A webinar poll of 743 audit professionals, conducted in 2026 by the business and technology insights company, revealed that 93 per cent of audit leaders and their teams reported some level of AI use.
However, only 38 per cent of them have implemented an AI strategy.
The poll found that generative AI use cases in internal audit focus on isolated tasks such as engagement pre-planning, including research and brainstorming risks (60 per cent), and drafting audit issues, ratings, or reports (60 per cent).
Other use cases included engagement planning (44 per cent) and reviewing drafts (41 per cent). Just over a third reported using GenAI for departmental risk assessment and audit planning, as well as preparing stakeholder communications such as presentations.
Fewer than a third reported using AI for audit testing (30 per cent), and only a small fraction applied AI to broader departmental activities, such as quality assurance reviews (12 per cent).
Commenting on the findings, James Bourke, director analyst in Gartner’s risk and audit practice, said audit’s current use of generative AI focuses more on moderate productivity gains than on strategic audit use cases.
“Therefore, although AI adoption rates are high in audit functions, it is not generally resulting in transformation of audit processes or delivery of better strategic insights,” he said.
Speaking to Accounting Times about the barriers audit leaders may face when formulating a strategy, Bourke said the reasons vary by organisation.
“One thing we hear often in our conversations with audit clients is that audit leaders are feeling pressure from senior stakeholders to do more with AI and to demonstrate results,” he told the brand.
“Therefore, using AI is a foregone conclusion, and many departments want to go straight to use cases without thinking through their overall goals and how AI fits. Strategy can be more of an afterthought rather than a starting point. Audit teams will need to move beyond these patterns to unlock more strategic impact and deeper insights.”
Gartner analysts noted that while AI adoption is high in audit, many teams are struggling to clearly define and communicate the value it delivers.
They explained that this entails taking a deliberate approach that links AI initiatives to audit quality, consistency, and risk insights. They said audit leaders should not use their adoption levels or productivity gains as the sole measure of success.
“Focusing narrowly on productivity or adoption metrics risks underrepresenting and even missing out on AI’s potentially broader impact on audit outcomes and decision-making,” Bourke said.
“The risk here is not that these metrics don't capture any of the value AI is delivering, but rather that they are too narrow. Effective use of AI can not only improve efficiency, but also help audit provide more assurance coverage and elevate its communication.
“A robust approach to assessing AI's impact requires a holistic value framework, going beyond usage rates, prompt volumes, or hours saved.”
He said more mature audit teams are using AI for audit testing, enhancing their ability to identify risks to the organisation by reviewing large volumes of unstructured data and moving toward a more continuous and real-time approach. Audit teams could uncover more insights from unstructured data and move from testing samples to full populations.
As a result, Bourke said, they could deliver more timely insights, and ultimately help the organisation manage risk more effectively.
He urged audit leaders to develop a strategy to use AI to its full potential.
“If you haven't thought carefully yet about the ‘why’ behind your use of AI, step back and consider how AI opportunities relate to your departmental objectives,” Bourke said.
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