Enterprise AI projects face a staggering 95% failure rate when moving from proof-of-concept to production. This isn't due to algorithmic limitations but structural issues that accumulate as "Production Debt." The gap between optimizing for demos versus building reliable, deterministic systems in enterprise environments creates insurmountable challenges for most AI initiatives.

Technical Debt manifests as brittle prompts that break when models deviate from expected outputs. Operational Debt emerges from ownership vacuums between data science and DevOps teams. Evaluation Debt stems from relying on subjective "vibe checks" rather than objective metrics, making it impossible to safely iterate on complex agentic systems that must perform consistently.

Integration Debt occurs when AI systems are built without understanding downstream interfaces, while Governance Debt—the project killer—stems from neglecting compliance requirements until launch day. Organizations must address these debts early by implementing strict data contracts, establishing clear ownership, building automated test suites, and embedding governance from the ground up.