Artificial intelligence is steadily reshaping how audits get done, and this shift raises an obvious question: how does it actually stack up against the way audits have always been performed? This paper works through that comparison, pulling together existing research to weigh AI-assisted auditing against traditional, sample-based methods across the areas that matter most — sampling coverage, fraud detection, speed and cost, accuracy, and the judgment calls only a human auditor can make. What emerges is a fairly clear picture: AI is genuinely good at chewing through huge datasets, catching irregularities a sample-based review might miss, and doing it consistently every time. But it stops short of replacing an auditor's instincts — the skepticism, ethical reasoning, and contextual read that come from experience rather than computation. The paper argues that the strongest audit model isn't AI or traditional methods alone, but the two working together, with AI handling the heavy analytical lifting and auditors retaining final judgment. This has practical relevance for accounting professionals, researchers, and institutions trying to make sense of where auditing is headed.

Keywords: Artificial Intelligence, Traditional Auditing, AI-Assisted Auditing, Audit Quality, Fraud Detection, Professional Judgment