Across Saudi Arabia, internal audit functions are adopting AI faster than almost any other assurance discipline. In a 2025 Wolters Kluwer survey, 39% of auditors said they already use AI and another 41% planned to by 2026 — roughly four in five within the year.
So the question in most Saudi audit shops is no longer whether to use AI. It is how to use it without breaking the things that make audit defensible.
The teams pulling ahead are not the ones automating the most. They are the ones being deliberate about where AI helps and where it must never go. Here is what they do differently.
1. They let AI draft, not decide
The slowest, most repetitive part of an engagement is writing — reports, executive summaries, finding narratives. That is where AI earns its keep. Deloitte estimates AI can cut control-testing time by up to 40%, time that goes straight back to judgement.
But the judgement itself stays with the auditor. The rating, the opinion, the sign-off: human. Smart teams draw that line on day one.
AI handles the heavy lifting and provides data-driven insights; humans provide oversight, contextual understanding, and final decision-making.
The failure mode is the opposite — letting a model "summarise and conclude" and trusting the output because it reads well. A confident wrong answer in an audit report is worse than a slow right one.
2. They keep the evidence inside their walls
For a SAMA-regulated bank, where audit evidence sits is not a detail. Generic AI tools want your working papers, observations, and supporting files in someone else's cloud. For a regulated function in the Kingdom, that is often a non-starter.
The teams doing this well run AI inside their own perimeter — on-premise or private cloud — so working papers never leave their governance. Data residency is a design decision, not a setting you hope is configured correctly.
3. They attribute every output to a human
When an AI draft is published with no owner, accountability quietly evaporates. The strong teams treat every AI output as an editable draft attributed to a named person, and they keep the preparer → reviewer → approver chain unbroken. The standard's spine stays intact; the AI just makes the writing faster.
4. They aim AI at fewer, higher-impact places
Gartner found that spreading analytics across every audit dilutes impact and strains the limited capacity of expert resources. The disciplined approach is to concentrate AI where the return is real.
In practice that means controls testing and reporting first — the work that is both repetitive and high-volume — rather than sprinkling "AI" across the whole plan to look modern.
5. They audit the AI itself
Internal audit now has a second AI job: governing the AI the rest of the organisation is deploying. That means maintaining an inventory of AI systems, validating their accuracy, controlling access to outputs, and insisting on audit trails and human-in-the-loop overrides.
The function that assures everyone else's AI has to model good governance in its own tools first.
The Saudi context raises the bar, not lowers it
Vision 2030 is pushing digital transformation through Saudi institutions at speed, and boards expect their audit functions to keep pace. At the same time, SAMA's expectations around control, data handling, and independence are not relaxing. Add bilingual reporting — a committee pack that reads natively in Arabic and English — and the bar for "AI in audit" in the Kingdom is genuinely higher than the global average.
That is not a reason to wait. It is a reason to be deliberate.
What good looks like
The pattern is consistent across the teams getting value: an assistant that drafts across the audit lifecycle, runs inside your perimeter, attributes its work to a person, and never decides.
That is exactly the thinking behind VistaAssist, the AI layer in ControlVista — it drafts reports, summaries, procedures and finding narratives across every module, on your infrastructure, and hands the judgement back to you.
Want to see it on your own audit lifecycle? Request a demo.