Audit Sampling Remains Key Despite AI Adoption

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Introduction

Artificial Intelligence has become one of the most transformative forces in professional services, including accounting and auditing. The modern auditor is no longer limited to spreadsheets, manual verification, or basic computer tools. Instead, AI powered solutions can quickly test entire data sets, detect unusual patterns, highlight anomalies, and interpret complex data relationships.

However, the rise of AI raises important questions:

• Does audit sampling still matter?
• If AI can test everything, why test only a sample?
• Will sampling become obsolete?
• Can AI replace auditor judgment?
• Can testing full populations detect fraud better than sampling?

To effectively evaluate these questions, this article examines various areas.

1. The Foundation of Audit Sampling

Audit sampling involves selecting less than 100% of a population and making conclusions about the whole. Standards like ISA 530 and SA 530 formally define and require sampling when full-population testing is impractical or unnecessary.

Reasons for Sampling

Even with AI, full-population testing is time-consuming and economically inefficient, making audit sampling essential.

Audits focus on material items, and sampling ensures attention is directed toward significant transactions rather than immaterial data.

AI supports analysis but cannot replace auditor judgment in risk assessment, evidence evaluation, and conclusion formation, which sampling facilitates.