Recent market volatility triggered by AI related narratives has once again reminded us how quickly sentiment can shift. Speculative scenarios about AI displacing large segments of white collar work have influenced investor confidence and corporate valuation discussions. Whether or not these projections materialize in full, the signal is clear: AI is no longer a theoretical technology risk. It is a strategic, operational, and governance issue that boards and management must address proactively.
I view this shift not as a source of "anxiety," but as a catalyst for a structured, disciplined response. AI has graduated from a theoretical IT risk to a fundamental pillar of strategic, operational, and governance oversight.
1. How We Should Respond
First, we must avoid emotional reactions. Internal Audit’s role is not to amplify fear but to bring clarity.
Our response should focus on three areas:
Risk Identification
We need to assess where AI may materially impact our business model. This includes workforce displacement risk, data governance exposure, model risk, regulatory compliance, and reputational risk.
Control Readiness
We must evaluate whether governance frameworks around AI usage are clearly defined. This includes approval protocols, data access controls, documentation standards, and monitoring mechanisms.
Board Communication
Narrative risk is real. Markets respond not only to performance but to perception. Internal Audit should support management and the Board by ensuring risk disclosures are balanced, transparent, and aligned with actual mitigation strategies.
AI disruption is a strategic risk, not just a technology risk. That distinction is critical.
2. Our Plan as Internal Audit
To move forward responsibly, Internal Audit should adopt a structured plan:
Short Term Actions
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Conduct an AI usage inventory across business units
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Review governance over third party AI tools
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Assess data privacy and cybersecurity exposure linked to AI integration
Medium Term Actions
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Integrate AI risk into Enterprise Risk Management updates
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Develop audit programs for algorithm governance and model validation
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Provide advisory input on responsible AI deployment frameworks
Long Term Positioning
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Build internal capability in data analytics and AI oversight
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Partner with IT and Risk functions to define sustainable monitoring processes
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Align AI risk assessment with COSO ERM principles and the Three Lines Model
The objective is not to resist AI adoption, but to ensure it is implemented with discipline and accountability.
3. Personal Development: Skills Over Knowledge
The broader question is how this shift affects us as professionals.
Knowledge becomes outdated quickly. Technical details, tools, and platforms change every year. What remains durable are core skills:
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Critical thinking
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Structured problem solving
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Risk assessment judgment
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Communication clarity
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Ethical reasoning
AI can process information faster than any auditor. It cannot replace professional skepticism, contextual understanding, or moral responsibility. The differentiator going forward will not be who knows the most, but who adapts the fastest and thinks the clearest.
Conclusion
I began writing this blog just one year ago. Looking back, some earlier articles already feel dated, particularly those discussing search techniques or basic AI applications. That realization itself reinforces an important lesson: Internal Audit is a lifelong learning profession.
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