprise software: they produce outputs that are fluent, authoritative, and occasionally incorrect in ways that resist easy detection. As organizations accelerate AI integration into production workflows, a new category of technical debt is quietly accumulating—one that conventional quality assurance practices were never designed to surface. The costs are real, and they are compounding.