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Artificial Intelligence (AI) is no longer a futuristic concept, it’s a present-day force reshaping industries, redefining roles, and challenging traditional governance models. In a previous blog I mentioned that potentially AI gets overrated, however it is here to stay and will further evolve.
Yet, as adoption accelerates, the gap between innovation and control is widening. This blog explores where we stand today and what’s needed to ensure AI remains a force for good.
The AI Revolution: meeting it promises?
AI’s transformative power lies in its versatility, from automating audits to enhancing decision-making in finance and healthcare. However, this power comes with risks: unintended bias, lack of transparency, and regulatory non-compliance are no longer theoretical concerns—they are operational realities
A recent KPMG global report (trust, attitudes and use of artificial intelligence, 2025), reveals that nearly half of employees admit to using AI in ways that contravene company policies, including uploading sensitive data to public tools.
Furthermore, it showed that:
• 75%+ of businesses use AI in some form
• <10% have mature governance frameworks in place
• Incidents rise: AI-related incidents have increased twentyfold since 2013
• Cultural resistance and technical complexity remain major hurdles.
This underscores a critical truth: AI governance is not meeting the AI adoption.
The Regulatory Landscape: from EU to Switzerland
The EU AI Act and ISO/IEC 42001:2023 are setting the tone for AI governance. Switzerland is also advancing its own frameworks, emphasizing ethics, accountability, and cross-sector governance
Yet, compliance remains inconsistent. Most organizations lack robust AI governance frameworks, leaving them exposed to reputational and legal risks.
Where we need to go: a call to action
To bridge the gap between innovation and control, organizations must:
1. Adopt AI Management Systems (AIMS) that integrate regulatory, ethical, and technical dimensions
2. Invest in training to build internal capabilities for responsible AI use
3. Embed assurance into every stage of AI deployment—from design to decommissioning
4. Foster transparency by making AI use visible and accountable across the enterprise.
AI assurance: building trust through controls
AI assurance is emerging as the cornerstone of responsible AI. In a recent inaugural speech professor Renkema positioned AI Assurance as a key area of IT auditing with multidisciplinary teams working together.
It involves evaluating, monitoring, and communicating the reliability and compliance of AI systems. Key components include:
• Explainability: Ensuring models are interpretable and decisions traceable
• Access Controls: Managing data exposure, especially with third-party AI providers
• Risk-Based Controls: Tailoring governance based on model type, use case, and regulatory exposure
• Ethical Guardrails: Embedding fairness, transparency, and accountability into AI design-
Final thoughts
AI is not just an emerging tool—it’s a transformation. But without strong controls, its promise may be overshadowed by its pitfalls. As leaders, we must ensure that our pursuit of innovation is matched by our commitment to responsibility. It should be part of the broader Governance frameworks in place at organizations.
Let’s shape a future where AI is not only powerful but principled.