How is AI used in external audit?

Question

Accepted Answer

AI is most useful in external audit where there is a large amount of evidence to read, structure and test, but the final conclusion still needs professional judgement. Current use cases include risk identification, process documentation, control assessment, journal analysis, evidence extraction, sample matching, disclosure checks and working-paper preparation.

The best starting points are usually repeatable workflows with clear inputs, clear outputs and a meaningful amount of manual effort. ITGC assessments are a good example: teams collect similar categories of evidence, assess controls against a methodology, document conclusions and prepare findings. Process understanding is another: walkthrough recordings, client documents and prior-year notes can be turned into process narratives, maps, risks and controls for auditor review.

AI can also help firms work more consistently. A well-configured workflow gives every team the same starting structure and evidence requirements, rather than relying on individual spreadsheet habits or the experience of one specialist. That does not remove judgement. It gives auditors better-prepared work on which to apply it.

The strongest implementations are deliberately narrow at first. They improve a real audit workflow, prove quality and value in live engagements, then expand as the firm builds confidence.

Platformed automates defined workflows including ITGC assessment, process understanding, risk assessment and control assurance. Firms can start with one workflow, configure it to their methodology and keep the auditor in control of every conclusion.