The new trust equation audit leaders need to solve

‍Register for Caseware Speaker Series: What Trust Looks Like in AI-Enabled Assurance on 13 August to explore how AI is changing evidence, controls, reliance and professional judgement →

AI may improve the speed of assurance, but speed has never been the same as trust. 

For audit leaders, the real test is not whether AI can summarise information, detect anomalies or streamline workflows. It is whether the evidence, controls and judgements shaped by AI can withstand scrutiny from boards, audit committees, regulators and management. 

That is the new trust equation in assurance: as AI accelerates parts of the process, confidence may become harder to explain, evidence and defend. 

AI is beginning to appear across finance, reporting, risk and assurance workflows, with adoption varying by organisation and use case. It can help teams analyse more data, identify anomalies and support documentation. But for decision-makers, the question is not only whether AI can improve productivity. It is whether audit leaders can maintain confidence in the outputs, processes and conclusions AI may influence. ‍

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Trust must still be earned

Audit has always depended on trust, but not blind trust. Assurance relies on evidence, controls, professional scepticism and the ability to explain how conclusions were reached. 

AI can strengthen assurance when used well, but it also changes how trust is established, tested and defended. 

When AI influences information, audit teams need to know whether that information is complete, accurate and appropriate. When AI is embedded in a reporting or control workflow, leaders need to consider whether existing controls still hold. When AI-generated outputs support audit work, the question becomes whether those outputs can be relied on, challenged or independently validated. 

The risk is not only that AI produces an incorrect output. It is that organisations may not have enough visibility into how that output was produced, used or tested. 

For audit leaders, that creates an accountability gap. They are being asked to support innovation and efficiency while maintaining confidence in the quality, transparency and defensibility of assurance outcomes. 

Professional judgement matters more, not less

One misconception about AI in audit is that greater automation reduces the role of human judgement. In practice, the opposite may be true. 

As AI becomes more embedded in assurance workflows, audit professionals may need sharper judgement about when to rely, when to investigate and when to challenge. 

Professional scepticism does not disappear in an AI-supported environment. It needs to evolve. 

Auditors may need to ask different questions. What data informed the output? Has the workflow been tested? Are there limitations, biases or blind spots? Has the result been corroborated? Can the conclusion be explained to someone outside the process? 

For audit leaders, these questions affect methodology, team capability, review processes and stakeholder confidence. In an AI-enabled environment, defensible confidence will not come from faster outputs alone. It will come from knowing when to trust, when to challenge and how to document the judgement applied. 

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The control environment is changing 

AI also raises important questions about controls. In some organisations, AI use may be visible and formally governed. In others, it may be embedded in tools, spreadsheets, workflows or third-party platforms in ways that are less obvious. 

That creates a practical risk question: do current controls still provide the level of confidence expected when AI is part of the process? 

Internal and external audit may approach the issue from different starting points, but both need to understand how AI affects governance, controls, evidence, reliance and assurance outcomes. 

The trust equation cannot be solved in silos. As AI becomes more embedded in the assurance landscape, audit leaders need a more connected conversation about risk, reliance and accountability. 

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The questions audit leaders should be asking now 

For audit decision-makers, the priority is not to have every answer. It is to know which questions should be on the agenda. 

Where is AI already influencing reporting, finance or assurance workflows? What evidence is being produced, selected, summarised or interpreted by AI? Are existing controls still appropriate where AI is involved? When can teams rely on AI-supported outputs, and when is further validation required? How should professional judgement and scepticism be documented when AI has played a role? 

These questions go to the heart of defensible confidence. They also show why AI-enabled assurance is not simply a technology issue. It is a leadership issue.

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Join the Caseware Speaker Series 

These issues will be explored in the next Caseware Speaker Series webinar, What Trust Looks Like in AI-Enabled Assurance. 

Date: Thursday, 13 August 2026

Time: 11:00 AM to 12:00 PM AEST / 9:00 AM to 10:00 AM SGT

The session will be a moderated thought-leadership conversation with audit and assurance subject matter experts, exploring how AI is changing audit evidence, controls, reliance and professional judgement. 

Caseware will moderate the conversation and will be joined by Chin Ding Khoo, Director at LNP Audit and Assurance, who will bring an external audit perspective. Caseware is also curating additional subject matter expertise to help bring a broader assurance view, including the role of internal audit in governance, risk management and controls. 

For audit leaders, the value of the conversation lies in its practicality: how to make confidence defensible when AI is part of the process.

Register now to join the conversation →