Evaluating Efficiency Gains in SOX Testing through Agentic Automation

Executive Summary

Agentic Artificial Intelligence (AI) presents a meaningful opportunity to modernize how SOX testing is executed. Market narratives with AI often lean on broad or ungrounded efficiency claims. To move beyond that noise, this white paper introduces a transparent and assumption-driven model to help audit leaders estimate the potential impact of automation taking into consideration their unique environment.

Using a representative mid-cap SOX program, our analysis shows that agentic automation can materially reduce the effort required for evidence-intensive, repeatable testing activities. Under reasonable assumptions, the model indicates that between 68% and 80% of testing hours may be redeployed, representing a directional annual value of approximately $0.7M in the modeled scenario. These results are not presented as universal benchmarks; rather, they reflect how automation behaves when applied to a clearly defined control landscape and testing assumptions.

While SOX has been in existence more than 20 years, the unique business processes and maturity of each company still leads to a lot of variability in how SOX programs are structured. To reflect this variability, audit leaders can adjust the main inputs to the model and estimate a more precise likely outcome, which can serve as a basis for their team's objectives instead of relying on generalized expectations.

This white paper is intended as a credible, practical starting point for audit leaders evaluating the next stage of their automation journey. It outlines the model and presents scenario-based results, such that leaders can augment the inputs and project efficiency gains in their organization. We hope the framework will be of special relevance to internal audit leaders when setting efficiency objectives for their Internal Audit and SOX activities.