AI in audit: The gap between knowing and doing

2026-06-16 · Source: Thomson Reuters Institute · Field: Finance & Economics — Artificial Intelligence & Machine Learning, Banking & Financial Services, Compliance & Risk Management · Depth: Intermediate, medium

Summary

A Thomson Reuters Institute analysis highlights a significant gap between AI deployment and effective governance within audit firms, noting that while many are piloting AI, few are truly prepared for the organizational changes required for scaling. An IDC survey of 1,005 audit and accounting professionals revealed two-thirds have AI in strategy or pilots, but only 7% describe themselves as "extremely prepared." The article emphasizes that the crucial skill for auditors is not just using AI, but knowing when to distrust its outputs, particularly in risk assessment, where professional skepticism is paramount. It also stresses the need for robust AI governance, focusing on traceability and explainability, and precisely defining the "human in the loop." While AI offers genuine value in tasks like document extraction and summarization, its true impact is incremental and depends more on clear organizational decisions than rapid technological adoption.

Key takeaway

For Directors of AI/ML or Audit Partners evaluating AI integration, recognize that successful adoption hinges on robust governance and cultivating auditor skepticism. Prioritize defining "human in the loop" workflows and implementing traceability and explainability for all AI outputs. Your focus should shift from mere pilot programs to embedding organizational changes that support critical human judgment, especially in risk assessment, to avoid accumulating quiet audit risk.

Key insights

Audit firms must prioritize AI governance and cultivate auditor skepticism over mere deployment to realize true value.

Principles

• AI deployment differs fundamentally from effective governance.
• Auditor skill is knowing when to distrust AI outputs.
• Risk assessment requires professional skepticism, not just pattern recognition.

In practice

• Implement AI for document extraction and summarization tasks.
• Define specific "human in the loop" points in AI workflows.
• Train auditors to pressure-test AI outputs and identify omissions.

Topics

• AI in Audit
• Audit Governance
• Professional Skepticism
• Risk Assessment
• AI Explainability
• Human-in-the-Loop

Best for: CTO, VP of Engineering/Data, Executive, Director of AI/ML, Consultant, Legal Professional

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Editorial summary, takeaway, and curation by AIssential. Original article published by Thomson Reuters Institute.