Organizations lack a designated owner for AI verification, conflating correctness with verifiability as distinct failure modes.

Harvey AI rebuilt its document review algorithm in April 2026 not because it was producing incorrect results, but because the original system attached citations to whole cells rather than individual statements, making it impossible for lawyers to verify the reasoning behind each conclusion.

Anthropic discovered that its BBQ bias benchmark showed near-zero bias scores, but the underlying cause was that models were deflecting or refusing to answer questions—a behavior that registers as technically unbiased because non-answers cannot be biased. Scale AI's HiL-Bench (April 2026) found frontier agents solved up to 89% of software engineering tasks with full context, but performance dropped to 24% when realistic details were removed; agents did not ask for help and shipped wrong outputs confidently.

Most organizations lack a dedicated role—comparable to the CISO in security—with explicit authority and budget to own the evaluation pipeline, define production-derived test cases, sample live outputs, and escalate verification gaps before deployment.

LLM-as-judge (using one AI to evaluate another) inherits the same biases, hallucination patterns, and blind spots as the systems being evaluated, creating what Anthropic calls the 'ouroboros' problem; practitioners who succeed split compliance checks (schema validation, authorization) into deterministic layers while limiting LLM judgment to genuinely contextual decisions.