An Analytical Study on Artificial Intelligence as a Turning Point for the Transformation of the Auditing Industry

With the digital economy growing at an unprecedented rate, corporate transaction data has become exponentially large, presenting several challenges to the auditing profession - including the exponential increase in the volume of data, more complex transaction patterns, and stricter regulatory requirements - making traditional sampling-based auditing techniques less effective. This paper examines how artificial intelligence (AI) can be a game changer in the evolution of the auditing industry. The research employs…

By the Truvace deskOctober 21, 20262 min read1 sourcePeer-reviewedTRV-2026-2131· cite & verify

An Analytical Study on Artificial Intelligence as a Turning Point for the Transformation of the Auditing Industry — A Review of Bexar Metropolitan Water District's Projected Financial Data for Fiscal Years 2009 and 2010 by Texas. Office of the State Auditor; Keel, John. Public domain

In brief

A paper published in 2026 reviews how artificial intelligence has been used in auditing since 2015. It starts from the pressure that rapidly growing corporate transaction data, more complex transaction patterns, and stricter regulation place on traditional sampling-based audit techniques.

The author concludes that AI is a key driver of change in the profession, though the change is gradual and the sector is still in transition. The work shows why audit firms and regulators may need to rethink governance and talent, but as a literature review and comparative analysis it offers conclusions about trends rather than measured results at specific firms.

Main points

The study uses a literature review and comparative analysis covering AI use in auditing since 2015.
Its analysis is organized into four dimensions: process transformation, motivations for adopting AI, practical benefits, and obstacles.
It describes the sector as moving from a mainly human-oriented approach toward a human-machine partnership.
Issues named for the transition include overdependence on AI, lack of transparency in algorithmic decisions, accountability, and workforce change.

The gain

AI is changing the scope, effectiveness, and decision-making references of the auditing profession, helping it cope with growing data volumes and complex transactions that weaken sampling-based methods.

The rundown

The paper frames the problem as one of scale and complexity: corporate transaction data has become exponentially large, which the author says makes traditional sampling-based auditing techniques less effective.

The author organizes the review into four dimensions, covering how audit processes change, why firms adopt AI, what practical benefits result, and what obstacles arise during adoption.

From these findings the author offers recommendations on AI governance practices within firms, the creation of strong regulatory frameworks, and the development of competent auditing talent.