AI can write the code faster than any team can fully account for it. Each accepted change looks fine on its own, but architectural drift, and the understanding debt underneath it, compounds quietly until it doesn't.

AI can generate COBOL to Java migrations fast, but production sign-off still requires a human reviewer's certification. This piece explains why unbounded review breaks down at scale, and how a bounded, source-traced evidence trail makes sign-off possible again.

Prompt-derived AI can translate legacy code fast, but it can't replace the multi-year parallel run enterprises rely on for proof. Here's what can.

Coverage measures conversion, not completeness, and buyers find the gap once testing stalls. What disposition-based verification requires before cutover.

LLMs can generate legacy code migrations fast, but they can't demonstrate what they changed or account for what broke along the way. Here's why sampling and coverage reports fall short, and what deterministic verification actually requires.