Last week, at Çağ University in Mersin, we had the opportunity to present at XI. Academic Studies Congress, held under the theme “The Psychology of the Age of Artificial Intelligence: Human, Society, Technology, and Institutional Structures.”
From an auditing perspective, the theme could not have been more relevant.
Our presentation, “Leveraging AI to Enhance the Value of Food Safety Audits,” was built around a simple but increasingly important observation: AI does not replace auditor judgment, but it changes how that judgment is formed.
As AI becomes embedded into audit processes, understanding its influence on auditor behavior is now just as important as understanding how technology works.
A clear message emerging from academic discussions is that AI is no longer optional. It has become part of the organizational infrastructure.
Many auditors now work through AI:
- using automated analytics,
- relying on AI-supported risk prioritization,
- reviewing summaries and flags produced by intelligent systems.
Recent Optro (formerly AuditBoard) research shows that most organizations already use AI across core processes, often faster than governance frameworks can keep pace with. Auditing is no exception. This matters because AI does more than speed things up. It shapes attention. It influences what appears risky, what looks normal, and what may never be questioned at all.
One of the most striking findings from this AI oversight research is that the largest concentration of AI risk is not technical. It is human.
Incidents linked to AI are rarely driven by bad intent. Instead, they tend to rise from:
- time pressure and efficiency expectations,
- uncritical reliance on AI-generated outputs,
- limited understanding of model limitations and bias,
- unclear responsibility when AI is embedded into daily work.
Auditors are particularly exposed to these dynamics.
When AI highlights certain risks or generates confident-looking conclusions, it can subtly anchor judgment. Challenging those outputs requires conscious effort, especially under tight deadlines.
This is not about auditor capability. It is about psychology.
Traditional governance models are built around policies, training sessions, and periodic reviews. These controls assume risk can be managed through documentation and after-the-fact checks.
AI risk does not behave that way.
Most AI-related risk materializes at the point of use, when individuals make rapid decisions within operational workflows. Research consistently shows a mismatch between how AI is used by employees and how it is governed by organizations.
For auditing, this raises an uncomfortable but necessary question: “Are our controls designed for how auditors actually work today or for how we think they work?”
At the congress, much of the discussion focused on how humans adapt to intelligent systems. For auditors, this adaptation requires a reframing of professional judgment.
The question is no longer “Can we trust AI?”
It is “How does AI influence what we trust?”
In practice, the value auditors bring increasingly lies in:
- interpreting AI outputs in context,
- applying professional skepticism where automation creates confidence,
- identifying blind spots introduced by standardization and scale,
- exercising ethical judgment where AI has no answer.
In food safety audits, for example, AI can detect trends across data, but it cannot assess safety culture, behavioral incentives, or informal practices on the ground. These remain fundamentally human responsibilities.
The congress subtheme “human, society, technology, and institutional structures” offered a useful lens for where auditing must go next.
Human-centered AI governance in auditing means:
- accepting AI use as the norm, not the exception,
- embedding judgment checkpoints where AI influence is strongest,
- clarifying accountability for AI-supported audit decisions,
- recognizing behavioral risk as something auditors should explicitly consider.
Recent research from Optro makes it clear that organizations with strong AI adoption but weak human-layer governance are already experiencing more incidents, not fewer. Auditors have a role to play in closing that gap.
Final Thoughts
Auditors are not simply users of AI. They are critical safeguards of trust in AI-enabled systems. AI is changing not only what we audit, but how we think while auditing.
As AI adoption accelerates, the profession’s key challenge is behavioral, not technical.
Reference: “The AI Oversight Gap – Adoption is scaling, Governance controls aren’t”. by Optro
