White paper
Evaluating efficiency gains in SOX testing through agentic automation
A transparent and assumption-driven model to help audit leaders estimate the potential impact of agentic automation in their unique environment.
Dec 9, 2025
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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.
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 a practical starting point for audit leaders evaluating the next stage of their automation journey. It outlines the model and presents scenario-based results, so that leaders can adjust the inputs and project efficiency gains in their organization. We hope the framework is useful to internal audit leaders when setting efficiency objectives for their Internal Audit and SOX activities.
What the paper covers
What is in the full report
The full report is a detailed analysis of SOX testing automation, with its assumptions and evaluations, to help you set your automation target.
An efficiency framework
A model for estimating efficiency gains in SOX testing, with each assumption shown.
Current automation capabilities
What automation can do in SOX testing today.
Variables and limitations
The main variables that move the estimate, and where the model stops.
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