How we used agentic AI to crack automated SOX testing at scale… in 90 days

The article describes how an Internal Audit team used agentic AI and advanced OCR to automate SOX testing for 175 controls—over 40% of their SOX scope—in just three months, cutting average testing time per control by about 60% while improving quality. Instead of relying on traditional RPA, they implemented a system of specialized AI agents for evidence extraction, testing, review, and documentation, all orchestrated through the Midship platform, which was chosen after rigorous vendor pilots for its flexibility, output quality, and willingness to co-innovate. The AI now ingests messy, unstructured evidence, applies test criteria, generates audit-ready workpapers in the external auditor’s template (with tickmarks, annotations, and full audit trails), and routes everything through a robust human-in-the-loop review process to manage risks like hallucinations and preserve professional judgment.

From this experience, the team concludes that configuration and data quality are crucial, deep contextual prompts dramatically improve results, and output design is as important as raw model accuracy for auditor trust. They emphasize that automation delivers higher quality, immediate results, and scalable infrastructure for other testing programs, enabling auditors to shift from repetitive evidence work to more strategic, risk-focused activities. Looking ahead, they plan to push SOX automation toward 90%+ coverage, explore zero-touch evidence collection, self-service testing for process owners, and continuous monitoring, while stressing that success hinges on an inspiring vision, an innovation mindset, and a carefully selected technology partner willing to iterate with them.