Applying AI to internal control testing is powerful and can advance audit programs in meaningful ways. The key to success involves setting up purposeful delivery models built to tackle discrete objectives while maintaining professional judgment.
Internal audit and SOX are getting the same pressure as the rest of finance to implement artificial intelligence. In theory, it makes sense because internal audit work has elements of high-volume data analysis, which is time-sensitive. In reality, applying AI within internal audit is complex, given inconsistent evidence formats and the need for outputs to hold up under review. That combination makes internal audit both a difficult and high-impact use case for AI and automation.
