Key takeaway
Artificial intelligence is moving deeper into audit workflows, from planning and document review to risk identification and reporting. The shift is not eliminating professional judgment; it is changing where auditors spend their time and increasing the importance of governance over AI-enabled work.
Gartner’s 2026 research found AI use across 93% of surveyed audit functions, while 60% still lacked a formal AI strategy. This gap captures the current stage of the profession: adoption is moving faster than structured governance.
Why AI is a different kind of change for audit
In a wider Gartner poll of 743 audit professionals, generative AI was most common in discrete productivity tasks. Sixty percent used it to draft audit issues, ratings or reports, 41% to review drafts, and 30% in audit testing. Only 12% reported use in quality-assurance reviews. High adoption therefore does not yet mean end-to-end automation.
Why the auditor remains central
Audit is not simply data processing. Auditors assess the reliability of evidence, determine how to respond to risk and remain accountable for professional conclusions. IAASB’s technology work and ICAEW guidance both emphasize quality management, oversight and professional responsibility as AI becomes embedded in assurance.
EY provides one example of the emerging operating model. According to ICAEW, its multi-agent capabilities were embedded in the global EY Canvas platform in 2026 across 130,000 assurance professionals. Human auditors remain responsible for reviewing AI-generated work and exercising professional scepticism.
What changes in the audit-firm business model
The Financial Times reported in September 2026 that audit-fee inflation had slowed to below 2%, compared with a historical norm of roughly 4–5%. AI-related efficiency is one factor among several. The trend matters because clients increasingly expect productivity gains from automation to influence price as well as delivery speed.
What this means for Georgia
Georgia has a regulated audit market. SARAS’s current registries list 238 audit firms and 435 auditors. Public official data do not currently show how many Georgian audit firms use AI or how the technology affects local audit pricing, so international adoption rates should not be treated as Georgian market statistics.
For Georgian firms, the most realistic first stage is likely to be the automation of supporting work: document classification, information extraction, workpaper summarisation, risk-signal detection and knowledge search. More consequential uses in testing and evidence assessment require stronger controls over data, validation, documentation and accountability.
BTU researchers’ assessment
BTU researchers assess that AI’s most important near-term effect on Georgian audit may be a change in the composition of working time rather than the replacement of auditors. Routine processing can decline while professional judgment, data-quality control, technology-risk assessment and review become more important.
Conclusion
The new era of audit is not audit without auditors. It is audit in which machines process more material while humans carry greater responsibility for interpretation, scepticism and trust. For Georgia, the strategic challenge is not simply to adopt AI quickly, but to integrate it without weakening the quality and credibility that audit is designed to provide.