AI Value: Chief Audit Executives Struggle to Quantify Its Worth

Most chief audit executives lack clarity on the measurable value of artificial intelligence within their organizations.

Key Findings from the Gartner Survey

Despite widespread integration of AI tools into audit workflows, many professionals remain uncertain about quantifying their impact. A Gartner survey revealed that 93% of audit leaders and practitioners utilize AI in some capacity, yet only 15% have established formal deployment processes for routine audit applications. This disparity highlights a critical gap between adoption and strategic implementation.

AI Adoption and Strategic Implementation

Among 142 chief audit executives surveyed in May, 54% had not initiated efforts to evaluate AI’s contribution to audit outcomes. While 7% linked AI to cost-saving metrics such as reduced external expenditures or avoided hiring, these figures underscore the challenge of demonstrating ROI in a domain where efficiency gains are often intangible. Only 38% of 161 surveyed executives reported having an AI strategy, with most integrating it into broader departmental frameworks rather than developing dedicated audit-specific plans. Another 39% are in the process of creating such strategies, indicating a gradual shift toward structured approaches.

AI’s Role in Audit Functions

AI’s role in audit functions spans multiple stages, with drafting and planning dominating usage. Over 60% of 743 respondents employ AI for engagement preplanning tasks, including risk research and brainstorming, as well as for drafting audit issues, ratings, or reports. Smaller subsets use AI to design risk and control matrices—tools that map risks to mitigating controls—and to review final documents before publication. Audit testing, which involves verifying the effectiveness of controls and directly influencing report conclusions, remains underutilized, with only 30% of respondents applying AI in this area. This lower adoption rate is significant, as errors in testing—such as model hallucinations or omissions—carry greater financial and reputational risks than inaccuracies in preliminary drafts.

Challenges in AI Testing and Validation

Organizations leveraging AI for testing face heightened demands for rigorous validation and documentation. Auditors must ensure that model outputs meet regulatory scrutiny, requiring transparent records of how conclusions are derived. Gartner emphasizes that structured use cases for high-value workflows represent the most effective solution, with 12% of respondents currently piloting such approaches.

Barriers to AI Integration

Barriers to AI integration primarily stem from human and procedural challenges. Unclear expectations for tool usage emerged as the top obstacle, cited by 48% of respondents, followed by inconsistent application methods among auditors. Technical hurdles, including model selection and data access, ranked lower in priority. The lack of standardized practices leads to fragmented outcomes, complicating comparisons between audit engagements.

Gartner’s Recommendations for Effective AI Integration

Gartner’s analysis underscores that while AI adoption is prevalent, its full potential remains unrealized without strategic alignment. The organization advocates for formalized processes, clear governance, and targeted training to address gaps in understanding and implementation. As audit teams navigate this evolving landscape, the emphasis on measurable value and accountability will shape the next phase of AI integration.

Gartner’s analysis underscores that while AI adoption is prevalent, its full potential remains unrealized without strategic alignment.